Quant LetterNo. 20
121 items across 10 sections, as sent to readers on 12 October 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
15 items
Risk Valuation: The article introduces a model that predicts daily electricity prices and average temperatures, offering a way to manage price and volume risks, and includes formulas for derivatives like quanto options.
10 shares4 citations todaySource ↗
Order Book Simulation: The paper expands a numerical method to simulate the spread of financial market orders, showing the price impact of flash limit-orders and market orders, and advocates for non-uniform sampling in diffusive dynamics simulations.
8 shares4 citations todaySource ↗
Stock Market Dynamics: The study reveals that market valuation duration is influenced by limited cash flow information beyond a year and negatively predicts annual market return, outperforming other predictors, suggesting valuation duration is a key variable that enhances the price-dividend ratio in stock-market dynamics.
6 sharesSource ↗
Wind Power Analytics Market: The article explores the creation of a market for wind power forecasting analytics that takes into account correlations, using a Shapley value-based policy for revenue allocation.
8 sharesSource ↗
No Effect on Labour Productivity: The paper finds no significant effect of AI adoption on labour productivity in UK businesses from 2015 to 2019, based on data from the UK Office for National Statistics.
4 shares2 citations todaySource ↗
The paper suggests that China should develop a more effective monetary policy while promoting Central bank digital currencies, examining their impact on China's monetary policy and money supply multiplier.
4 shares2 citations todaySource ↗
Discontinuous Model Sensitivity Analysis: The article discusses a framework for understanding complex computational models, focusing on discontinuities and discrete input variables, and applies it to risk analysis of compound risk models and reinsurance credit risk.
10 shares5 citations todaySource ↗
Statistical Significance in Risk Assessment and Model Selection: The paper presents a framework for evaluating socio-technical risks of foundation models, using a new statistical method and a risk-aware approach, and applies it to assess large language models for risks of deviating from instructions and producing harmful content.
7 shares4 citations todaySource ↗
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.
6 shares32 citations todaySource ↗
The study uses supermarket sales data to create a Food Consumer Price Index in China and uses machine learning to predict CPI growth rate, performing better than traditional models.
11 shares1 citation todaySource ↗
The research suggests that diversifying super-Pareto losses increases portfolio risk, discouraging risk sharing in market equilibrium.
170 shares26 citations todaySource ↗
The study uses game theory and deep multi-agent reinforcement learning to create models that match market prices of specific options, aiding in understanding local volatility and path-dependence.
68 shares1 citation todaySource ↗
The article presents a new, efficient method for pricing European multi-asset options that is faster than existing methods.
28 shares16 citations todaySource ↗
The paper introduces a new simulation-based method for estimating functionals of a conditional expectation, which helps overcome the issue of dimensionality.
16 shares9 citations todaySource ↗
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.
43 shares1 citation todaySource ↗
Working papers in finance and economics from SSRN.
31 items
The thesis shows that deep reinforcement learning (DRL) offers a new approach for portfolio allocation, enhancing existing methods by better adapting to market conditions.
2 shares3 citations todaySource ↗
Micro-Level Data Approach: The paper introduces a microeconometric approach to inflation forecasting using individual stock prices and machine learning, which is more accurate than traditional aggregate data methods.
3 sharesSource ↗
The study suggests that previous research significantly overestimates the diminishing returns of mutual funds when considering additional institutional assets managed under the same strategy.
3 shares1 citation todaySource ↗
The article discusses the conflict between the use of complex machine learning algorithms in administrative decisions and the need for explanation in administrative law, hinting at possible systemic impacts.
2 sharesSource ↗
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.
6 sharesSource ↗
A new machine learning method is developed to tackle investor irrationality in copy trading, enhancing performance by integrating sources of irrationality into the algorithm.
2 sharesSource ↗
The paper presents a new procurement policy for data-driven commodity purchasing, combining operational and financial instruments, and tests the policy on real market data of four major commodities.
2 sharesSource ↗
The study introduces a framework for using machine learning in financial statement fraud literature analysis, utilizing bibliometric analysis techniques and topic modeling.
3 sharesSource ↗
The tutorial explains how to use machine learning on encrypted data, which is beneficial for managing sensitive personal data under regulations like GDPR.
5 sharesSource ↗
Predictive Power: The study reveals that Natural Language Processing can predict short-term interest rate decisions based on the communication strategy of Banco de México.
3 shares2 citations todaySource ↗
The study finds a correlation between credit risk and Environmental Social and Governance (ESG) factors using Supervised Machine Learning techniques.
2 sharesSource ↗
MF-DMA and HAR-RV: The paper introduces a model that uses global trading information of SP 500 Index to predict VIX index return, emphasizing the need for analyzing high-frequency price data globally.
2 sharesSource ↗
The paper highlights the intraday variation of market beta in the Chinese stock market, noting that growth stocks and stocks with higher leverage or more research coverage have more significant intraday beta variation.
2 sharesSource ↗
The study analyzes the effect of patent portfolio traits on market-perceived valuation uncertainty, finding that a larger market value increases uncertainty, while more patents and lower value dispersion decrease it.
2 sharesSource ↗
The research investigates volatility risk premiums in the swaption market, concluding that a strategy that takes on both volatility and jump risks yields a higher Sharpe ratio during periods of low interest rates.
3 sharesSource ↗
The research identifies the key factors influencing commodity futures liquidity, with idiosyncratic factors and excess hedging demand having the most impact, and well-funded liquidity providers able to lessen negative effects.
2 sharesSource ↗
The article highlights the growing popularity of collective investment trusts (CITs), an investment vehicle replacing mutual funds in employer-sponsored retirement plans.
2 sharesSource ↗
Stock Return Prediction: The article discusses how combining finance theories with big data can improve stock return predictions, with theory-guided models outperforming purely data-driven ones.
4 sharesSource ↗
The article introduces a comprehensive framework for portfolio optimization using a neural network, and compares three different problem-solving methods within this framework.
3 shares11 citations todaySource ↗
The article creates a global implied volatility surface using index options from 20 regions, and finds that the surface's convexity can predict global equity premia.
2 sharesSource ↗
The article offers a new approach to understanding the term structure of market equity yields, showing a positive aggregation effect when using aggregated equity yields of individual firms to estimate the market equity yield curve.
2 sharesSource ↗
The article investigates the corporate bond fund industry, concluding that while large funds stabilize the corporate bond market, they also increase the risk to underlying bonds in illiquid markets.
2 sharesSource ↗
Research shows that predicting returns of the reversal factor's long-short portfolio can yield significant returns in the Chinese stock market.
3 sharesSource ↗
The Federal Reserve's Secondary Market Corporate Credit Facility improved bond liquidity during COVID-19, but didn't greatly affect capital investment or employment costs.
2 sharesSource ↗
Fire Sales: A study indicates that ETFs can cause fire sales in illiquid assets, with authorized participants acting as a buffer to prevent such sales.
2 sharesSource ↗
A New Methodology: A new method measures the risk premium of higher-order cumulants using leveraged ETFs, showing significant differences across asset classes, especially during stressful times.
2 sharesSource ↗
SEC v. Coinbase: The SEC is taking action against Coinbase Inc. and Coinbase Global Inc., asserting its power to regulate investment contracts in new situations.
20 sharesSource ↗
The article introduces an algorithm that uses past transaction data to predict liquidity over a four-hour period, with the LSTM-based algorithm performing better than SARIMAX and TBATS algorithms in unusual situations.
2 sharesSource ↗
Stablecoins and money market mutual funds have similar reserve asset backing and market microstructure, but stablecoins show larger dispersions from the dollar peg and higher volatility.
2 sharesSource ↗
Digital assets offer higher average returns than equity and commodities, but are more susceptible to short-term external shocks, according to a study.
28 sharesSource ↗
The study suggests two semi-nonparametric GARCH models for more precise estimation of Bitcoin volatility dynamics, showing their superiority over traditional GARCH models.
12 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
17 items
A study uses deep learning and basic trading rules to create a quantitative trading strategy, testing its effectiveness with Chinese market data.
43 sharesSource ↗
Research examines the link between trader sentiment and market volatility in India during COVID-19, suggesting new options trading strategies considering traders' neuro-specific intentions.
32 sharesSource ↗
A study reveals that algorithmic traders profit while non-algorithmic traders lose, with market volatility causing contrasting trading behaviors.
29 sharesSource ↗
Dynamic Nelson-Siegel Approach: The research assesses the Dynamic Nelson–Siegel approach to modeling volatility smile, demonstrating its superior performance over static models in mean returns and Sharpe ratio.
19 sharesSource ↗
The paper presents a new f-Beta for portfolio optimization, comparing its performance with Standard Beta and Drawdown Betas using selected stocks against the S&P 500 market index.
18 sharesSource ↗
The study explores the use of artificial intelligence in predicting stock market trends in volatile situations, showing its superior performance over linear regression, particularly after the Russian-Ukrainian war.
17 sharesSource ↗
A study successfully used machine learning, specifically the XGBoost model, to predict gender diversity on the boards of Chinese publicly-traded companies.
22 sharesSource ↗
A proposed XGBoost-based framework for detecting fraud in mobile transactions was found to be most effective when combined with multiple unsupervised outlier detection algorithms.
17 sharesSource ↗
A comparison of machine learning methods for predicting English reading comprehension test difficulty found that elastic net was best for continuous prediction, while random forests excelled in classification tasks.
17 sharesSource ↗
The article discusses a study that introduces a new volatility forecasting model for global market indices. This model uses a spatial-temporal graph neural network (GNN) and performs better than existing models in short- and mid-term forecasting, potentially leading to economic benefits for investors.
20 sharesSource ↗
Panel data approach: Machine learning methods using panel data analysis are more effective in predicting volatility than traditional linear models, according to a study.
45 sharesSource ↗
Algorithmic investment strategies using machine learning models perform better than passive strategies, with Linear Support Vector Machine and Bayesian Generalized Linear Model being the most effective, research shows.
31 sharesSource ↗
The article uses machine learning to study the effect of free trade agreement policies on trade flows, concluding that more detailed agreements have a greater impact.
22 sharesSource ↗
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.
26 sharesSource ↗
The study applies machine learning to predict stock market returns based on company traits, with results varying depending on company size, recent data availability, and market-specific elements.
19 sharesSource ↗
The research examines the role of cross-market information flow on China's stock market volatility, finding it significantly aids in medium and long-term forecasts but not short-term volatility.
18 sharesSource ↗
The article employs machine learning to find the best ReTakaful contributions model for Morocco's Islamic insurance sector, showcasing the algorithms' potential in calculating appropriate contributions.
18 sharesSource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
12 items
NextGen LLM Applications with MultiAgent Conversation: AutoGen is an open-source platform designed for creating LLM applications that allow multiple agents to work together to accomplish tasks.
6,785 shares
Self-Improving Pipelines for Language Model Calls: The machine learning community is exploring techniques for prompting language models and layering them to tackle complex tasks.
3,401 shares
Automatic Agent Generation: AutoAgents is a new system that creates and manages multiple AI agents to form a team for different tasks.
388 shares
A breakthrough in decoding perceived speech from noninvasive recordings could allow interpretation of language from brain activity without surgery.
138 shares
Space and Time Representation: The capabilities of large language models (LLMs) have led to debates on whether they simply learn superficial statistics or a comprehensive model of the data generating process.
121 shares
Math Word Problem Solving: To solve math word problems, accurate reasoning about quantities in the text and reliable equation generation are required.
105 shares
A Comparative Analysis: The first article analyzes the comparison between feedback from GPT4 and human reviewers in scientific publications.
72 shares
The second article delves into representation engineering, a field aiming to enhance AI transparency through cognitive neuroscience insights.
61 shares
The third article highlights the shortcomings of large language models in decision-making tasks and their lack of autonomy.
60 shares
Large Language Models (LLMs) have improved roleplaying tasks by enabling models to imitate various characters.
55 shares
Researchers are investigating the possibility of creating AI research agents capable of performing long-term tasks.
50 shares
Past efforts to use specific information have failed because even the largest models were unable to follow the guidelines.
47 shares
Repositories the letter featured.
9 items
Python's Time Series Toolkit: The toolkit provides various tools for conducting time series analysis in Python.
100 shares
Neural Networks for Time Series Forecasting: The repository offers resources for learning and creating sequence-to-sequence neural networks for time series forecasting using Keras and TensorFlow.
590 shares
Maximizing Sharpe Ratio for Autotrading: The capstone project introduces a deep policy gradient algorithm for automated trading by optimizing the Sharpe ratio.
7 shares
Python Notebook for Data Exploration and App Deployment: The advanced Python notebook enables users to analyze data, develop tools, and launch applications.
115 shares
Reference Environments for Reinforcement Learning: The collection provides benchmark environments for conducting offline reinforcement learning.
1,008 shares
Embedded Vector Search Engine: The article provides a comprehensive review of JVector, a sophisticated embedded vector search engine.
822 shares
3D Content Creation: The article delves into the use of Generative Gaussian Splatting as a method for efficient 3D content creation.
1,725 shares
VSCode Knowledge Management: The article introduces a new system for managing and sharing personal knowledge within the VSCode environment.
14,184 shares
Easy AI Artwork Creation: The article showcases an AI-powered tool that generates artwork from text prompts and images on personal computers.
7,925 shares
Industry news: funds, hiring, markets and regulation.
3 items
Bloomberg has launched Portfolio Manager Workspace, a real-time tool that combines portfolio data with industry information, risk analytics, and liquidity insights.
7 shares
Macro hedge funds saw significant profits in September due to increased volatility, while stocks and bonds suffered major losses, as per HFR data.
6 shares
The inconsistency in liquidity is posing challenges to systematic credit risk management.
4 shares
Episodes on markets, quant methods and economics.
8 items
Inflation and the Macroeconomy: Rob Arnott, founder of Research Affiliates, talks about inflation, macroeconomics, and index investing at the Economic Club of Miami.
3 shares
Doug Moglia predicts a potential 2021 recession due to the Federal Reserve's monetary policy tightening, suggesting Turkish stocks as a possible investment strategy.
16 shares
Will McBride and Dmitry Pargamanik explain the purpose of the five equity market trading sessions in a discussion with IBKR’s Jeff Praissman.
6 shares
Thilo Hagendorff discusses his research on the role of machine psychology in machine learning tasks and explores the potential limitations of machine learning.
4 shares
Scientific Investing and Macro Divergences: Marty Lueck, co-founder of Aspect Capital, highlights the significance of hypothesis testing, trend impacts on portfolios, and his role as Research Director in a podcast.
4 shares
Tokens and the Future of Money: Rachel O'Dwyer's book Tokens examines the emergence of digital currencies and the potential consequences of online platforms becoming new banks.
3 shares
Founder of Hirtle Callaghan & Co.: Jon Hirtle talks about the development of the OCIO model, changes in asset allocation, and shares his views on the current investment landscape.
4 shares
Market Impact Analysis: Interactive Brokers' strategists analyze the effects of recent employment reports and geopolitical events on equity and fixed income markets.
2 shares
Posts from quant and economics blogs and newsletters.
9 items
The article explores the use of mean reversion and momentum strategies together in arbitrage trading to maximize benefits.
13 shares
The article investigates the merging of mean reversion and momentum strategies in arbitrage trading for a more varied approach.
13 shares
The article outlines rules and provides an example for the optimal ADX trading strategy using the Average Directional Movement Index Indicator.
4 shares
The piece analyzes the Williams Percent Range and RSI Indicator systems within the context of the Williams R Trading Strategy.
2 shares
Groette's article offers an in-depth review of the Supertrend Indicator Trading Strategy, detailing its backtest settings, formula, and performance.
2 shares
The piece on quantifiedstrategies.com outlines the CCI Trading Strategy, including its backtest, indicator settings, rules, and an example.
2 shares
The Put Call Ratio Trading Strategy is examined on quantifiedstrategies.com, discussing its effectiveness, backtest rules, and settings.
2 shares
The article provides a comprehensive guide to the best trading indicators, detailing the most frequently used technical indicators and strategies.
2 shares
Quantifiedstrategies.com's article delves into the Parabolic SAR Trading Strategy, including its backtest.
2 shares
Posts from quant researchers on X.
10 items
Data Cleaning and Training with Python: Cleanlab is a Python library that automatically cleans and processes data sets, including outliers and duplicates.
3 shares
Industry and FamaFrench Factors: A study indicates that while short-term reversal effect in stock returns has lessened, reversals adjusted for industry and Fama-French factors are still significant.
3 shares
Impact on Equity Valuations: Financial Times reports that half of public companies are not profitable, which could lead to equity valuation problems and fiscal tightening.
1 shares
Skewness Risk Premium for High Returns: Research suggests that purchasing currencies with a high skewness risk premium and selling those with a low one can yield high returns and Sharpe ratio.
1 shares
Efficient Frontier Allocation and Cognitive Map: The Art Financialization Dissertation explores the efficient frontier allocation ecosystem cognitive map.
1 shares
The article introduces a new benchmark for timeseries forecasting and two new model architectures, pointing out issues with popular models like DeepAR LSTM.
0 shares
The article discusses the influence of the Wall Street Bets social media movement on the financial market.
0 shares
Vizro: McKinsey has launched Vizro, a new open-source Python library for data visualization, according to the article.
0 shares
The article presents a study showing that language models have an internal world model and a sense of time, as evidenced by a map found in Llama2's activations.
0 shares
The article examines the impact of the Bitcoin market, with a focus on limit order book (LoB) and intraday dynamics.
0 shares
Threads from r/quant, r/algotrading and friends.
7 items