{"topic":"Econometrics & Forecasting","items":[{"title":"The Impossible Trinity of Time-Series Validation: A Conservation Law among Training Sufficiency, Test Coverage, and Temporal Causality","url":"/papers/arxiv/2609.29530/","summary":"Proves that training sufficiency, test coverage, and temporal causality cannot be maximized simultaneously in time-series validation, pricing each constraint explicitly.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":4,"scale":"fanfare"},{"title":"Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting","url":"/papers/arxiv/2609.25617/","summary":"A hierarchical multi-task framework jointly predicts price movement, volatility and volume using liquidity-aware signals, outperforming neural and tree-based baselines on Chinese equity indices.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"fanfare"},{"title":"Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets","url":"/papers/arxiv/2609.23223/","summary":"Jointly reconciling hourly price and spread forecasts improves intraday electricity price prediction accuracy by up to 19.7% and battery-arbitrage profits by up to 10.4%.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"fanfare"},{"title":"Nonlinear Drivers of Macroeconomic Tail Risk: A Threshold Stochastic Volatility-in-Mean VAR with Regime-Dependent Leverage","url":"/papers/arxiv/2609.26994/","summary":"A regime-switching volatility-in-mean VAR reveals that the drivers of growth and inflation tails differ from median dynamics, with macroeconomic uncertainty playing a larger role in downside risk.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"fanfare"},{"title":"A Stochastic Nested Fixed Point Algorithm for Large-Scale BLP Estimation","url":"/papers/arxiv/2609.23998/","summary":"A stochastic nested fixed-point estimator reduces memory and computational cost for random-coefficients logit demand models, enabling estimation on 100 million markets in hours.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"fanfare"},{"title":"The \"Rough\" HAR Model","url":"/papers/arxiv/2609.21587/","summary":"Augmenting HAR with a negative moving-average component approximates rough dynamics, outperforming classical models out-of-sample and matching continuous-time rough model accuracy.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"fanfare"},{"title":"Network Realized GARCH--Itô Models: Volatility Spillovers with High-Frequency Identification","url":"/papers/arxiv/2609.29515/","summary":"Introduces a network realized GARCH-Itô model that identifies dynamic volatility transmission among assets using high-frequency data, outperforming recursive forecasts on sector ETFs.","featured":"2026-09-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"fanfare"},{"title":"Industry Information and Equity Return Predictability","url":"/papers/ssrn/7486138/","summary":"Using production, employment, and sales data across 426 industries, the research shows that upstream industry signals predict aggregate monthly stock returns with 23.8% out-of-sample R-squared.","featured":"2026-09-25","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"fanfare"},{"title":"Discounted Sales of Expiring Perishables: Challenges for Demand Forecasting in Grocery Retail Practice","url":"/papers/arxiv/2602.04464/","summary":"Including discounted sales of soon-to-expire perishables in demand forecasts leads to underestimating demand, highlighting the need for better forecasting to reduce inventory waste in grocery stores.","featured":"2026-02-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":0,"scale":"shares"},{"title":"Directional-shift Dirichlet ARMA models for compositional time series with structural break intervention","url":"/papers/arxiv/2601.16821/","summary":"The article introduces a new Bayesian model that analyzes compositional time series data, effectively handling structural breaks and enhancing forecasting accuracy during these changes.","featured":"2026-02-02","label":"arXiv","topic":"Econometrics & Forecasting","cites":4,"score":0,"scale":"shares"},{"title":"History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis","url":"/papers/arxiv/2601.10143/","summary":"A new adaptive data management system enhances model performance in quantitative finance by constantly updating to reflect market changes, addressing the shortcomings of relying solely on historical data.","featured":"2026-01-16","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":1,"scale":"shares"},{"title":"Sample Size Issues in Finance Research","url":"/papers/ssrn/4463148/","summary":"The study promotes the use of Bayesian statistics in finance to better analyze large AI-generated datasets and mitigate misleading significance from traditional methods.","featured":"2025-12-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":300,"scale":"shares"},{"title":"A3T-GCN for FTSE100 Components Price Forecasting","url":"/papers/arxiv/2511.21873/","summary":"A combined A3T-GCN model enhances the accuracy of FTSE100 stock price forecasts by using technical indicators and optimized sequences.","featured":"2025-12-01","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":0,"scale":"shares"},{"title":"Blameocracy: Causal Rhetoric in Politics","url":"/papers/arxiv/2504.06550/","summary":"U.S. Causal Rhetoric: Growing blame/credit language in congressional tweets changed donation patterns, fueled protests and polarization, and shifted public trust.","featured":"2025-11-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":21,"scale":"shares"},{"title":"FinCARE: Financial Causal Analysis with Reasoning and Evidence","url":"/papers/arxiv/2510.20221/","summary":"KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.","featured":"2025-10-27","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":7,"scale":"shares"},{"title":"Robust Optimization in Causal Models and G-Causal Normalizing Flows","url":"/papers/arxiv/2510.15458/","summary":"We show interventionally robust optimization is continuous under a G‑causal Wasserstein distance and introduce a causal normalizing flow that respects this, improving data augmentation for causal prediction and portfolio optimization.","featured":"2025-10-27","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":17,"scale":"shares"},{"title":"Demand Forecasting for New Fashion","url":"/papers/repec/wly-jforec-v-44-y-2025-i-2-p-270-280/","summary":"Fashion product demand is hard to predict, but machine learning—especially deep learning and ensembles—can make forecasts more accurate.","featured":"2025-10-27","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"Predicting Vehicle Wait Times at Borders","url":"/papers/repec/eee-retrec-v-89-y-2021-i-c-s0739885921000068/","summary":"The study explores new data sources and machine learning techniques to forecast short-term wait times at a US-Mexico border crossing, emphasizing the difficulties of high data variability.","featured":"2025-10-24","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":25,"scale":"shares"},{"title":"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation","url":"/papers/arxiv/2509.05922/","summary":"The research uses machine learning to identify that the volatility of options-implied risk and market liquidity are key factors causing market lows, challenging simpler models.","featured":"2025-09-13","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":19,"scale":"shares"},{"title":"Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events","url":"/papers/arxiv/2509.08183/","summary":"The paper presents a risk model that combines heavy-tailed priors with chaotic dynamics to predict volatility clustering, fat tails, and extreme events, providing a dual perspective for systemic risk analysis.","featured":"2025-09-13","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":12,"scale":"shares"},{"title":"FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model","url":"/papers/arxiv/2509.08742/","summary":"Multi-modal Forecasting: FinZero, a pre-trained model fine-tuned by the Uncertainty-adjusted Group Relative Policy Optimization method, is introduced in the article, enhancing the accuracy, adaptability, and scalability of financial time series forecasting.","featured":"2025-09-13","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":6,"scale":"shares"},{"title":"Forecasting Probability Distributions of Financial Returns with Deep Neural Networks","url":"/papers/arxiv/2508.18921/","summary":"The research shows that deep neural networks can accurately forecast financial return distributions and are competitive with traditional models for risk assessment and portfolio management.","featured":"2025-08-29","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":9,"scale":"shares"},{"title":"FinCast: A Foundation Model for Financial Time-Series Forecasting","url":"/papers/arxiv/2508.19609/","summary":"Time-Series Forecasting Model: FinCast, a new model for financial time-series forecasting, outperforms existing methods by effectively capturing diverse patterns without needing domain-specific adjustments.","featured":"2025-08-29","label":"arXiv","topic":"Econometrics & Forecasting","cites":19,"score":10,"scale":"shares"},{"title":"The Coherent Multiplex: Scalable Real-Time Wavelet Coherence Architecture","url":"/papers/arxiv/2508.19994/","summary":"Wavelet Coherence Architecture: The Coherent Multiplex system uses a multilayer graph to identify and analyze coherence among multiple time series in real-time, with potential uses in neuroscience, finance, and biomedical signal analysis.","featured":"2025-08-29","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":6,"scale":"shares"},{"title":"Stealing accuracy: Predicting day-ahead electricity prices with temporal hierarchy forecasting (THieF)","url":"/papers/arxiv/2508.11372/","summary":"The research introduces temporal hierarchy forecasting in predicting electricity prices, showing that reconciling forecasts for different time blocks improves accuracy at all levels.","featured":"2025-08-20","label":"arXiv","topic":"Econometrics & Forecasting","cites":4,"score":8,"scale":"shares"},{"title":"Deformation of semicircle law for correlated time series and Phase transition","url":"/papers/arxiv/2508.07192/","summary":"The study investigates the eigenvalue of the Wigner random matrix derived from a time series with temporal correlation, discussing the deformation of the semi-circle law and its moments of distribution and convergence.","featured":"2025-08-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":1,"scale":"shares"},{"title":"iQRA for Electricity Markets","url":"/papers/arxiv/2507.15079/","summary":"A new method, Isotonic Quantile Regression Averaging (iQRA), for generating probabilistic forecasts from point forecast ensembles in electricity markets, outperforms other methods in reliability and sharpness.","featured":"2025-07-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":null,"score":7,"scale":"shares"},{"title":"Forecasting NYC Yellow Taxi Ridership Decline: A Time Series Analysis of Daily Passenger Counts (2017-2019)","url":"/papers/arxiv/2507.10588/","summary":"A study predicting daily passenger counts for New York City's yellow taxis from 2017-2019 shows a consistent decline in ridership, with the most accurate predictions made using a first-order autoregressive model.","featured":"2025-07-17","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":5,"scale":"shares"},{"title":"Efficiency through Evolution, A Darwinian Approach to Agent-Based Economic Forecast Modeling","url":"/papers/arxiv/2507.04074/","summary":"The article presents a new Darwinian Agent-Based Modeling method for macroeconomic forecasting, which uses evolutionary principles and simple rules to create realistic economic patterns efficiently.","featured":"2025-07-10","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":8,"scale":"shares"},{"title":"Temperature Sensitivity of Residential Energy Demand on the Global Scale: A Bayesian Partial Pooling Model","url":"/papers/arxiv/2506.22768/","summary":"A study found that global residential energy demand rises at temperatures below -5 degrees Celsius and above 30 degrees Celsius, with developed countries more sensitive to high temperatures.","featured":"2025-07-03","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Unified Econometrics Discipline","url":"/papers/ssrn/5245790/","summary":"The article discusses the rapid growth and transformation of econometrics, emphasizing advances in cross-sectional data analysis, policy analysis, and time series techniques.","featured":"2025-06-25","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":23,"scale":"shares"},{"title":"Predicting Stock Market Crash with Bayesian Generalised Pareto Regression","url":"/papers/arxiv/2506.17549/","summary":"The study uses a Bayesian Generalised Pareto Regression model to predict extreme losses in India's Nifty 50 index, showing increased tail risk with higher market volatility and the model's effectiveness in forecasting tail risk in emerging markets.","featured":"2025-06-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":12,"scale":"shares"},{"title":"Bayesian VAR Count Data Forecasting","url":"/papers/ssrn/5285954/","summary":"The article introduces a new method for predicting and modeling time series data, capable of managing overdispersion, skewness, and changing volatility.","featured":"2025-06-11","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":5,"scale":"shares"},{"title":"Mathematical Causal Graphs","url":"/papers/ssrn/5284544/","summary":"The paper presents a mathematical framework for studying Causal Graphs with Dynamic Trace GCTD, aiming to pioneer a new research field in discrete mathematics and network theory.","featured":"2025-06-11","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"Climate Normals Estimation","url":"/papers/ssrn/5284152/","summary":"The article highlights the need to quantify the interannual variability in climatological time series for accurate El NiñoSouthern Oscillation predictions, questioning current methodologies' effectiveness in a changing climate.","featured":"2025-06-11","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"Time Series Stationarity Testing","url":"/papers/ssrn/5287311/","summary":"The article emphasizes the importance of the DickeyFuller Test and Augmented DickeyFuller ADF Test in confirming time series stationarity, crucial in actuarial science, quantitative finance, and machine learning.","featured":"2025-06-11","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning","url":"/papers/arxiv/2506.07421/","summary":"A machine learning study found that private tutoring in Japan can have positive socioeconomic impacts, but these are undermined by economic disparities among households.","featured":"2025-06-11","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":5,"scale":"shares"},{"title":"RealTime IV Surface Forecasting","url":"/papers/ssrn/5275880/","summary":"A novel two-step real-time sequential forecasting framework is introduced for predicting option implied volatility surface, which performs better than random walk forecasts.","featured":"2025-06-04","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Deep Learning Enhanced Multivariate GARCH","url":"/papers/arxiv/2506.02796/","summary":"A new volatility modeling framework, LSTM-BEKK, is introduced, integrating deep learning into multivariate GARCH processes for improved robustness and forecasting in financial return data.","featured":"2025-06-04","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":13,"scale":"shares"},{"title":"Safe Asset Emergence in Global Housing Returns","url":"/papers/ssrn/5269749/","summary":"New data and time series on global real estate returns over centuries have been presented, combining historical primary data with machine learning approaches and econometrics.","featured":"2025-05-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Hybrid Models for Forecasting","url":"/papers/ssrn/5268691/","summary":"The research uses traditional econometric models, machine learning, and deep learning techniques to predict financial time series, using SP 500 index and Bitcoin data, and assesses the models based on forecast error metrics and trading performance indicators.","featured":"2025-05-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Comparative Analysis of Financial Data Techniques","url":"/papers/ssrn/5268353/","summary":"The study contrasts the traditional method of calculating logarithmic returns with the fractional differencing method in data preparation for machine learning models, finding that fractional differentiation methods enhance predictive model forecasting performance.","featured":"2025-05-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Bayesian Hydraulic Model Calibration","url":"/papers/ssrn/5265012/","summary":"A Bayesian calibration framework uses convolutional neural networks to efficiently quantify uncertainty and infer parameters in flood-prone areas with limited data.","featured":"2025-05-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":5,"scale":"shares"},{"title":"Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models","url":"/papers/arxiv/2505.19617/","summary":"ARIMA with SVM/LSTM: A study using econometric models, machine learning, and deep learning to predict financial trends for the S&P 500 and Bitcoin emphasizes the importance of well-constructed hybrid models for profitable trading strategies.","featured":"2025-05-30","label":"arXiv","topic":"Econometrics & Forecasting","cites":20,"score":18,"scale":"shares"},{"title":"Multivariate Affine GARCH","url":"/papers/ssrn/5260415/","summary":"A specific financial model can capture time-varying volatility and dynamic correlation across asset returns, useful for portfolio optimization and option pricing.","featured":"2025-05-21","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction","url":"/papers/arxiv/2505.13558/","summary":"A new model, Clustering and Attention mechanism GRU (CAGRU), has been proposed for predicting customer buying intentions, using customer characteristics and a GRU neural network to provide more accurate predictions across different customer groups.","featured":"2025-05-21","label":"arXiv","topic":"Econometrics & Forecasting","cites":3,"score":16,"scale":"shares"},{"title":"A Set-Sequence Model for Time Series","url":"/papers/arxiv/2505.11243/","summary":"The article presents a Set-Sequence model for financial predictions, eliminating the need for manually created features, learning a shared summary at each period and predicting outcomes, performing better than benchmarks on stock return prediction and mortgage behavior tasks.","featured":"2025-05-21","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":14,"scale":"shares"},{"title":"Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)","url":"/papers/arxiv/2505.12806/","summary":"A new method for fitting acyclic vector autoregressive processes provides a flexible way to identify hierarchical causal networks in time series systems, useful in econometrics and social network analysis.","featured":"2025-05-21","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":13,"scale":"shares"},{"title":"A SWOT Analysis of Artificial Intelligence in Economic Forecasting","url":"/papers/ssrn/5247014/","summary":"The SWOT analysis of AI in economic forecasting shows its ability to handle complex data and improve predictions, but also reveals its transparency issues and high data requirements.","featured":"2025-05-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Offshore Wind Power Prediction","url":"/papers/ssrn/5254054/","summary":"The Informer, a new deep learning algorithm, is used for ultra-short-term offshore wind power prediction, effectively extracting features and capturing sequence dependency from long time-series data.","featured":"2025-05-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Quantum and DNA Computing for School Cyberattacks","url":"/papers/ssrn/5253362/","summary":"The paper discusses the potential of quantum computing and DNA computing to improve predictive analytics in cybersecurity for educational institutions, suggesting a hybrid quantum-DNA forecasting framework.","featured":"2025-05-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"RealTime PV Power Forecasting","url":"/papers/ssrn/5241274/","summary":"The XGBoost model, using historical weather and PV output data, offers more precise ultrashort-term PV power predictions than the SVR model, contributing to grid stability.","featured":"2025-05-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Short-Term Rental Price Forecasting","url":"/papers/ssrn/5243654/","summary":"The study introduces a forecasting model for short-term rental prices using open-access data, identifying key pricing factors and promoting digital equity through accessible advanced housing analytics.","featured":"2025-05-07","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Forecasting Technology Adoption","url":"/papers/ssrn/5240826/","summary":"The research proposes a method to predict the adoption of innovative technology products, considering individual willingness to adopt and social influences.","featured":"2025-05-07","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Model Validation: Out-of-sample vs. Out-of-time","url":"/papers/ssrn/5241694/","summary":"Out-of-sample vs. Out-of-time: The research suggests that out-of-sample model validation may underestimate forecast errors and negatively impact model selection, particularly when models are misspecified.","featured":"2025-05-07","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Latent Variable Estimation in Bayesian Black-Litterman Models","url":"/papers/arxiv/2505.02185/","summary":"The research modifies the Bayesian Black-Litterman portfolio model to be fully data-driven, eliminating subjective investor views, improving Sharpe ratios, and reducing turnover.","featured":"2025-05-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":20,"scale":"shares"},{"title":"Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models","url":"/papers/arxiv/2505.03109/","summary":"The research compares seven Deep Learning models for use in the renewable energy sector, with Long-Short Term Memory and Multilayer Perceptron models proving most accurate.","featured":"2025-05-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":6,"score":13,"scale":"shares"},{"title":"Machine Learning Algorithms For Accurate Stock Market Forecasting: Case Studies 2012","url":"/papers/ssrn/5228711/","summary":"The article explores the use of machine learning in predicting stock market movements and timing market entry, addressing challenges in financial data mining.","featured":"2025-04-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"},{"title":"Multi-Horizon Echo State Network Prediction of Intraday Stock Returns","url":"/papers/arxiv/2504.19623/","summary":"The research introduces a return prediction framework for intraday returns using Echo State Network models, providing efficient implementation and strong forecasting performance.","featured":"2025-04-30","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":9,"scale":"shares"},{"title":"Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting","url":"/papers/arxiv/2504.19309/","summary":"The paper suggests a hybrid architecture combining Convolutional Neural Networks and Transformers for efficient short- and long-term financial time series data modeling, showing improved performance in intraday stock price prediction.","featured":"2025-04-30","label":"arXiv","topic":"Econometrics & Forecasting","cites":7,"score":14,"scale":"shares"},{"title":"Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction","url":"/papers/arxiv/2504.17313/","summary":"The Patched Channel Integration Encoder (PCIE) model, a new method using multiple stock channels and unique tokenization, has improved stock price forecasting and prediction.","featured":"2025-04-30","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":13,"scale":"shares"},{"title":"Making Work Meaningful","url":"/papers/ssrn/5223106/","summary":"The research identifies social impact as the key factor in making work meaningful, based on an analysis of panel data.","featured":"2025-04-23","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":6,"scale":"shares"},{"title":"Causal Discovery via Simultaneous DAG Recovery Using the Angles Space of Directional Dependence Measures","url":"/papers/arxiv/2504.15268/","summary":"The paper presents a new method, Nonparametric Angles-based Correlation (NAbC), for defining the finite-sample distributions of any dependence measure, improving the modeling of financial portfolios under various data conditions.","featured":"2025-04-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":26,"scale":"shares"},{"title":"The heterogeneous causal effects of the EU's Cohesion Fund","url":"/papers/arxiv/2504.13223/","summary":"A study measures the impact of the EU's Cohesion Fund on regional output and investment, showing its peak effect within the first seven years, particularly in poorer regions.","featured":"2025-04-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":9,"score":16,"scale":"shares"},{"title":"Cross-Modal Temporal Fusion for Financial Market Forecasting","url":"/papers/arxiv/2504.13522/","summary":"The article presents Cross-Modal Temporal Fusion (CMTF), a new transformer-based system that uses diverse financial data to enhance the precision of financial market predictions.","featured":"2025-04-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":10,"score":22,"scale":"shares"},{"title":"Kolmogorov-Arnold Networks for Time Series Analysis","url":"/papers/ssrn/5217399/","summary":"Kolmogorov-Arnold Networks (KANs) are a strong alternative to MultiLayer Perceptron (MLP) for time series analysis and forecasting, as outlined in the survey.","featured":"2025-04-16","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Do determinants of EV purchase intent vary across the spectrum? Evidence from Bayesian analysis of US survey data","url":"/papers/arxiv/2504.09854/","summary":"A study finds that consumers knowledgeable about electric vehicles and their environmental benefits, and who trust in the growth of charging stations, are more likely to consider buying one.","featured":"2025-04-16","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":13,"scale":"shares"},{"title":"Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting","url":"/papers/arxiv/2504.02518/","summary":"The study introduces a faster, more accurate online model for predicting electricity prices.","featured":"2025-04-09","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":29,"scale":"shares"},{"title":"Rationally Turbulent Expectations Chapter 4: Cumulant Hierarchy","url":"/papers/ssrn/5196021/","summary":"The book portrays the capital market as a rational learning entity, with expectations often more unstable than the trends they try to forecast.","featured":"2025-04-02","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Hierarchical sparse Bayesian multitask learning for disease prediction in pooled microbiome studies","url":"/papers/arxiv/2502.02552/","summary":"The article discusses a hierarchical Bayesian multitask learning model for binary classification learning, which effectively predicts human health status using microbiome profiles.","featured":"2025-04-02","label":"Machine learning","topic":"Econometrics & Forecasting","cites":1,"score":8,"scale":"shares"},{"title":"Quantum Machine Learning for Forecasting","url":"/papers/ssrn/5180119/","summary":"Research shows that Quantum Machine Learning provides better financial forecasting accuracy and cybersecurity resilience than traditional models.","featured":"2025-03-20","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Capital Gain Integration in Valuation","url":"/papers/ssrn/5178507/","summary":"The gain of capital concept, accounting for biases in forecasting, investor perceptions, and firm characteristics, is introduced to enhance traditional discounted cash flow models and better determine firm valuations.","featured":"2025-03-20","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Stock Returns Prediction with Technical Indicators","url":"/papers/ssrn/5173702/","summary":"The paper presents a method that combines semiparametric partially additive time series models with technical indicators to enhance the prediction of stock returns.","featured":"2025-03-20","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":26,"scale":"shares"},{"title":"CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation","url":"/papers/arxiv/2503.04164/","summary":"CoFinDiff, a new synthetic financial data generation model, is designed to overcome the shortcomings of traditional statistical models, producing synthetic data that matches any conditions.","featured":"2025-03-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":20,"score":14,"scale":"shares"},{"title":"Maine's Forestry and Logging Industry: Building a Model for Forecasting","url":"/papers/arxiv/2503.06087/","summary":"Research predicts that Maine's forestry and logging industry's contribution may remain stable, but local communities could suffer from decreased employment and firms, and increased tariffs could cause further damage.","featured":"2025-03-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":12,"scale":"shares"},{"title":"FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models","url":"/papers/arxiv/2503.06928/","summary":"Financial Prediction Evaluation Suite: The research links time series forecasting models with financial asset pricing, through dataset construction, model validation, metric development, and performance assessment.","featured":"2025-03-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":3,"score":10,"scale":"shares"},{"title":"Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market","url":"/papers/doi/10-1016-j-jcomm-2024-100449/","summary":"A new method for predicting long-term electricity prices, which combines forecasts and extrapolates price series, has improved accuracy by 3% to 15% in German and Spanish power markets.","featured":"2025-03-05","label":"arXiv","topic":"Econometrics & Forecasting","cites":10,"score":13,"scale":"shares"},{"title":"Using quantile time series and historical simulation to forecast financial risk multiple steps ahead","url":"/papers/arxiv/2502.20978/","summary":"A new method for estimating Value-at-Risk (VaR) and Expected Shortfall (ES) models allows for tail risk forecasting and can be applied to any data or model where the VaR and ES relationship remains constant.","featured":"2025-03-05","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":14,"scale":"shares"},{"title":"Oil Price Forecasting: Machine Learning vs Deep Learning","url":"/papers/repec/spr-annopr-v-345-y-2025-i-2-d-10-1007-s10479-023-05400-8/","summary":"Machine Learning vs Deep Learning: The study reveals that deep learning methods, particularly the long short-term memory approach, are more effective than machine learning methods like the support vector machine in predicting oil prices, especially during crises.","featured":"2025-03-05","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":31,"scale":"shares"},{"title":"Time Series Analysis","url":"/papers/ssrn/5140015/","summary":"The paper discusses common time series models used in finance for asset price prediction, risk management, and portfolio optimization, and outlines future research challenges.","featured":"2025-02-26","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":14,"scale":"shares"},{"title":"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective","url":"/papers/arxiv/2502.14431/","summary":"The study analyzes the interdependence and sensitivity of US stock and commodity markets during the COVID-19 crash using Topological Data Analysis and Granger-causality.","featured":"2025-02-26","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":14,"scale":"shares"},{"title":"Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework","url":"/papers/arxiv/2502.16023/","summary":"The ContraSim algorithm is introduced as a tool to understand the correlation between daily financial headlines and market movements, enhancing the precision of financial forecasting.","featured":"2025-02-26","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":11,"scale":"shares"},{"title":"Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups","url":"/papers/arxiv/2502.14497/","summary":"The study shows a two-way link between news narratives and financial market shocks, indicating that changes in public discourse can trigger economic shifts and vice versa, with factors like partisanship and unexpected events like COVID-19 affecting this relationship.","featured":"2025-02-26","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":17,"scale":"shares"},{"title":"Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis","url":"/papers/arxiv/2502.15813/","summary":"A new hybrid model combining long-short-term memory networks and Graph Neural Networks enhances the accuracy of stock market predictions by capturing temporal patterns and complex inter-stock relationships, surpassing traditional and advanced benchmarks.","featured":"2025-02-26","label":"arXiv","topic":"Econometrics & Forecasting","cites":22,"score":13,"scale":"shares"},{"title":"Intraday Returns Forecasting in Brazil","url":"/papers/ssrn/5122977/","summary":"The study compares machine learning methods for forecasting Brazilian stock returns, with Ridge Regression performing best when considering transaction costs.","featured":"2025-02-19","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":30,"scale":"shares"},{"title":"Oil Production Decline Prediction","url":"/papers/ssrn/5124652/","summary":"Ensemble residual machine learning models are more effective than traditional models in oil production forecasting due to their ability to handle high nonlinearity in data.","featured":"2025-02-19","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":14,"scale":"shares"},{"title":"Multiscale Dynamics in Chinese Financial Markets","url":"/papers/repec/taf-tjorxx-v-76-y-2025-i-1-p-97-110/","summary":"The paper introduces a new statistical machine learning method for breaking down and analyzing complex time series, proving its effectiveness on financial data from the COVID-19 pandemic, suggesting it could replace traditional methods.","featured":"2025-02-19","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":13,"scale":"shares"},{"title":"Stock Investment Framework","url":"/papers/ssrn/5115392/","summary":"A new framework for stock investment selection has been proposed, using time series subpatterns and multirelationship fusion to better understand stock market relationships.","featured":"2025-02-05","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"Forecasting S&P 500 Using LSTM Models","url":"/papers/doi/10-5281-zenodo-14759118/","summary":"The report finds that LSTM models are more effective than ARIMA models in predicting the S&P 500 index due to their ability to handle volatile financial data.","featured":"2025-02-05","label":"arXiv","topic":"Econometrics & Forecasting","cites":11,"score":12,"scale":"shares"},{"title":"What is causal about causal models and representations?","url":"/papers/arxiv/2501.19335/","summary":"A study presents a new framework for interpreting actions in causal Bayesian networks, addressing the limitations of current methods and enhancing the understanding of causal representation learning.","featured":"2025-02-05","label":"Machine learning","topic":"Econometrics & Forecasting","cites":6,"score":24,"scale":"shares"},{"title":"LLMVaR Risk Forecasting","url":"/papers/ssrn/5104383/","summary":"The research introduces new methods for forecasting financial risk using large language models, finding these models effective for short-term but traditional models superior for long-term financial risk management.","featured":"2025-01-23","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":26,"scale":"shares"},{"title":"Crossing penalised CAViaR","url":"/papers/arxiv/2501.10564/","summary":"A new objective function has been proposed for estimating all quantiles in dynamic quantiles models, providing a more robust and flexible approach to handling multiple dynamic quantiles in time-series data.","featured":"2025-01-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel Data","url":"/papers/arxiv/2501.09917/","summary":"The study disputes the common view that wage inequality in the US is due to unobserved skills, instead attributing it to increasing skill volatility.","featured":"2025-01-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":7,"scale":"shares"},{"title":"Exploring the heterogeneous impacts of Indonesia’s conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests","url":"/papers/arxiv/2501.12803/","summary":"The research uses machine learning to study the effects of Indonesia's conditional cash transfer scheme on maternal health care, finding significant variations based on supply-side factors and poverty indicators.","featured":"2025-01-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":6,"scale":"shares"},{"title":"Causal Claims in Economics","url":"/papers/arxiv/2501.06873/","summary":"Research shows a rise in the use of causal claims in over 44,000 economic papers from 1980-2023, with complex causal narratives more likely to be published in top journals and receive more citations.","featured":"2025-01-15","label":"arXiv","topic":"Econometrics & Forecasting","cites":10,"score":16,"scale":"shares"},{"title":"Explainable ML for Aluminum Alloys Properties Prediction","url":"/papers/ssrn/5082633/","summary":"The study introduces a machine learning-based predictive framework for forecasting tensile properties of aluminum alloys, providing a cost-effective method to optimize alloy design.","featured":"2025-01-08","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Constrained Sampling with Primal-Dual Langevin Monte Carlo","url":"/papers/arxiv/2411.00568/","summary":"The study presents a PD-LMC algorithm that samples from a probability distribution while meeting statistical constraints, useful in Bayesian inference and prediction fairness.","featured":"2025-01-08","label":"Machine learning","topic":"Econometrics & Forecasting","cites":15,"score":35,"scale":"shares"},{"title":"Limitations of Post-Hoc Causal Interpretations","url":"/papers/ssrn/5074003/","summary":"The study criticizes post hoc causal interpretations in social sciences, advocating for machine learning as a supplementary tool for causal inference, not a standalone solution.","featured":"2025-01-01","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":7,"scale":"shares"},{"title":"Altered Monthly Asset Returns","url":"/papers/ssrn/5074864/","summary":"The termination of the CRSP tape in 2025 will alter 9.62% of monthly returns, but these changes will not significantly impact time-series average premia or their significance.","featured":"2025-01-01","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"A System of BSDEs with Singular Terminal Values Arising in Optimal Liquidation with Regime Switching","url":"/papers/arxiv/2412.19058/","summary":"A novel model is presented to address a stochastic control issue in optimal liquidation with dark pools, using a system of backward stochastic differential equations with jumps and singular terminal values.","featured":"2025-01-01","label":"arXiv","topic":"Econometrics & 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Forecasting","url":"/papers/repec/eee-intfor-v-41-y-2025-i-1-p-290-306/","summary":"The paper presents a new method for forecasting loss given default (LGD) by dividing features into groups, building individual models, and combining the results.","featured":"2025-01-01","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":1,"scale":"shares"},{"title":"Iterated Combination Method for Risk Measure Forecasts","url":"/papers/ssrn/5059002/","summary":"The study introduces an innovative technique, iterative combination, for forecasting Value-at-Risk and Expected Shortfall, proving it to be more effective than traditional methods.","featured":"2024-12-18","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":10,"scale":"shares"},{"title":"ML Forecasting for Investments","url":"/papers/ssrn/5045793/","summary":"The MLAGRUPPO model, combining machine learning and attention mechanism, can improve stock risk prediction and intelligent investment decision-making.","featured":"2024-12-18","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":11,"scale":"shares"},{"title":"Geometric Deep Learning for Realized Covariance Matrix Forecasting","url":"/papers/arxiv/2412.09517/","summary":"A new method for forecasting asset return covariance matrices using a Riemannian-geometry-aware deep learning framework outperforms traditional methods by considering the geometric properties of the matrices.","featured":"2024-12-18","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":8,"scale":"shares"},{"title":"S&P 500 Trend Prediction","url":"/papers/arxiv/2412.11462/","summary":"The study uses machine learning to predict S&P 500 trends, concluding that KNN is best for short-term predictions and XGBoost for long-term forecasts.","featured":"2024-12-18","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":5,"scale":"shares"},{"title":"Causal Diffusion Transformers for Generative Modeling","url":"/papers/arxiv/2412.12095/","summary":"The article discusses Causal Diffusion, a framework that enhances diffusion models' performance and allows a seamless shift between autoregressive and diffusion generation modes, achieving top results on the ImageNet generation benchmark.","featured":"2024-12-18","label":"Machine learning","topic":"Econometrics & Forecasting","cites":34,"score":20,"scale":"shares"},{"title":"Disciplining Forecasts","url":"/papers/ssrn/5046369/","summary":"The research introduces a portfolio optimization framework for the top 500 U.S. stocks, showing that efficient use of characteristic information and risk management can surpass value-weighted portfolios.","featured":"2024-12-12","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Systematic comparison of deep generative models applied to multivariate financial time series","url":"/papers/arxiv/2412.06417/","summary":"The study contrasts deep generative models (DGMs) and parametric models for creating financial time series, highlighting the advantages of DGMs in an implied volatility trading task.","featured":"2024-12-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":3,"scale":"shares"},{"title":"FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware","url":"/papers/arxiv/2412.07752/","summary":"The article introduces FlashRNN, a hardware-optimized solution for faster RNN processing, essential for time-series tasks and logical reasoning.","featured":"2024-12-12","label":"Machine learning","topic":"Econometrics & Forecasting","cites":6,"score":120,"scale":"shares"},{"title":"Right on Time: Revising Time Series Models by Constraining their Explanations","url":"/papers/arxiv/2402.12921/","summary":"The article introduces Right on Time (RioT), a method that helps correct confounders in time series models by interacting with model explanations across both the time and frequency domain.","featured":"2024-12-12","label":"Machine learning","topic":"Econometrics & Forecasting","cites":9,"score":26,"scale":"shares"},{"title":"Green Finance in Economic Cycles","url":"/papers/repec/eee-tefoso-v-209-y-2024-i-c-s0040162524005900/","summary":"The deep multi-layer perceptron (DMLP) and k-nearest neighbor (KNN) machine learning models can improve economic forecasting accuracy, especially when used with cross-validation and bootstrap bagging techniques.","featured":"2024-12-12","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":18,"scale":"shares"},{"title":"Stock Return Variation Across Countries","url":"/papers/repec/eee-finana-v-96-y-2024-i-pa-s1057521924005015/","summary":"The study uses machine learning to forecast country equity returns based on market traits, identifying significant predictability and key predictors.","featured":"2024-12-12","label":"RePEc","topic":"Econometrics & 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empirical models and distributional properties of prices in nonferrous metals spot and futures markets significantly affect the selection of contract, data frequency, variables, model type, estimation methods, and diagnostic tests.","featured":"2024-11-27","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":4,"scale":"shares"},{"title":"Quantile deep learning models for multi-step ahead time series prediction","url":"/papers/arxiv/2411.15674/","summary":"The article introduces a new deep learning framework for predicting multi-step time series, which improves the performance of deep learning models. It has been effectively tested on Bitcoin and Ethereum, demonstrating its ability to manage volatility and provide useful information for decision-making.","featured":"2024-11-27","label":"arXiv","topic":"Econometrics & Forecasting","cites":8,"score":5,"scale":"shares"},{"title":"China Business Cycle Forecasting","url":"/papers/repec/kap-compec-v-64-y-2024-i-5-d-10-1007-s10614-024-10549-w/","summary":"The study uses machine learning to predict China's business cycle using various indicators, with Logistic Regression being the most successful.","featured":"2024-11-27","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":18,"scale":"shares"},{"title":"Stock Market Index Forecasting with DLWR-LSTM Model","url":"/papers/repec/eee-finlet-v-68-y-2024-i-c-s1544612324008511/","summary":"The paper presents a DLWR-LSTM model for stock index forecasting, offering consistent accuracy regardless of time series variance.","featured":"2024-11-13","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":15,"scale":"shares"},{"title":"Causal Machine Learning","url":"/papers/ssrn/5010187/","summary":"Causal machine learning can transform ineffective marketing campaigns into profitable ones by targeting individual treatment effects, correlating with measures of loss aversion.","featured":"2024-11-06","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Forecasting Returns with CNNs in Korea","url":"/papers/ssrn/5008629/","summary":"A machine learning-based study successfully predicts short-term stock market trends in the Korean market, showcasing the potential of deep learning techniques in financial market predictability.","featured":"2024-11-06","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Time-Causal VAE: Robust Financial Time Series Generator","url":"/papers/arxiv/2411.02947/","summary":"The article discusses a time-causal variational autoencoder (TC-VAE) that generates reliable financial time series data. This data closely mirrors real market distribution and is beneficial for financial optimization tasks.","featured":"2024-11-06","label":"arXiv","topic":"Econometrics & Forecasting","cites":13,"score":6,"scale":"shares"},{"title":"Expert-aided causal discovery of ancestral graphs","url":"/papers/arxiv/2309.12032/","summary":"A novel method for causal inference uses ancestral graph sampling and expert feedback to refine causal discovery, providing uncertainty estimates and accounting for unobserved confounders.","featured":"2024-11-06","label":"Machine learning","topic":"Econometrics & Forecasting","cites":9,"score":15,"scale":"shares"},{"title":"Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks","url":"/papers/arxiv/2410.23275/","summary":"The authors propose a Dynamic Graph Neural Network for predicting in temporal financial networks, offering a tool for systemic risk monitoring in financial entities trading swap contracts.","featured":"2024-10-31","label":"Machine learning","topic":"Econometrics & Forecasting","cites":0,"score":7,"scale":"shares"},{"title":"Bayesian GEVMAR Model for Actuarial Data","url":"/papers/ssrn/4993360/","summary":"The study uses a new model to analyze nonstandard actuarial data in general insurance, showing better results than the previous Gaussian model.","featured":"2024-10-23","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"MGARCH Model","url":"/papers/ssrn/4990401/","summary":"A new study using a multivariate GARCH model identifies shocks and volatility spillovers in speculative return systems, using SP 500 returns, Treasury yields, and the U.S. Dollar Index.","featured":"2024-10-23","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":7,"scale":"shares"},{"title":"Can GANs Learn the Stylized Facts of Financial Time Series?","url":"/papers/arxiv/2410.09850/","summary":"The study examines the capability of Generative Adversarial Networks in learning complex financial time series patterns, highlighting that their performance is greatly influenced by the generator architecture chosen.","featured":"2024-10-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":7,"score":13,"scale":"shares"},{"title":"Transformers for Solar Flare Forecasting","url":"/papers/ssrn/4983283/","summary":"The paper examines the use of machine learning algorithms, specifically Transformers, to predict solar flares and lessen their impact on Earth.","featured":"2024-10-17","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":6,"scale":"shares"},{"title":"Economic Growth Forecasting in Sverdlovsk Region","url":"/papers/repec/aiy-jnjaer-v-23-y-2024-i-3-p-674-695/","summary":"Machine learning, particularly the random forest model, is more effective in predicting the Gross Regional Product growth of Russia's Sverdlovsk region than traditional models, according to a study.","featured":"2024-10-17","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":28,"scale":"shares"},{"title":"Equity Price Model","url":"/papers/ssrn/4979203/","summary":"The 3MR Reactive Price Model uses linear regression and yield prediction to forecast future values of the S&P 500 index, rejecting the martingale hypothesis and allowing for a retrospective yield estimate.","featured":"2024-10-09","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Accelerating Training with Neuron Interaction and Nowcasting Networks","url":"/papers/arxiv/2409.04434/","summary":"The article explores the enhancement of weight nowcaster networks (WNNs) through neuron interaction and nowcasting (NiNo) networks, resulting in a 50% speed increase in neural network training for vision and language tasks.","featured":"2024-10-09","label":"Machine learning","topic":"Econometrics & 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discovery methods on observational data, revealing that score matching-based methods excel in difficult scenarios, setting a new evaluation standard for causal discovery methods.","featured":"2024-10-03","label":"Machine learning","topic":"Econometrics & Forecasting","cites":35,"score":22,"scale":"shares"},{"title":"Role of Econometric Tools and Techniques in Data Analysis","url":"/papers/ssrn/4957383/","summary":"The chapter discusses the role of econometrics in economic data analysis, highlighting its practical uses and key tools like regression analysis and time series forecasting.","featured":"2024-09-18","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Hybrid Wind Forecast Model","url":"/papers/ssrn/4957172/","summary":"The study introduces a new hybrid ensemble model for accurate wind speed forecasting, utilizing signal decomposition, deep learning models, and metaheuristic optimization algorithms.","featured":"2024-09-18","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Downside risk reduction using regime-switching signals: a statistical jump model approach","url":"/papers/arxiv/2402.05272/","summary":"The article introduces a regime-switching investment strategy using a statistical jump model, which can reduce risk and increase returns compared to traditional strategies.","featured":"2024-09-18","label":"arXiv","topic":"Econometrics & Forecasting","cites":16,"score":75,"scale":"shares"},{"title":"Stock Prediction Unveiled","url":"/papers/ssrn/4950981/","summary":"The third edition of Financial Modeling Excellence: Innovative Approaches to Stock Predictions delves into probabilistic models for stock price predictions, focusing on time series data and AR and MA models.","featured":"2024-09-10","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"Machine Learning and Econometrics for Real Estate","url":"/papers/repec/bla-reesec-v-52-y-2024-i-5-p-1308-1339/","summary":"The article uses a random effects model and machine learning to predict commercial real estate values, achieving a low error rate and providing useful data visualizations.","featured":"2024-09-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":23,"scale":"shares"},{"title":"Validating Causal Models with Quantitative Probing","url":"/papers/repec/bpj-causin-v-11-y-2023-i-1-p-23-n-1019/","summary":"The piece introduces a new method called quantitative probing for validating causal models, showcasing its success in simulations and offering a guide for its application in causal modelling.","featured":"2024-09-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":12,"scale":"shares"},{"title":"Price effects and pass-through of a VAT increase on restaurants in Germany: causal evidence for the first 12 months and a mega sports event","url":"/papers/arxiv/2409.01180/","summary":"A German study found that 31% of a VAT increase on restaurant services was immediately passed onto consumers, rising to 58% after six months, with the UEFA Euro 2024 having no effect on price changes.","featured":"2024-09-10","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":8,"scale":"shares"},{"title":"A Financial Time Series Denoiser Based on Diffusion Models","url":"/papers/arxiv/2409.02138/","summary":"Using the diffusion model as a denoiser for financial time series data improves data predictability and trading performance, leading to more profitable trades and enhanced trading efficiency.","featured":"2024-09-10","label":"arXiv","topic":"Econometrics & Forecasting","cites":20,"score":7,"scale":"shares"},{"title":"Bayesian Post-Processing for RS Image Classification","url":"/papers/ssrn/4943043/","summary":"A new algorithm using Empirical Bayes approach enhances machine learning results in remote sensing image analysis.","featured":"2024-09-05","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Optimizing Predictive Analytics for Energy Consumption Forecasting: A Case Study in the Nigerian Power Sector","url":"/papers/ssrn/4946145/","summary":"The study applies predictive analytics and machine learning to enhance energy efficiency in Nigeria's power sector.","featured":"2024-09-05","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems","url":"/papers/arxiv/2408.17387/","summary":"The study uses Bayesian optimization to solve complex, two-stage stochastic programs, showing better results than standard methods and providing a more efficient solution to uncertain optimization problems.","featured":"2024-09-05","label":"Machine learning","topic":"Econometrics & Forecasting","cites":2,"score":9,"scale":"shares"},{"title":"Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis","url":"/papers/arxiv/2408.12115/","summary":"The paper presents a new method using the hybrid neural network model CNN-BiGRU-SSA for precise prediction and optimization of cross-border commodity pricing strategies, demonstrating superior performance on various datasets.","featured":"2024-08-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":11,"score":3,"scale":"shares"},{"title":"Enhancing causal discovery in financial networks with piecewise quantile regression","url":"/papers/arxiv/2408.12210/","summary":"The article presents a new method for building financial networks using quantile regression and a piecewise linear embedding scheme. This method uncovers intricate tail interactions in financial markets and identifies Bitcoin as the main influencer.","featured":"2024-08-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":3,"scale":"shares"},{"title":"Amortized Bayesian Multilevel Models","url":"/papers/arxiv/2408.13230/","summary":"Research explores the use of neural network architectures in Bayesian Multilevel Models to enable efficient training and inference on unseen data sets, solving computational challenges and providing quick posterior inference.","featured":"2024-08-28","label":"Machine learning","topic":"Econometrics & Forecasting","cites":13,"score":8,"scale":"shares"},{"title":"SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting","url":"/papers/arxiv/2404.14757/","summary":"The paper introduces the State Space Transformer model for time series forecasting, which effectively captures global and local patterns, offering superior performance with less memory and computational cost.","featured":"2024-08-28","label":"Machine learning","topic":"Econometrics & Forecasting","cites":36,"score":30,"scale":"shares"},{"title":"Forecasting FTSE Bursa Malaysia","url":"/papers/repec/rnd-arimbr-v-16-y-2024-i-2-p-104-114/","summary":"The study uses machine learning to predict stock prices in Malaysia, finding the Sequential Minimal Optimization Regression algorithm to be most accurate.","featured":"2024-08-28","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":15,"scale":"shares"},{"title":"Causality-Inspired Models for Financial Time Series Forecasting","url":"/papers/arxiv/2408.09960/","summary":"A novel framework for financial time series forecasting is presented, using causality models to improve prediction accuracy, especially in unstable markets.","featured":"2024-08-21","label":"arXiv","topic":"Econometrics & Forecasting","cites":8,"score":2,"scale":"shares"},{"title":"LENS: Large Pre-trained Transformer for Exploring Financial Time Series Regularities","url":"/papers/arxiv/2408.10111/","summary":"Financial Regularities: The article introduces PLUTUS, a pre-trained transformer-based model for financial time series modeling, setting a new benchmark in the field with its superior performance.","featured":"2024-08-21","label":"Machine learning","topic":"Econometrics & Forecasting","cites":3,"score":13,"scale":"shares"},{"title":"A GCN-LSTM Approach for ES-mini and VX Futures Forecasting","url":"/papers/arxiv/2408.05659/","summary":"A new predictive model using a multi-channel Graph Convolutional Network and Long Short-Term Memory network is proposed for forecasting E-mini S&P 500 and CBOE Volatility Index futures.","featured":"2024-08-15","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":7,"scale":"shares"},{"title":"Strong denoising of financial time-series","url":"/papers/arxiv/2408.05690/","summary":"The paper introduces a method to enhance the signal to noise ratio in financial data using auto-encoders, offering a new way to discover regularities in financial time-series.","featured":"2024-08-15","label":"arXiv","topic":"Econometrics & Forecasting","cites":2,"score":3,"scale":"shares"},{"title":"CSGFM Equity Forecasting","url":"/papers/repec/taf-ufajxx-v-78-y-2022-i-3-p-9-29/","summary":"Equity premium predictions for long-term country stocks based on a global factor model are more accurate than time-series models, resulting in substantial benefits in various developed equity markets.","featured":"2024-08-15","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":10,"scale":"shares"},{"title":"Efficient Asymmetric Causality Tests","url":"/papers/arxiv/2408.03137/","summary":"A study discusses the importance of significant differences between positive and negative components in asymmetric causality tests, applying this theory to the interaction between the world's two largest financial markets.","featured":"2024-08-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing","url":"/papers/arxiv/2408.02558/","summary":"The study introduces peer-induced fairness, a new framework for auditing algorithmic fairness, differentiating between adverse outcomes due to algorithmic bias and individual shortcomings, and offering understandable feedback for those impacted by unfavorable decisions.","featured":"2024-08-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":3,"scale":"shares"},{"title":"Stock Price Forecasting","url":"/papers/ssrn/4906691/","summary":"The research uses machine learning to predict stocks that will not have negative returns next year, recommending a Boglehead investment approach, with XGBoost providing the best results.","featured":"2024-07-31","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":4,"scale":"shares"},{"title":"Multivariate Cointegration","url":"/papers/ssrn/4906546/","summary":"The research shows that using multivariate cointegration for financial arbitrage strategies can generate returns without significantly increasing risk.","featured":"2024-07-31","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Scalable Monte Carlo for Bayesian Learning","url":"/papers/arxiv/2407.12751/","summary":"The book offers a graduate-level overview of advanced Markov chain Monte Carlo algorithms, focusing on scalable methods relevant to machine learning and AI.","featured":"2024-07-24","label":"Machine learning","topic":"Econometrics & Forecasting","cites":0,"score":30,"scale":"shares"},{"title":"Forecasting Covariance Matrices","url":"/papers/repec/oup-jfinec-v-22-y-2024-i-3-p-696-742/","summary":"A new model enhances the prediction accuracy of large realized covariance matrices of returns by breaking down the return covariance matrix using standard firm-level factors and sectoral restrictions.","featured":"2024-07-24","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":14,"scale":"shares"},{"title":"Financial Distress Prediction in Pakistan","url":"/papers/repec/eme-imefmp-imefm-10-2023-0404/","summary":"The study creates a predictive model for forecasting financial distress in Pakistani companies, finding financial ratio indices to be more effective than individual ratios.","featured":"2024-07-24","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":9,"scale":"shares"},{"title":"Regression Learners for Load Prediction","url":"/papers/ssrn/4893594/","summary":"The research develops an electrical load forecasting system using machine learning, based on three years of data from the Wavi substation in India.","featured":"2024-07-17","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Markov-Switching Trees","url":"/papers/repec/spr-alstar-v-108-y-2024-i-2-d-10-1007-s10182-024-00501-6/","summary":"The study suggests a method combining decision trees and time series modeling to predict play calls in the National Football League based on various factors.","featured":"2024-07-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":23,"scale":"shares"},{"title":"ML in Long-Term Mortality Forecasting","url":"/papers/repec/pal-gpprii-v-49-y-2024-i-2-d-10-1057-s41288-024-00320-5/","summary":"The article presents a novel machine learning framework for long-term mortality forecasting, improving prediction accuracy and addressing the issue of diminishing patterns in long-term predictions.","featured":"2024-07-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":14,"scale":"shares"},{"title":"ML Forecasting for Standard Dominance","url":"/papers/repec/eee-tefoso-v-205-y-2024-i-c-s0040162524002956/","summary":"The study uses machine learning to predict the results of standard battles in the Chinese solid-state lighting industry, indicating that strong alliances, patent application experience, and marketization level increase a firm's chances of winning.","featured":"2024-07-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":12,"scale":"shares"},{"title":"Forecasting Chinese Economy","url":"/papers/repec/bla-acctfi-v-63-y-2023-i-1-p-719-767/","summary":"The research indicates that mixed-frequency factor models provide better forecasts of the Chinese economy, although they were not significantly superior during the Global Financial Crisis.","featured":"2024-07-10","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":10,"scale":"shares"},{"title":"New Approximate Mixing Concept for Time Series","url":"/papers/ssrn/4882128/","summary":"A new concept called approximate mixing for random variables on metric spaces provides a balance between traditional mixing assumptions and proves a central limit theorem for nonstationary time series on Hilbert spaces.","featured":"2024-07-03","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":9,"scale":"shares"},{"title":"Cost-aware Bayesian optimization via the Pandora's Box Gittins index","url":"/papers/arxiv/2406.20062/","summary":"The paper suggests using the Gittins index from the Pandora's Box problem in economics as a function for cost-aware Bayesian optimization.","featured":"2024-07-03","label":"Machine learning","topic":"Econometrics & Forecasting","cites":22,"score":7,"scale":"shares"},{"title":"Forecasting Accuracy in Markets","url":"/papers/repec/gam-jijfss-v-12-y-2024-i-3-p-59-d-1422975/","summary":"The study reveals that different models are more effective for different assets like gold, cocoa, and the S&P500 index, impacting risk management strategies.","featured":"2024-07-03","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":31,"scale":"shares"},{"title":"Universal randomised signatures for generative time series modelling","url":"/papers/arxiv/2406.10214/","summary":"The article presents a new model for analyzing financial data over time, using a unique Wasserstein-type distance method, and compares its performance with current standards.","featured":"2024-06-20","label":"arXiv","topic":"Econometrics & Forecasting","cites":8,"score":7,"scale":"shares"},{"title":"Machine Learning for Financial Forecasting","url":"/papers/repec/eme-regepp-rege-05-2022-0079/","summary":"Machine learning models performed better than market benchmarks during the Russia-Ukraine war, but not prior to the conflict, indicating caution should be used when forecasting stock prices with these models.","featured":"2024-06-20","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":33,"scale":"shares"},{"title":"Distributional Refinement Network: Distributional Forecasting via Deep Learning","url":"/papers/arxiv/2406.00998/","summary":"The article introduces the Distributional Refinement Network (DRN), a model that enhances predictive performance and interpretability in actuarial modelling by merging a baseline model with a flexible neural network.","featured":"2024-06-05","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":3,"scale":"shares"},{"title":"A Comparison of Standard Statistical, Machine Learning and Deep Learning Methods in Forecasting the Time Series","url":"/papers/ssrn/4840148/","summary":"The accuracy of Machine Learning and Deep Learning in forecasting macroeconomic indicators is compared to the traditional statistical method ARIMA.","featured":"2024-05-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"},{"title":"Poverty Prediction Nigeria","url":"/papers/ssrn/4838153/","summary":"The study uses a panel dataset to predict poverty status in Nigeria, finding that demographic and housing indicators can accurately predict poverty in 80% of cases.","featured":"2024-05-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":99,"scale":"shares"},{"title":"Causal Interactions’ Indicator Between Two Time-Series Through Extreme Variations of the Explanatory Power of the First Eigenvalue Using Lagged Correlation Matrices","url":"/papers/ssrn/4841224/","summary":"The paper presents a method for identifying causal interactions between variables, which has been validated in predicting stock return and volatility in financial markets.","featured":"2024-05-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Kalman's Error","url":"/papers/ssrn/4840827/","summary":"The article corrects a misconception in Kalman's estimator by discussing two types of orthogonality applied to data and time samples.","featured":"2024-05-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Selecting Experimental Sites for External Validity","url":"/papers/arxiv/2405.13241/","summary":"A proposed Bayesian decision-theoretic approach optimizes external validity in experiments, revealing efficiency losses when using evidence from randomly-selected sites or those with the largest expected treatment effects.","featured":"2024-05-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"},{"title":"Efficient mid-term forecasting of hourly electricity load using generalized additive models","url":"/papers/arxiv/2405.17070/","summary":"The paper introduces a new forecasting method using Generalized Additive Models for precise mid-term hourly electricity load forecasts, improving accuracy and understanding of component influences, beneficial for the power system industry.","featured":"2024-05-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":19,"score":3,"scale":"shares"},{"title":"Local Causal Discovery for Structural Evidence of Direct Discrimination","url":"/papers/arxiv/2405.14848/","summary":"The LD3 algorithm, which can identify evidence of direct discrimination in a polynomial time, offers a more efficient method for causal fairness analysis in complex decision systems.","featured":"2024-05-28","label":"Machine learning","topic":"Econometrics & Forecasting","cites":8,"score":6,"scale":"shares"},{"title":"Forecasting Metal Prices with Machine and Deep Learning Models","url":"/papers/repec/eee-jrpoli-v-92-y-2024-i-c-s0301420724004070/","summary":"The research uses machine and deep learning models to forecast metal futures, finding their efficiency varies based on metal choice, sample period, and inputs.","featured":"2024-05-28","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"TKAN: Temporal Kolmogorov-Arnold Networks","url":"/papers/ssrn/4825654/","summary":"The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.","featured":"2024-05-15","label":"SSRN","topic":"Econometrics & Forecasting","cites":189,"score":2,"scale":"shares"},{"title":"Machine Learning for Embrittlement Forecasting","url":"/papers/ssrn/4823393/","summary":"The research uses machine learning, specifically the Gradient Boosting algorithm, to predict how preirradiation hardening affects the transition temperature shift in RPV steel.","featured":"2024-05-15","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Forecasting with machine learning Shadow-Rate VARs","url":"/papers/ssrn/4828070/","summary":"The article examines the use of Shadow Rate Vector Autoregressions in macroeconomic forecasting, particularly the effects of shrinkage priors.","featured":"2024-05-15","label":"SSRN","topic":"Econometrics & Forecasting","cites":1,"score":3,"scale":"shares"},{"title":"Earnings Forecast Accuracy","url":"/papers/ssrn/4827682/","summary":"The study examines the link between model-based earnings forecast accuracy and portfolios sorted on implied cost of capital, highlighting that machine learning models provide the highest return spreads and the importance of considering transaction costs in financial analysis.","featured":"2024-05-15","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Decomposing weather forecasting into advection and convection with neural networks","url":"/papers/arxiv/2405.06590/","summary":"The research introduces a machine learning model for weather forecasting that separately learns horizontal and vertical atmospheric movements, surpassing existing methods in accuracy and efficiency.","featured":"2024-05-15","label":"Machine learning","topic":"Econometrics & Forecasting","cites":2,"score":13,"scale":"shares"},{"title":"Relationship between the S&P 500 index and its constituents: A regime-switching copula approach and a model-free asymmetry test","url":"/papers/ssrn/4815835/","summary":"The study reveals an asymmetric relationship between the returns of the S&P 500 index and its constituents during high market volatility, especially for stocks with lower dividends and higher return volatilities.","featured":"2024-05-08","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Accelerating Convergence in Bayesian Few-Shot Classification","url":"/papers/arxiv/2405.01507/","summary":"The research combines mirror descent-based variational inference with Gaussian process for few-shot classification, enhancing accuracy, uncertainty measurement, and faster convergence.","featured":"2024-05-08","label":"Machine learning","topic":"Econometrics & Forecasting","cites":2,"score":15,"scale":"shares"},{"title":"Diffusive Gibbs Sampling","url":"/papers/arxiv/2402.03008/","summary":"The article introduces Diffusive Gibbs Sampling (DiGS), a new method for sampling from multi-modal distributions, which performs better in tasks like Bayesian neural networks and molecular dynamics.","featured":"2024-05-08","label":"Machine learning","topic":"Econometrics & Forecasting","cites":24,"score":171,"scale":"shares"},{"title":"A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units","url":"/papers/ssrn/4810879/","summary":"The Gated Recurrent Straightthrough Unit (GRSTU), a new deep learning model, outperforms statistical jump models in identifying regime changes in the S&P500 index, especially with smaller datasets.","featured":"2024-05-01","label":"SSRN","topic":"Econometrics & 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Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Double/Debiased ML in Stata","url":"/papers/repec/tsj-stataj-v-24-y-2024-i-1-p-3-45/","summary":"The article presents a package named ddml for double/debiased machine learning in Stata, supporting estimators of causal parameters for five econometric models and is compatible with various supervised machine learning programs.","featured":"2024-04-24","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":20,"scale":"shares"},{"title":"Forecasting S&P 500 returns with ML","url":"/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-024-00644-0/","summary":"The LSTM classifier, a machine learning technique, can predict future stock prices more accurately than random choice, questioning the random walk and efficient market theories.","featured":"2024-04-24","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"Analyst Forecast","url":"/papers/ssrn/4796635/","summary":"A machine learning method categorizes analysts' forecast revisions into five types, improving accuracy and reducing information asymmetry in earnings announcements.","featured":"2024-04-17","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Predicting Implicit Patterns and Optimizing Market Entry and Exit Decisions in Stock Prices using integrated Bayesian CNN-LSTM with Deep Q-Learning as a Meta-Labeller","url":"/papers/ssrn/4794069/","summary":"The piece introduces a hybrid model that combines various AI techniques for predicting stock prices and optimizing trading decisions.","featured":"2024-04-17","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"ML for hierarchical time series forecasting","url":"/papers/repec/eee-intfor-v-40-y-2024-i-2-p-597-615/","summary":"A multi-output regression model is proposed for better supply chain forecasting, using variables from different hierarchical levels to generate reliable predictions.","featured":"2024-04-17","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":13,"scale":"shares"},{"title":"Nonparametric Time Series Bounds","url":"/papers/ssrn/4784190/","summary":"A study explores the properties of empirical risk minimization for time series, focusing on predicting a univariate time series belonging to a class of location-scale parameter-driven processes.","featured":"2024-04-10","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":148,"scale":"shares"},{"title":"Dividend Forecasting in the Age of Machine Learning","url":"/papers/ssrn/4784398/","summary":"The article shows the superior accuracy of machine learning in predicting dividends, highlighting its effectiveness in complex information structures and potential influence on corporate finance and investment decisions.","featured":"2024-04-10","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Regularization for electricity price forecasting","url":"/papers/arxiv/2404.03968/","summary":"The study reveals that LQ and elastic net penalty functions provide more precise electricity price predictions than other methods, including the popular LASSO. It also confirms that cross-validation is a useful tool for optimizing parameters.","featured":"2024-04-10","label":"arXiv","topic":"Econometrics & Forecasting","cites":13,"score":2,"scale":"shares"},{"title":"A Neural Framework for Generalized Causal Sensitivity Analysis","url":"/papers/arxiv/2311.16026/","summary":"Causal Sensitivity Analysis: The article introduces NeuralCSA, a new neural framework for analyzing causal sensitivity, capable of handling various models, treatments, and queries, and providing accurate causal query bounds.","featured":"2024-04-10","label":"Machine learning","topic":"Econometrics & Forecasting","cites":20,"score":54,"scale":"shares"},{"title":"Supervised autoencoder MLP for financial time series forecasting","url":"/papers/arxiv/2404.01866/","summary":"The study investigates the use of supervised autoencoders in improving financial forecasting through precise parameter tuning.","featured":"2024-04-03","label":"arXiv","topic":"Econometrics & Forecasting","cites":27,"score":3,"scale":"shares"},{"title":"Forecasting the Risk-Free Rates: Practical Forecasting Model for the Future of 10-Year US Treasury Yield through Exogenous Variables","url":"/papers/ssrn/4780234/","summary":"The study introduces a forecasting model for predicting the 10-Year US Treasury Yield based on variables like exchange rates and crude oil prices.","featured":"2024-04-03","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":7,"scale":"shares"},{"title":"Forecasting CPI","url":"/papers/repec/wly-jforec-v-43-y-2024-i-3-p-702-753/","summary":"The study enhances the precision and promptness of Consumer Price Index (CPI) forecasts by using a large Chinese news corpus and Internet search data, and combining penalized regression and mixed-frequency data sampling methods.","featured":"2024-04-03","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":9,"scale":"shares"},{"title":"Machine Learning for Causal Inference: Is a Nonlinear First Stage Really Forbidden in 2SLS?","url":"/papers/ssrn/4772060/","summary":"The paper shows that the bias in the two-stage least squares estimator can be split into an observable and unobservable bias, without needing to specify the first stage's functional form or validate the instrumental variable.","featured":"2024-03-27","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Valuation of Levered Equity and Debt Shield","url":"/papers/ssrn/4765068/","summary":"The note argues that discounting the debt tax shield at the cost of debt capital gross of corporate tax has several advantages, including not needing to forecast speculative tax shields due to future net borrowings.","featured":"2024-03-20","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Mean-Field Microcanonical Gradient Descent","url":"/papers/arxiv/2403.08362/","summary":"The study presents a new model that improves the efficiency of sampling multiple data points simultaneously, particularly in high-dimensional financial time series.","featured":"2024-03-20","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":9,"scale":"shares"},{"title":"Forecasting Gold Price","url":"/papers/repec/spr-annopr-v-334-y-2024-i-1-d-10-1007-s10479-021-04187-w/","summary":"The paper suggests using the eXtreme Gradient Boosting (XGBoost) machine learning model and Shapley additive explanations (SHAP) for accurate forecasting and interpretation of gold price fluctuations, surpassing other advanced models.","featured":"2024-03-20","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":13,"scale":"shares"},{"title":"Jumps and Margin Level Study","url":"/papers/repec/taf-reroxx-v-36-y-2023-i-2-p-2136228/","summary":"The SE-SVCJ model, combined with a GPD, provides a more accurate forecast of margin levels in the stock index futures market.","featured":"2024-03-20","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":14,"scale":"shares"},{"title":"TS-RSR: A Provably Efficient Approach for Batch Bayesian Optimization","url":"/papers/arxiv/2403.04764/","summary":"A novel method for batch Bayesian Optimization (BO) is introduced, which reduces redundancy and focuses on points with high predictive means or uncertainty, showing superior performance on nonconvex test functions.","featured":"2024-03-13","label":"Machine learning","topic":"Econometrics & Forecasting","cites":3,"score":13,"scale":"shares"},{"title":"Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues","url":"/papers/arxiv/2307.11888/","summary":"The study reveals that deep neural networks using linear complex-valued RNNs and MLPs can accurately approximate regular causal sequence-to-sequence maps, with complex eigenvalues near unit disk aiding in information storage.","featured":"2024-03-13","label":"Machine learning","topic":"Econometrics & Forecasting","cites":41,"score":91,"scale":"shares"},{"title":"Panel data nowcasting: P/E ratios","url":"/papers/repec/wly-japmet-v-39-y-2024-i-2-p-292-307/","summary":"P/E ratios: The study uses machine learning to accurately predict corporate earnings, outperforming other methods.","featured":"2024-03-13","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":20,"scale":"shares"},{"title":"Stock Return Forecasting with Machine Learning","url":"/papers/repec/eee-jfinec-v-153-y-2024-i-c-s0304405x2400014x/","summary":"The article uses machine learning to predict stock returns, challenging the efficient market hypothesis due to its strong predictive power. It also shows that machine learning models are effective in out-of-sample performance.","featured":"2024-03-06","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"Forecasting Wheat Futures with Convolutional Neural Networks","url":"/papers/ssrn/4733370/","summary":"The article uses a convolutional neural network to predict futures prices by analyzing aerial images of wheat fields and cloud cover, suggesting that unique algorithm and data choice can yield positive alpha in a short time frame.","featured":"2024-02-21","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Robust agents learn causal world models","url":"/papers/arxiv/2402.10877/","summary":"The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.","featured":"2024-02-21","label":"Machine learning","topic":"Econometrics & Forecasting","cites":102,"score":32,"scale":"shares"},{"title":"Regime Switching in Commodity Prices","url":"/papers/repec/taf-apeclt-v-31-y-2024-i-4-p-338-345/","summary":"A study from 1959 to 2022 using a 3-state Markov-switching model found that oil prices are more volatile than copper prices, reacting more to market cartelization, war episodes, and global demand shifts.","featured":"2024-02-21","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":11,"scale":"shares"},{"title":"Forecasting Single Family House Prices in the US using GMDH","url":"/papers/ssrn/4723624/","summary":"The article discusses the use of a machine learning technique, the group method of data handling (GMDH), for predicting house price index (HPI), leading to more accurate housing market forecasts.","featured":"2024-02-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"A Bayesian theory of market impact","url":"/papers/arxiv/2303.08867/","summary":"The research explains how large orders, split into smaller ones (meta-orders), affect prices in financial markets, suggesting that the square-root impact law originates from the over-estimation of order flows from meta-orders.","featured":"2024-02-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":6,"score":19,"scale":"shares"},{"title":"Where models fail: causality and self-reference in financial economics","url":"/papers/arxiv/2311.16570/","summary":"A research paper suggests that the use of unidirectional causation in capital market studies may be flawed, and a better understanding of empirical finance could be achieved by recognizing the limitations of current quantitative finance tools.","featured":"2024-01-30","label":"arXiv","topic":"Econometrics & Forecasting","cites":4,"score":21,"scale":"shares"},{"title":"Forecasting Bid–Ask Spreads in Foreign Exchange: Analysis and Machine Learning Prediction","url":"/papers/ssrn/4701477/","summary":"Machine learning algorithms can effectively predict transaction costs in the foreign exchange market, which can greatly vary based on factors like time of day or week.","featured":"2024-01-23","label":"SSRN","topic":"Econometrics & Forecasting","cites":1,"score":3,"scale":"shares"},{"title":"Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review","url":"/papers/arxiv/2401.10370/","summary":"The paper introduces new methods for conditional time series generation in financial risk modeling using deep generative methods, and presents a framework to assess the quality of the generated series.","featured":"2024-01-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":11,"score":6,"scale":"shares"},{"title":"The econometrics of happiness: Are We Underestimating the Returns to Education and Income?","url":"/papers/arxiv/1807.11835/","summary":"Value Rounding Behavior: The study addresses the issue of response scale simplification in surveys, particularly by less educated respondents, and introduces a model to estimate latent subjective wellbeing.","featured":"2024-01-23","label":"arXiv","topic":"Econometrics & Forecasting","cites":27,"score":27,"scale":"shares"},{"title":"Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm","url":"/papers/arxiv/2401.09376/","summary":"The research applies the Hui-Walter paradigm from epidemiology to machine learning, enabling accurate model evaluation under dynamic and uncertain data conditions without requiring labeled data.","featured":"2024-01-23","label":"Machine learning","topic":"Econometrics & Forecasting","cites":0,"score":20,"scale":"shares"},{"title":"ML for Financial Risk Measurement","url":"/papers/repec/aza-rmfi00-y-2023-v-17-i-1-p-43-52/","summary":"A new sequential learning algorithm based on Kalman filtering has proven to be more effective than traditional methods in measuring financial market risk.","featured":"2024-01-23","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":31,"scale":"shares"},{"title":"Hybrid Model for Index Futures Forecasting","url":"/papers/repec/eee-ecofin-v-69-y-2024-i-pb-s1062940823001456/","summary":"A new hybrid model called WT-ARIMA-LSTM has been introduced for share price index futures forecasting, offering superior accuracy and robust performance in various market conditions.","featured":"2024-01-09","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"Reviewing Large Dynamic Covariance Matrices","url":"/papers/repec/eee-ecosta-v-29-y-2024-i-c-p-16-30/","summary":"The article discusses recent advancements in estimating large, time-varying dynamic covariance matrices, with a focus on GARCH model extensions and identifying structural breaks in large covariance structures.","featured":"2024-01-09","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":12,"scale":"shares"},{"title":"Univariate Forecasting Models' Update Frequency","url":"/papers/repec/eee-ejores-v-314-y-2024-i-1-p-111-121/","summary":"Intermediate updating scenarios in univariate time series forecasting models can achieve similar or better accuracy with less computational cost, challenging the need for constant model updates.","featured":"2024-01-09","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":8,"scale":"shares"},{"title":"Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data","url":"/papers/arxiv/2312.17375/","summary":"Framework for Understanding Relationships: The article enhances the Constraint-based Causal Discovery algorithm to identify intricate causal relations between financial assets and variables, useful for factor-based investing and market dynamics comprehension.","featured":"2024-01-03","label":"arXiv","topic":"Econometrics & Forecasting","cites":8,"score":4,"scale":"shares"},{"title":"Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network (preprint)","url":"/papers/arxiv/2312.15535/","summary":"The research uses neural networks to predict exports of certain OECD countries and Iran from 2021-2025, suggesting that long-term export contracts are less impacted by crises like Covid-19, and should be considered in economic policies.","featured":"2024-01-03","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":5,"scale":"shares"},{"title":"Cluster-based Regression using Variational Inference and Applications in Financial Forecasting","url":"/papers/arxiv/2205.00605/","summary":"The paper introduces a method to identify clusters and estimate cluster-specific regression parameters using Variational Inference (VI), which is ideal for financial forecasting in markets with different regimes and market change patterns.","featured":"2024-01-03","label":"arXiv","topic":"Econometrics & Forecasting","cites":3,"score":45,"scale":"shares"},{"title":"ML Earnings Forecasts and Investor Expectations","url":"/papers/ssrn/4673392/","summary":"The research indicates that machine learning can enhance earnings forecasts, especially for small firms and longer horizons, and that investors' expectations align with the best machine forecast.","featured":"2024-01-03","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Modeling and Forecasting Cash-Flows in Private Investments","url":"/papers/ssrn/4673444/","summary":"The study analyzes cashflows in private investment strategies using a comprehensive dataset, demonstrating the effectiveness of the Yale model and suggesting improvements.","featured":"2024-01-03","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":4,"scale":"shares"},{"title":"Transfer Learning for Causal Effect Estimation","url":"/papers/arxiv/2305.09126/","summary":"A Transfer Causal Learning framework has been developed to improve the accuracy of causal effect estimation in scenarios with limited data, such as rare medical conditions.","featured":"2024-01-03","label":"Machine learning","topic":"Econometrics & Forecasting","cites":4,"score":15,"scale":"shares"},{"title":"Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative Analysis","url":"/papers/arxiv/2311.15218/","summary":"A new dataset combining numerical stock data with qualitative text data for sentiment extraction is presented, achieving over 60% accuracy for the Dow Jones Industrial Average.","featured":"2024-01-03","label":"Machine learning","topic":"Econometrics & Forecasting","cites":1,"score":23,"scale":"shares"},{"title":"Detecting toxic flow","url":"/papers/arxiv/2312.05827/","summary":"PULSE, a quick online Bayesian method, is introduced for predicting toxic trades, outperforming standard methods and offering real-time implementation.","featured":"2023-12-13","label":"arXiv","topic":"Econometrics & Forecasting","cites":13,"score":3,"scale":"shares"},{"title":"ML Panel Data Regressions for Heavy-Tailed Data","url":"/papers/repec/eee-econom-v-237-y-2023-i-2-s0304407622001282/","summary":"A study presents structured machine learning regressions for heavy-tailed dependent panel data, using a new concentration inequality for such data.","featured":"2023-12-13","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":19,"scale":"shares"},{"title":"Epistemic Limits of Empirical Finance: Causal Reductionism","url":"/papers/ssrn/4646664/","summary":"Causal Reductionism: The research criticizes the use of one-way causation in capital market studies, suggesting that current quantitative finance tools may only be suitable for after-the-fact causal inference.","featured":"2023-11-29","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":31,"scale":"shares"},{"title":"Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series","url":"/papers/arxiv/2311.13326/","summary":"The study examines the effectiveness of curriculum and imitation learning in managing complex time-series data, suggesting the former improves performance while the latter should be used with caution.","featured":"2023-11-29","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":19,"scale":"shares"},{"title":"Design-Robust Two-Way-Fixed-Effects Regression For Panel Data","url":"/papers/arxiv/2107.13737/","summary":"A new estimator for average causal effects in binary treatment with panel data has been proposed, offering better performance and robustness than the traditional two-way estimator, even with a misspecified fixed effect model.","featured":"2023-11-15","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":361,"scale":"shares"},{"title":"Bayesian Data Imputation: Missing Data Filling","url":"/papers/ssrn/4625229/","summary":"Missing Data Filling: The article highlights the role of data imputation in risk management, explaining its use in filling gaps in incomplete data for a better understanding of risk factors.","featured":"2023-11-08","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":189,"scale":"shares"},{"title":"Optimal Valuation Ratio: Forward Price Ratios","url":"/papers/ssrn/4625138/","summary":"Forward Price Ratios: The research criticizes the use of trailing price ratios for predicting stock market returns due to changes in cash flow growth, suggesting the use of forward price ratios scaled by cash flow forecasts for better valuation.","featured":"2023-11-08","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Reproducible parameter inference using bagged posteriors","url":"/papers/arxiv/2311.02019/","summary":"The BayesBag study introduces a technique of applying bagging to Bayesian posteriors to enhance reproducibility and uncertainty quantification in model misspecification.","featured":"2023-11-08","label":"Machine learning","topic":"Econometrics & Forecasting","cites":5,"score":5,"scale":"shares"},{"title":"Interpretive Earnings Forecasts via Machine Learning: A High-Dimensional Financial Statement Data Approach","url":"/papers/ssrn/4619313/","summary":"Using machine learning models and comprehensive Compustat financial statement data for earnings forecasting can yield predictions that are up to 13% more accurate than traditional linear approaches.","featured":"2023-11-02","label":"SSRN","topic":"Econometrics & Forecasting","cites":2,"score":7,"scale":"shares"},{"title":"Investor Returns: Market-Based Statistics","url":"/papers/ssrn/4614148/","summary":"Market-Based Statistics: The study presents three market-based approximations of actual return from market trades, which deviate from traditional evaluations based on time series analysis of investors' returns.","featured":"2023-11-02","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":25,"scale":"shares"},{"title":"Forecasting Transportation Demand in the U.S. Market","url":"/papers/ssrn/4611277/","summary":"The study forecasts U.S. domestic transportation demand using machine learning and econometric methods, with machine learning models providing more accurate predictions.","featured":"2023-10-25","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series","url":"/papers/doi/10-1016-j-ijforecast-2022-01-001/","summary":"The paper outlines a successful method for point and probabilistic forecasting using a mix of machine learning models, as demonstrated in the M5 Competition, highlighting the significance of diverse models and careful validation example selection.","featured":"2023-10-25","label":"arXiv","topic":"Econometrics & Forecasting","cites":17,"score":5,"scale":"shares"},{"title":"Few-Shot Learning Patterns in Financial Time Series for Trend-Following Strategies","url":"/papers/arxiv/2310.10500/","summary":"The article introduces X-Trend, a new time-series trend-following forecaster that adapts quickly to market changes, showing improved performance and faster recovery from the COVID-19 downturn than other models.","featured":"2023-10-18","label":"arXiv","topic":"Econometrics & Forecasting","cites":10,"score":3,"scale":"shares"},{"title":"Exact Computation of Censored Least Absolute Deviations Estimator for Panel Data with Fixed Effects","url":"/papers/ssrn/4598250/","summary":"The article shows that the computation of the censored least absolute deviations estimator for Panel Data can be done by redefining the estimation problem as a Mixed Integer Programming issue.","featured":"2023-10-12","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":6,"scale":"shares"},{"title":"Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions","url":"/papers/arxiv/2310.07427/","summary":"Quantum-Enhanced Forecasting for Time Series: The study introduces a time series forecasting method, Quantum Gramian Angular Field (QGAF), that merges quantum computing and deep learning to enhance the accuracy of time series classification and forecasting, and validates its effectiveness using major stock market datasets.","featured":"2023-10-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":32,"score":7,"scale":"shares"},{"title":"Latent Causal Socioeconomic Health Index.","url":"/papers/arxiv/2009.12217/","summary":"The article introduces a national LAtent Causal Socioeconomic Health (LACSH) index, combining latent health factor index with spatial and statistical causal modeling to analyze the causal effects on a latent trait with spatial correlation.","featured":"2023-10-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":43,"scale":"shares"},{"title":"Machine learning framework for forecasting sales of new products with short life cycles","url":"/papers/repec/eee-intfor-v-39-y-2023-i-4-p-1874-1894/","summary":"For predicting sales of short-lived products, simple ARIMAX is more effective than deep neural networks, but DNNs perform well when Gaussian white noise is added, a study finds.","featured":"2023-10-12","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":26,"scale":"shares"},{"title":"Multidimensional Time Series Data: Automated Regime Detection","url":"/papers/ssrn/4587877/","summary":"Automated Regime Detection: The Wasserstein k-means clustering algorithm effectively identifies regimes in synthetic one-dimensional time series data, and its use in multidimensional data is proposed via the sliced Wasserstein k-means method.","featured":"2023-10-04","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering","url":"/papers/arxiv/2310.01285/","summary":"The paper explores the use of Wasserstein k-means clustering on multidimensional time series data for automated regime detection, proving its effectiveness in identifying different market regimes in real financial time series.","featured":"2023-10-04","label":"arXiv","topic":"Econometrics & Forecasting","cites":4,"score":5,"scale":"shares"},{"title":"Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems","url":"/papers/arxiv/2309.16888/","summary":"The paper introduces a new data-driven method using a Transformer-based Multivariate Time Series Classifier to enhance decision making in Venture Capital and Growth Capital investments by predicting the success of potential investment targets.","featured":"2023-10-04","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":4,"scale":"shares"},{"title":"Financial Price Forecasting with Deep Learning Models","url":"/papers/arxiv/2305.04811/","summary":"The article explores deep learning models for financial forecasting, discussing their structures, uses, pros and cons, and potential future research areas.","featured":"2023-10-04","label":"arXiv","topic":"Econometrics & Forecasting","cites":null,"score":86,"scale":"shares"},{"title":"FSDA: Tackling Tail-Event Analysis in Imbalanced Time Series Data (ECML PKDD 2023 - LIDTA Slides)","url":"/papers/ssrn/4581944/","summary":"The FSDA method combines feature selection and data augmentation to enhance the accuracy of machine learning models in predicting rare events in imbalanced financial data.","featured":"2023-09-28","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Loss Function for Investment Strategies","url":"/papers/ssrn/4575978/","summary":"The article introduces a new Mean Absolute Directional Loss function to enhance the efficiency of machine learning models used in financial forecasting.","featured":"2023-09-21","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Bayesian Modeling of Dynamic Parameters","url":"/papers/ssrn/4575128/","summary":"The paper introduces a nonparametric time-varying parameter (TVP) model using Bayesian additive regression trees (BART) for macroeconomic models, providing flexibility in parameter change and easy inference.","featured":"2023-09-21","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Estimation and testing of forecast rationality with many moments","url":"/papers/arxiv/2309.09481/","summary":"The article examines the use of P-GMM moment selection procedure in estimating and testing forecast rationality, using data from the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters.","featured":"2023-09-21","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":5,"scale":"shares"},{"title":"Scoring Market Consensus Against Probability Forecasts","url":"/papers/repec/inm-ordeca-v-18-y-2021-i-3-p-169-184/","summary":"The paper proposes a new probability scoring rule that incentivizes forecasters to gather superior information and discourages them from following the crowd.","featured":"2023-09-21","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Radiological Consequences Assessment: Comparative Analysis","url":"/papers/ssrn/4570883/","summary":"Comparative Analysis: The Benchmarking on Assessment of Radiological Consequences (BARCO) project compares real-time forecasts of radiological impact in emergencies, offering recommendations for code users.","featured":"2023-09-14","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":5,"scale":"shares"},{"title":"Bayesian ANN for Efficiency Analysis","url":"/papers/repec/eee-econom-v-236-y-2023-i-2-s0304407623002075/","summary":"The paper introduces a novel method for frontier estimation in econometrics, merging Data Envelopment Analysis and Stochastic Frontier Analysis using Bayesian artificial neural networks, and validates its efficiency with Monte Carlo experiments and a dataset of large US banks.","featured":"2023-09-14","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":16,"scale":"shares"},{"title":"Machine Learning Predictability: Factors Affecting Stock Return Forecasts","url":"/papers/ssrn/4552395/","summary":"Factors Affecting Stock Return Forecasts: The study shows that while machine learning strategies can predict short-term returns for small firms and early historical data, they have not provided significant economic gains for most of the U.S. market in the past 20 years.","featured":"2023-08-30","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":7,"scale":"shares"},{"title":"Retail Demand Forecasting: A Comparative Study for Multivariate Time Series","url":"/papers/arxiv/2308.11939/","summary":"The study creates improved retail demand prediction models using macroeconomic factors and past sales data.","featured":"2023-08-24","label":"arXiv","topic":"Econometrics & Forecasting","cites":28,"score":4,"scale":"shares"},{"title":"ML & Deep Learning for Electricity Price Forecasting","url":"/papers/ssrn/4547250/","summary":"The paper introduces a seasonal attention mechanism through the BiLSTM model, improving forecasts of extreme prices in the British electricity market.","featured":"2023-08-24","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"GARCH Model Selection Bias","url":"/papers/ssrn/4546356/","summary":"Information criteria can impact the robustness of the News Impact Curve in financial time series due to their restrictive or slack nature when dealing with asymmetric volatility.","featured":"2023-08-24","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Panel Data Nowcasting: P/E Ratios","url":"/papers/ssrn/4547276/","summary":"P/E Ratios: The paper highlights the superior performance of structured machine learning regressions for nowcasting with panel data of different frequencies, especially in predicting corporate earnings.","featured":"2023-08-24","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Forecasting GCC Financial Stress with Neural Networks","url":"/papers/repec/kap-apfinm-v-30-y-2023-i-3-d-10-1007-s10690-022-09387-3/","summary":"The research uses a One-Dimensional Convolutional Neural Network to predict financial stress in the GCC oil, stock, and bond markets, and finds that financial stress indices and oil significantly improve forecasting performance and risk hedging.","featured":"2023-08-17","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":17,"scale":"shares"},{"title":"Econometrics and Analytics for Movie Success Forecasts","url":"/papers/repec/inm-ormnsc-v-68-y-2022-i-1-p-189-210/","summary":"Machine learning and social media data can enhance forecast accuracy in commercial applications, especially when combined with econometrics.","featured":"2023-08-17","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":22,"scale":"shares"},{"title":"Machine Learning and Modern Econometrics for Tail-Risk Protection","url":"/papers/ssrn/4540499/","summary":"The study introduces a dynamic tail risk protection strategy using machine learning, comparing various methods and suggesting an ensemble classifier for better performance.","featured":"2023-08-17","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Forecasting Oil Prices with VRP and Google Data","url":"/papers/ssrn/4534884/","summary":"The paper suggests that incorporating variance risk premium and Google search data into models improves real oil price forecasts, with penalized regressions providing the best results.","featured":"2023-08-09","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Asset Return Covariance Forecasting with Errors","url":"/papers/ssrn/4533182/","summary":"The study introduces new models for predicting the covariance of asset returns, taking into account measurement errors and maintaining high volatility and correlation persistence.","featured":"2023-08-09","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":72,"scale":"shares"},{"title":"Residual Factor Prediction Via Time Series-based Machine Learning","url":"/papers/ssrn/4532565/","summary":"The paper presents a Machine Learning model that uses residual factors from the FamaFrench threefactor model to identify significant alpha factors, providing significant alpha return even when style factors are controlled.","featured":"2023-08-09","label":"SSRN","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"ML Control for Counterfactual Forecasting","url":"/papers/ssrn/4522825/","summary":"The Machine Learning Control Method (MLCM) is a new tool for analyzing causal effects without a control group, recently used to study COVID-19's impact on income inequality in Italy.","featured":"2023-08-02","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Overnight GARCH-Itô Models","url":"/papers/ssrn/4523600/","summary":"The paper presents a unified factor overnight GARCH-Itô Models model for estimating and predicting large volatility matrices, suggesting a weighted least squares estimation procedure with a nonparametric factor volatility estimator.","featured":"2023-08-02","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Clustering Zero-Inflated Time Series","url":"/papers/repec/spr-jclass-v-40-y-2023-i-2-d-10-1007-s00357-023-09437-z/","summary":"A novel clustering method for high-dimensional zero-inflated time series data has been developed, utilizing a modified thick-pen transform and an efficient iterative clustering algorithm, proven effective through simulations and real datasets.","featured":"2023-08-02","label":"RePEc","topic":"Econometrics & Forecasting","cites":null,"score":15,"scale":"shares"},{"title":"Modeling Shanghai Composite Index Opening Spread","url":"/papers/ssrn/4516288/","summary":"The research uses hybrid models to predict the opening price difference rate of the Shanghai Stock Exchange Composite Index, suggesting implications for stock market forecasting and investment decisions.","featured":"2023-07-26","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Augmented HAR","url":"/papers/ssrn/4516177/","summary":"The Augmented HAR algorithm, combined with artificial neural networks, enhances forecast accuracy for stocks with less than seven years of data.","featured":"2023-07-26","label":"SSRN","topic":"Econometrics & Forecasting","cites":6,"score":2,"scale":"shares"},{"title":"Machine Learning in Mortality Forecasting","url":"/papers/ssrn/4521377/","summary":"The article introduces a new machine learning approach for long-term mortality forecasting, enhancing prediction accuracy and addressing the issue of diminishing patterns in long-term forecasts.","featured":"2023-07-26","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Sig-Splines: Time Series Generative Models Calibration","url":"/papers/ssrn/4514421/","summary":"Time Series Generative Models Calibration: A new model for analyzing multivariate time series data is proposed, using linear transformations and signature transforms instead of traditional neural networks, adding convexity to the model's parameters.","featured":"2023-07-19","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":46,"scale":"shares"},{"title":"GANs and Synthetic Financial Data: VaR Calculation","url":"/papers/ssrn/4512017/","summary":"VaR Calculation: The article discusses the unique characteristics of financial data time series developed using a Generative Adversarial Neural net (GAN), emphasizing its applications in machine learning.","featured":"2023-07-19","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Big Data Forecasting in SCM","url":"/papers/ssrn/4515014/","summary":"The article introduces a new framework for supply chain forecasting strategies and technologies, using Big Data Analytics for optimization and performance assessment.","featured":"2023-07-19","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":3,"scale":"shares"},{"title":"Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors","url":"/papers/arxiv/2307.07689/","summary":"A new dynamic forecasting method using supervised Principal Component Analysis (PCA) has been introduced, which is more effective in predicting U.S. macroeconomic variables.","featured":"2023-07-19","label":"arXiv","topic":"Econometrics & Forecasting","cites":16,"score":5,"scale":"shares"},{"title":"Unsupervised ML in Financial Time-Series Analysis","url":"/papers/ssrn/4503933/","summary":"The study merges ontological methodology and temporal clustering to detect structural changes and crucial periods in financial time series, building on prior research in commodity markets.","featured":"2023-07-12","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"Forecasting Financial Risk with Quantile RF","url":"/papers/ssrn/4504950/","summary":"The study introduces a financial risk forecasting model using Generalized Quantile Random Forests, which offers competitive risk and shortfall forecasts and generates appealing Sharpe, Sortino, and Omega ratios.","featured":"2023-07-12","label":"SSRN","topic":"Econometrics & Forecasting","cites":null,"score":2,"scale":"shares"},{"title":"A causal interactions indicator between two time series using extreme variations in the first eigenvalue of lagged correlation matrices","url":"/papers/arxiv/2307.04953/","summary":"The article discusses a method to examine the relationship between time variables in daily monetary flows in retail brokerage using the first eigenvalue distribution of lagged correlation matrices.","featured":"2023-07-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":2,"scale":"shares"},{"title":"Forecasting financial markets with semantic network analysis in the COVID-19 crisis","url":"/papers/doi/10-1002-for-2936/","summary":"A novel textual data index has been utilized to forecast Italian stock and bond market returns and volatilities, showing significant predictability, especially for bond market data during the COVID-19 crisis.","featured":"2023-07-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":20,"score":46,"scale":"shares"},{"title":"Expert Aggregation for Financial Forecasting","url":"/papers/arxiv/2111.15365/","summary":"The Bernstein Online Aggregation procedure merges predictions from various machine learning models to enhance portfolio performance, surpassing individual algorithms and providing a superior portfolio Sharpe Ratio.","featured":"2023-07-12","label":"arXiv","topic":"Econometrics & Forecasting","cites":12,"score":40,"scale":"shares"},{"title":"Statistical electricity price forecasting: A structural approach","url":"/papers/arxiv/2306.14186/","summary":"Encoding domain knowledge improves electricity price forecasting accuracy.","featured":"2023-06-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":0,"score":3,"scale":"shares"},{"title":"Data-Driven Risk Measurement by SV-GARCH-EVT Model","url":"/papers/arxiv/2201.09434/","summary":"A new financial risk measurement model that considers fat-tailed distribution and leverage effect performs better than other models in capturing financial return characteristics.","featured":"2023-06-28","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":33,"scale":"shares"},{"title":"The self-exciting nature of the bid-ask spread dynamics","url":"/papers/arxiv/2303.02038/","summary":"A new State-dependent Spread Hawkes model has been proposed to forecast spread values in financial securities, incorporating the impact of the current spread state on its intensity functions.","featured":"2023-06-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":32,"scale":"shares"},{"title":"Measuring Consistency in Text-based Financial Forecasting Models","url":"/papers/arxiv/2305.08524/","summary":"FinTrust proposes a tool to improve consistency in financial text for forecasting.","featured":"2023-06-07","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":18,"scale":"shares"},{"title":"Modeling and evaluating conditional quantile dynamics in VaR forecasts","url":"/papers/arxiv/2305.20067/","summary":"Simulation shows improved time-varying VaR models for forecasting conditional quantiles.","featured":"2023-06-01","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"},{"title":"Generalized Autoregressive Score Trees and Forests","url":"/papers/arxiv/2305.18991/","summary":"Proposal to improve GAS model forecasts by localizing parameters using decision trees and random forests, outperforming baseline model in empirical analyses.","featured":"2023-06-01","label":"arXiv","topic":"Econometrics & Forecasting","cites":5,"score":3,"scale":"shares"},{"title":"The Federal Reserve’s Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis","url":"/papers/arxiv/2305.12318/","summary":"Quantitative easing positively affected US GDP but did not impact inflation.","featured":"2023-05-24","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"},{"title":"Deformation of Marchenko–Pastur Distribution for the Correlated Time Series","url":"/papers/arxiv/2305.12632/","summary":"Study of eigenvalue distribution of Wishart matrix with temporal correlation.","featured":"2023-05-24","label":"arXiv","topic":"Econometrics & Forecasting","cites":1,"score":2,"scale":"shares"}],"per_quarter":{"2023 Q2":8,"2023 Q3":33,"2023 Q4":21,"2024 Q1":30,"2024 Q2":34,"2024 Q3":36,"2024 Q4":32,"2025 Q1":33,"2025 Q2":40,"2025 Q3":12,"2025 Q4":7,"2026 Q1":3,"2026 Q2":0,"2026 Q3":8}}