Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links featuring the latest research on quant investing and useful resources. Below, you'll find a curated list, with each title linking to the source for more information. Thank you for reading!
Betting
A Comparison between Financial and Gambling Markets (Liu et al.)
This paper explores the parallels between financial and betting markets and their strategies, revealing many common characteristics.
Commodities
Performance of Commodity Futures-Based Dynamic Portfolios (Adhikari)
This study evaluates various commodity investment strategies, finding that some tactical approaches may offer better returns than traditional buy-and-hold methods over time.
A hidden Markov model for statistical arbitrage in international crude oil futures markets (Fanelli et al.)
The authors find that statistical arbitrage strategies on Brent, WTI, and Shanghai crude futures are profitable after costs.
Crypto
Sentiment in the Cross Section of Cryptocurrency Returns (John et al.)
Investor sentiment is shown to be priced in the cross-section of crypto returns and is proven useful for forming trading strategies.
Equities
A Unified Framework for Value and Momentum (Boudoukh et al.)
This study proposes a unified explanation for the momentum and value effects by linking them to fundamental valuation principles.
Enhancing the High-Volume Return Premium (Kang)
The author explores trading strategies around abnormal volume and finds significant improvements to previously proposed strategies.
Optimal Factor Timing in a High-Dimensional Setting (Lehnherr et al.)
The authors explore timing of equity factors using a large set of signals and find meaningful improvements in Sharpe ratios after costs.
Limits to Diversification: Passive Investing and Market Risk (Fang et al.)
The growing popularity of index-based investing may inadvertently increase market-wide volatility and reduce diversification benefits for investors.
An Investigation of Multi-factor Asset Pricing Models in the UK (Tharyan et al.)
The authors construct asset-pricing factors and test portfolios for the UK market and provide data, finding the q-factor model to outperform in spanning tests.
Interday Cross-Sectional Momentum: Global Evidence and Determinants (Schlie and Zhou)
This paper documents short-term predictability across international equity markets that could provide value to investors, although being sensitive to transaction costs.
Insider trading (Balogh)
This study presents a comprehensive and transparent dataset on insider trading activities, addressing limitations in existing commercial databases and enabling new research opportunities.
A Factor Model of Company Relative Valuation (Wu et al.)
A linear factor model based on company valuations is presented, where mispricings around the model present possible investment opportunities.
Discount Rate Revision and the Mispricing Factor Zoo (Yang and Chen)
This study examines stock market anomalies, distinguishing between rational risk factors and mispricing factors.
What Drives Anomaly Decay? (Brogaard et al.)
The authors explore why the profitability of equity anomalies declines over time, finding that liquidity plays a key role.
ESG
Sustainable Investing: Evidence From the Field (Edmans et al.)
This study reveals that professional investors consider environmental and social factors in their decisions, but few seem willing to give up returns for environmental or social concerns.
The ESG Disclosure Premium (Gao et al.)
Sharing information about environmental and social practices benefits companies through a lower cost of capital.
Machine Learning and Large Language Models
What drives stock returns across countries? Insights from machine learning models (Cakici and Zaremba)
This study examines stock return predictability across international markets using machine learning, revealing significant predictability that, however, might be difficult for investors to harvest.
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts (Chen et al.)
AI-powered stock forecasts are shown to mirror human biases, showing both promise and limitations in financial decision-making.
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models (Suh and Cheng)
This comprehensive survey explores the statistical foundations of deep learning.
Variable Importance Measures for Multivariate Random Forests (Sikdar et al.)
This study proposes new ways to assess feature importance in multivariate random forests, improving predictive accuracy across applications.
Data Science Principles for Interpretable and Explainable AI (Sankaran)
This paper reviews the literature on AI models and interpretability, providing a rich reference list for further reading.
Market Stress Detection Through Autoencoder's Deep Latent Factors and Random Matrix Theory (Qyrana et al.)
This paper introduces a novel approach for measuring potential turmoil in markets, providing value for market timing.
Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching (Bie et al.)
The authors propose a regime-switching yield curve model that offers benefits compared to traditional models and uncovers state-dependent predictability.
Deep Gamma Hedging (Armstrong and Tatlow)
Gamma hedging using deep learning is found to offer advantages, in particular when model uncertainty is high.
Options
Some Anonymous Options Trades Are More Equal than Others (Huang et al.)
This study reveals significant disparities in how retail investors' options trades are executed, with pricing closely tied to broker incentives.
GitHub
Medium
Top 10 Python Libraries for Quants in 2024 (Ruiz)
How I Automated My Entire Morning Routine with Python (Araujo)
Using Hidden Markov Models (HMMs) for Volatility Classification & Price Forecasting in FinTech (Alexzap)
Applications of Rolling Windows for Time Series, with Python (Paialunga)
Podcasts
Nassim Taleb — Meditations on Extremistan (Joseph Noel Walker)
Algo Anxiety… Can AI Resolve This? ft. Mark Rzepczynski (Top Traders Unplugged)
What Regular Investors Need to Know About Options Flows | Brent Kochuba (Excess Returns)
Ariel Investments' John Rogers on How You Can Still Win With Value Investing (Odd Lots)
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