Generative ML for Multivariate Equity Returns
The study uses machine learning techniques to model the returns of S&P 500 equities.
9 shares8 citations todaySource ↗
Quant LetterNo. 27
November 2023, Week 5
86 items across 9 sections, as sent to readers on 29 November 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
- Items
- 86
- Papers
- 52
- Sections
- 9
- Issue
- No. 27
arXiv
Quantitative-finance and ML-for-finance preprints from arXiv.
19 items
Finance7
High Order Universal Portfolios
The research explores the properties of the Cover universal portfolio and its enhancements as a new synthetic asset.
7 sharesSource ↗
Optimal Tracking Portfolio in Incomplete Markets
The paper uses capital injection to solve an optimal tracking portfolio problem in incomplete market models, showcasing the q-learning algorithm's performance.
6 shares12 citations todaySource ↗
Narratives from GPT Networks of News
The study uses natural language processing and network analysis to examine news content over time, linking the results to financial market dislocations.
6 shares5 citations todaySource ↗
FinMe: Performance-Enhanced Large Language Model Trading Agent
Performance-Enhanced Large Language Model Trading Agent: The research presents FinMe, a Large Language Model-based agent for financial decision-making, demonstrating its superior trading performance in stocks and funds.
6 shares253 citations todaySource ↗
Machine Learning for Path-Dependent Contracts
The paper presents a comparison of machine learning algorithms for pricing financial products with early-termination features and introduces a new method for calculating sensitivities.
4 shares1 citation todaySource ↗
Enhanced Data Generation for Asset Allocation
The study presents a method for creating synthetic datasets to evaluate asset allocation methods and build portfolios within the fixed income universe.
4 shares1 citation todaySource ↗
Miscellaneous3
Approaches for Control Tasks in Curriculum and Imitation Learning
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.
5 shares5 citations todaySource ↗
Hilbert Transforms for PDEs with Mixed Boundary Conditions: Exact Solutions
Exact Solutions: The research introduces new techniques for solving specific boundary value problems in linear parabolic Partial Differential Equations using odd and even Hilbert transforms.
4 sharesSource ↗
Exploring Analytically Tractable Trial Functions: Quantum-inspired Nonlinear Galerkin Ansatz for High-dimensional PDEs
Quantum-inspired Nonlinear Galerkin Ansatz for High-dimensional PDEs: The research investigates the use of Neural Galerkin methods in solving Hamilton-Jacobi-Bellman partial differential equations, offering trial functions with solvable evolution equations.
4 sharesSource ↗
Crypto & Blockchain4
Cryptocurrency Price Forecasting with Deep Learning
The study uses Machine Learning and Natural Language Processing to predict Bitcoin and Ethereum prices using Twitter and Reddit data, improving forecasting accuracy.
68 shares27 citations todaySource ↗
Deep State-Space Model for Crypto Price Prediction
The research introduces a deep state-space model for predicting cryptocurrency prices, which is more accurate than current and traditional dynamical models.
8 sharesSource ↗
Process Mining and Sequence Clustering Application
The paper uses process mining to address bottlenecks in industrial weaving processes, improving production flow, worker performance, product quality, and lead time.
4 sharesSource ↗
Heuristics for CoinJoin Transaction Detection on Bitcoin Blockchain
The research explores CoinJoin, a method that combines multiple transactions into one for increased privacy in Bitcoin transactions, and develops new ways to identify CoinJoin transactions on the blockchain.
4 shares14 citations todaySource ↗
Historical Trending5
Handling Missing Values in ML Portfolios
The study shows that using cross-sectional means for simple imputation is effective in dealing with missing values in machine learning-constructed portfolios, as complex imputations can cause underperformance due to estimation noise.
41 shares35 citations todaySource ↗
Comparison of RL and Deep Trajectory-Based Hedging
The research compares the effectiveness of Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control as data-driven hedging strategies in a simulated environment, offering guidelines for creating autonomous hedging agents.
35 shares5 citations todaySource ↗
StockEmotions: Investor Sentiment
Investor Sentiment: The article introduces StockEmotions, a new dataset for detecting emotions in the stock market from StockTwits, with DistilBERT and Temporal Attention LSTM model showing the best results.
17 shares21 citations todaySource ↗
Online Utility-Based Shortfall Risk Estimation and Optimization
The paper proposes a stochastic gradient descent based algorithm for Utility-Based Shortfall Risk optimization, providing non-asymptotic bounds on its convergence, using stochastic approximation-based estimations.
14 shares7 citations todaySource ↗
Theory and Stock Return Predictions
The research suggests that the predictability of cross-sectional return predictors decreases by half in post-sample scenarios, indicating that theory doesn't improve prediction and peer-review often misinterprets mispricing as risk.
48 shares5 citations todaySource ↗
SSRN
Working papers in finance and economics from SSRN.
18 items
Quantitative11
Learning Algorithms and Spoofing
The paper presents a model to test if a trading algorithm can manipulate the limit order book, concluding that market conditions can allow such manipulation.
18 shares4 citations todaySource ↗
Machine Learning for Asset Management
Machine Learning for Asset Management discusses the use of machine learning in finance, its history, and its applications in asset management.
6 sharesSource ↗
Machine Learning for Path-Dependent Contracts
The study introduces machine learning algorithms for pricing certain financial products and a new method for calculating sensitivities using Chebyshev interpolation techniques.
5 sharesSource ↗
Reinforcement Learning for Quadratic Hedging
The study compares Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control in hedging a European call option under different market conditions.
3 shares1 citation todaySource ↗
Shared Causal Manifolds for Risk Management
A finance webinar presented a machine learning-based framework for optimizing portfolio sensitivities and predicting future positions.
2 sharesSource ↗
Data-Driven Investment Strategies for Large-Cap Universe
A deep learning architecture has been used to create advanced investment strategies for US stocks, outperforming the S&P 500 index.
3 sharesSource ↗
Asset Returns in India: Historical Survey 1992-2023
Historical Survey 1992-2023: The paper examines the historical returns of different asset classes in India from 1992 to 2023, highlighting the need to understand dynamic correlations among these assets for effective portfolio management.
12 sharesSource ↗
Multi-Task Learning in Financial NLP
Improvements in Financial NLP's Multitask Learning can be achieved by considering skill diversity, task relatedness, and aggregation size.
50 sharesSource ↗
Firm Fixed Effects Models Revamped with Machine Learning
Using firm fixed effects in corporate finance research may negate the impact of persistent economic factors, but advanced machine learning can provide alternative insights.
2 sharesSource ↗
Dynamic Portfolio Choice with Transaction Costs using Machine Learning
A new computational framework is introduced for solving dynamic portfolio choice problems, using Gaussian process regression and Bayesian active learning, suggesting that more assets can mitigate some illiquidity.
2 sharesSource ↗
AI Trader: Reinforcement Learning for Portfolio Management
Reinforcement Learning for Portfolio Management: AI Trader, a model based on reinforcement learning, shows superior risk-gain performance in the Chinese market by incorporating industry effects.
2 sharesSource ↗
Financial7
Epistemic Limits of Empirical Finance: Causal Reductionism
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.
31 sharesSource ↗
Mispricing and Factor Models
The paper introduces a new measure of expected mispricing at the firm level using machine learning, which outperforms existing methods in predicting future mispricing.
9 shares1 citation todaySource ↗
Disagreement of Disagreement: Investor Disagreement Proxies Assessment
Investor Disagreement Proxies Assessment: The study introduces a new comprehensive framework to measure investor disagreement, unveiling a unique nonlinear composite measure that predicts returns better than existing measures.
39 shares2 citations todaySource ↗
Stochastic Volatility Models for Derivative Valuation
The article explores the use of stochastic volatility models in valuing derivative securities, highlighting the effectiveness of affine Heston and lognormal models.
6 shares2 citations todaySource ↗
Asset Pricing - Deep Structural Model
The paper introduces a new modelling framework using machine learning to estimate structural model parameters, showing its superior predictive power and its ability to offer insights into systematic risk compensation and firm leverage.
2 sharesSource ↗
Insider Trading Detection with Big Data and Machine Learning
A machine learning approach using account-level data can effectively identify suspicious insider trading, with these insiders earning higher returns and often using multiple accounts to trade around major information events.
2 sharesSource ↗
Frictions and Constraints in Intermediary Markets
Arbitrage activity in various markets is affected by funding and balance sheet segmentation, making it sensitive to localized funding and individual balance sheet shocks.
777 sharesSource ↗
RePEc
Economics working papers from RePEc's NEP field reports.
7 items
Finance4
Optimization models for liquidity-constrained index tracking
The article discusses two models for integrating liquidity constraints in index tracking portfolio optimization, revealing higher liquidity and tracking errors in such portfolios.
34 sharesSource ↗
Factor Models in Bond Portfolios
The chapter highlights the use of factor models in understanding bond portfolio risk and return, stressing the importance of model specification.
16 sharesSource ↗
Yield Curve Attribution for Global Bonds
The chapter outlines a yield curve-based method for attributing performance in global bond portfolios, emphasizing the need for accurate data and pricing.
16 sharesSource ↗
Australian superannuation fund asset allocation
The paper analyzes the asset class switching behavior of Australian superannuation funds using a Markov Regime Switching framework, indicating smaller funds are more aggressive and larger ones are more conservative.
25 sharesSource ↗
Machine Learning3
Real Estate Appraisals: ML vs Traditional Methods
ML vs Traditional Methods: Research indicates that XGBoost, a machine learning method, offers the most precise estimates in automated property valuations, suggesting a need for regulators to use multiple methods.
31 sharesSource ↗
Bitcoin Futures Forecasting with ML
Machine learning algorithms have proven to be more effective than traditional models in predicting Bitcoin futures prices, maintaining an average accuracy rate above 50%.
18 sharesSource ↗
Sentiment Difficulty in ABSA
A study investigates sentence difficulty in aspect-based sentiment analysis, using different learning models and text representations, and identifies the hardest sentences using a mix of classifiers.
27 sharesSource ↗
Machine learning
The general machine-learning papers the letter carried in 2023-25.
8 items
Recently Published4
More is Better: Optimal Overparameterization
Optimal Overparameterization: The paper offers theoretical support for the idea that larger models, more data, and increased computation enhance performance in random feature regression, a type of model similar to shallow networks.
66 shares23 citations todaySource ↗
σPCA: Unified Neural Model for PCA
Unified Neural Model for PCA: The research proposes a unified neural model for PCA as single-layer autoencoders, capable of learning a semi-orthogonal transformation that reduces dimensionality and orders by variances, without rotational indeterminacy.
16 sharesSource ↗
Optimality in Mean Estimation
The research delves into the mean estimation problem, concluding that no estimator can surpass the sub-Gaussian error rate for any distribution.
21 shares9 citations todaySource ↗
Efficient High-Dimensional Bandit Learning
The study presents a sample-efficient algorithm for high-dimensional multi-armed contextual bandits with batched feedback, achieving regret bounds similar to those in fully sequential settings with fewer batches.
17 shares3 citations todaySource ↗
Historical Trending4
Generative Diffusion Models: Memory Mechanisms
Memory Mechanisms: Research indicates that generative diffusion models, a machine learning method, can be seen as energy-based models and can help understand how long-term memory is formed, connecting creativity and memory recall.
703 shares66 citations todaySource ↗
Banach-Tarski Embeddings: Interpretable Transformers
Interpretable Transformers: A novel method for embedding recursive data structures into high-dimensional vectors has been developed, offering an interpretable model for transformer's latent state vectors and enabling computations without decoding.
79 sharesSource ↗
VeriCompress: Streamlining Verified Robust Compressed Neural Networks
Streamlining Verified Robust Compressed Neural Networks: VeriCompress, a new tool that automates the search and training of compressed models with robustness guarantees, has been launched, providing faster training, improved accuracy, and reduced memory and inference time for deployment on resource-limited platforms.
22 sharesSource ↗
Topological Properties of Basins in Width Bounded Neural Networks
A study has shown that autoencoders trained with standard SGD methods create bounded basins of attraction around their training data, answering a previous question and providing insight into why certain neural network functions are not dense in continuous function spaces.
22 sharesSource ↗
Papers with code
Papers that shipped their code, from the Papers with Code feed (2023-25).
5 items
Trending5
Enhancing Diffusion Model Sample Quality
Denoising diffusion models (DDMs) are becoming increasingly popular due to their high-quality and diverse generation capabilities.
16,676 shares
Filtering-based Attention for NLP
The article presents Localized Filtering-based Attention (LFA), a method that incorporates local language dependencies into Attention, and provides the corresponding code on GitHub.
307 shares
Realtime Neural Radiance Caching for Path Tracing
The article suggests a novel method for managing dynamic scenes in neural networks by adapting and training the radiance cache during rendering, rather than pretraining, and includes the code on GitHub.
172 shares
Accelerating DNN with Approximate Matrix Multiplication
The article explores the importance of MatMul in shifting from traditional High Performance Computing to deep learning.
146 shares
Learning to Learn
The model's inner loop is equivalent to linear attention or self-attention, based on the type of learner used.
100 shares
GitHub
Repositories the letter featured.
5 items
Finance5
Numerical Methods for Math Finance
The article provides lecture materials on numerical methods in mathematical finance.
21 shares
Koopa: Learning Time Series Dynamics
Learning Time Series Dynamics: The article announces the release of a new method for learning nonstationary time series dynamics, named Koopman Predictors.
83 shares
ML Code Implementation
The article explains machine learning algorithms mathematically and provides Python code examples.
1,459 shares
Friends Don't Let Friends Visualize Bad Data
The article discusses ineffective data visualization techniques and explains why they are not advisable.
3,197 shares
UnbiasedGBM: Gradient Boosting Decision Tree Repository
Gradient Boosting Decision Tree Repository: An unbiased feature importance is introduced in the new repository for Unbiased Gradient Boosting Decision Tree.
19 shares
Videos
Talks, lectures and tutorials.
5 items
Quantitative5
Spread Options Calibration
Prof. Matthew Dixon spoke about calibrating spread options using a seasonal commodity forward model at the first Thalesian Talk.
1 shares
Yield Farming Analysis
Dr. Thomas Li presented a mathematical model to analyze the economic dynamics of yield farming using onchain data from decentralized exchanges.
0 shares
Generative AI Potential
DeepMind is exploring how machine learning and generative AI can speed up software development and drive major transformations.
89 shares
Foreign Exchange Introduction
Saeed Amen discussed the role of foreign exchange as an asset class and the future of alternative data and machine learning in finance at the second Thalesian talk.
1 shares
Decision Tree: Splitting and Predictions
Splitting and Predictions: The video explains the process of decision trees in making predictions, emphasizing the need for a computer due to the high volume of calculations.
10 shares
X / Twitter
Posts from quant researchers on X.
9 items
Quantitative6
Deep Learning for Causal Inference
Bernard Koch of UCLA provides a tutorial on the integration of causal inference, econometrics, and machine learning, with a focus on neural networks.
7 shares
Machine Learning for Portfolio Returns
The use of machine learning in creating maximally predictable portfolios (MPP) greatly impacts return predictability, particularly in portfolios using a Kelly criterion style strategy.
5 shares
Empirical Bayes for Out-of-Sample Returns
A recent study introduces the use of empirical Bayes (EB) for analyzing out-of-sample returns in 70,000 long-short trading strategies.
1 shares
Options and Strategies: Recent Paper
Recent Paper: Article 1: The paper delves into 0DTE options and various strategies related to them.
1 shares
AI's Impact on Financial Markets
The article examines the revolutionary effects of Generative AI on financial markets and services, focusing on regulatory aspects of its implementation.
1 shares
Equity Returns: New Paper
New Paper: Article 3: The paper explores the predictability of signs in equity returns, proposing a long/short strategy based on future positive returns as a more efficient and safer alternative to the momentum strategy.
1 shares
Miscellaneous3
TSMixer: MLP for Time Series Forecasting
MLP for Time Series Forecasting: Article 1: Google Research has created TSMixer, a new time series forecasting tool using an all-MLP architecture, with Python code accessible for users.
1 shares
Decoding Intentions: AI and Signals
AI and Signals: Article 3: A recent AI policy paper explores the application of artificial intelligence in interpreting intentions and expensive signals.
0 shares
Code Generation Evaluation: LLMs
LLMs: Code Generation Evaluation LLMs is an analysis of the performance and effectiveness of code generation in language model systems.
0 shares
Threads from r/quant, r/algotrading and friends.
10 items
Quantitative5
Rising5
HFT vs Hedge Fund Compensation
30 shares
Fixed Income Job Outlook
27 shares
HFT Traders' Daily Routine
135 shares
Quant Workspace
68 shares