Popular Investing Research in 2024
Looking Back at a Year of Investing and Trading Research
As 2024 comes to an end, I’m excited to share a compilation of popular research papers from this year, chosen based on click rates in my weekly newsletters. These papers cover a variety of topics, including alternative data, commodities, equities, machine learning, trading, and more. Each title links to its source for more details. I hope you enjoy this curated list of insights and ideas!
Thank you for your continued support—don’t forget to hit the like button if you found this helpful. Wishing you a Happy New Year!
Alternative Data
Alternative Data in Active Asset Management (Green and Zhang)
The authors provide an overview of alternative data and discuss how nontraditional data sources provide unique insights and competitive advantages in active asset management.
Backtesting
The Three Types of Backtests (Joubert, Sestovic, Barziy, Distaso, and Lopez de Prado)
This paper discusses different forms of backtests and their pros and cons.
A discussion paper for possible approaches to building a statistically valid backtesting framework (Arakelian, Bolesta, Liu, Osterrieder, Poti, Schwendner, Sutiene, Vlah Jeric, and Weinberg)
The authors discuss potential considerations when setting up a backtesting framework and possible pitfalls when backtesting.
Commodities
Factor Momentum in Commodity Futures Markets (Jiang, Liu, and Qian)
This paper finds evidence of short-term factor momentum in commodity markets, suggesting that it might be possible to time commodity factors.
News Sentiment and Commodity Futures Investing (Chi, El-Jahel, and Vu)
A long-short commodity portfolio based on weekly news sentiment delivers Sharpe ratios similar to existing anomalies and a significant alpha.
How to Improve Commodity Momentum Using Intra-Market Correlation (Pauchlyová and Vojtko)
Applying a correlation filter to commodity momentum strategies increases Sharpe ratios significantly.
Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets (Fanelli)
This paper considers a statistical arbitrage portfolio involving WTI, Brent, and Dubai oil futures and finds meaningful out-of-sample performance net of costs.
Crypto
Microstructure and Market Dynamics in Crypto Markets (Easley, O´Hara, Yang, and Zhang)
The paper demonstrates that standard market microstructure metrics can predict cryptocurrency price dynamics, indicating market inefficiencies.
Trend-following Strategies for Crypto Investors (Le and Ruthbah)
The authors explore trend-following strategies for major cryptocurrencies.
A Trend Factor for the Cross-Section of Cryptocurrency Returns (Fieberg, Liedtke, Poddig, Walker, and Zaremba)
A new crypto factor is proposed that effectively predicts cryptocurrency returns, unexplained by existing predictors.
The Nature of the Beast: A Study of Crypto Volatility (Gosal, McMurran, and Ding)
This is a comprehensive study on the properties of volatility in crypto returns. It finds similarities to traditional markets but also patterns unique to crypto markets.
Currencies
Understanding the Performance of Currency Basis-Momentum (Fan, Han, Li, and Liu)
Currency basis momentum generates meaningful Sharpe ratios and statistically significant alphas when controlling for carry and momentum factors, although it is related to them.
Cointegration-Based Strategies in Forex Pairs Trading (Lemishko, Landi, and Caicedo-Llano)
This paper proposes an FX trading strategy using cointegration to identify profitable opportunities.
Equities
Same-Weekday Momentum (Da and Zhang)
A significant portion of stock momentum strategies is found to be explained by same-weekday momentum, attributed to seasonal patterns in fund flows and institutional trading.
Mosaics of Predictability (Cong, Feng, He, and Wang)
The paper introduces a new method for clustering assets to reveal patterns in return predictability, highlighting significant heterogeneity across different asset characteristics and economic conditions.
Narrative Momentum (Lee, Lou, Ozik, and Sadka)
A long-short portfolio based on changes in narrative intensities yields a strongly significant alpha, as investors underreact to changing narratives in media.
Price-Path Convexity and Short-Horizon Return Predictability (Gulen and Woeppel)
The shape of recent stock price movements can predict future short-term returns better than traditional indicators.
End-of-Day Reversal (Baltussen, Da, and Soebhag)
Stocks that have sold off sharply during the day are shown to reverse during the last 30 minutes of trading, largely attributed to increased retail dip buying and reduced selling pressure by short sellers towards the end of the day.
Formula Investing (Hanauer and Schwartz)
This study examines four well-known investing formulas over a 60-year period in the U.S. stock market, finding that they all generate significant returns, primarily by capturing established factor premiums.
Trended Momentum (Cai, Li, and Keasey)
Stocks with smoother price paths are more likely to experience a return continuation.
Intrinsic Value: A Solution to the Declining Performance of Value Strategies (Bergen, Franzoni, Obrycki, and Resendes)
The authors propose a new value measure that strongly outperforms traditional value measures such as book-to-market, yielding significant alphas.
Fixed Income
Fear in the "Fearless" Treasury Market (Wang, Wang, Zhang, and Zhou)
A fear index based on the Thomson Reuters (now LSEG) MarketPsych dataset strongly predicts Treasury bond returns, controlling for other factors.
Fixed-income Investing Primer (Dubofsky)
This comprehensive guide offers practical advice for individual investors on the world of fixed-income securities and investment strategies.
Machine Learning and Large Language Models
StockGPT: A GenAI Model for Stock Prediction and Trading (Mai)
The author introduces StockGPT which is trained directly on daily stock returns up to 2000 and tested out-of-sample over the period 2001-2023, finding it to be a significant predictor of returns.
Automate Strategy Finding with LLM in Quant investment (Kou, Yu, Peng, and Chen)
Ensembling across alpha sources mined through large language models is shown to generate robust performance.
Trading Volume Alpha (Goyenko, Kelly, Moskowitz, Su, and Zhang)
The paper explores how predicting the trading volume for individual stocks using machine learning can enhance portfolio performance by optimizing trading costs and tracking errors.
Advanced Statistical Arbitrage with Reinforcement Learning (Ning and Lee)
A model-free approach to statistical arbitrage based on reinforcement learning is proposed, outperforming other methods.
Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading? (Cho)
The study explores ChatGPT's ability to generate profitable day trading strategies by analyzing news data, highlighting its potential to identify mispriced stocks.
Financial Statement Analysis with Large Language Models (Kim, Muhn, and Nikolaev)
The authors feed financial statements to GPT-4 and find that it outperforms financial analysts in predicting the sign of earnings changes.
Quant Investing
James H. Simons, PhD: Using Mathematics to Make Money (Simons)
A discussion with Jim Simons on quant investing, how he runs his business, competition among quant firms, philanthropy, and much more.
A Comparison between Financial and Gambling Markets (Liu, Donovan, and Popov)
This paper explores the parallels between financial and betting markets and their strategies, revealing many common characteristics.
Regime Switching
Regime-Based Strategic Asset Allocation (Bouyé and Teiletche)
This paper explores a regime-based approach to strategic asset allocation by incorporating macro regimes into the portfolio construction process.
Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach (Shu, Yu, and Mulvey)
A market timing strategy using a statistical jump model to identify bull and bear markets outperforms using Markov-switching models, achieving higher Sharpe ratios and lower turnover.
Trading
A Profitable Day Trading Strategy For The U.S. Equity Market (Zarattini, Barbon, and Aziz)
This paper offers a comprehensive analysis of open-range breakout (ORB) strategies in U.S. stocks, finding that 5-minute ORB strategies are more profitable than those using longer time frames, with relative volume playing a pivotal role.
Market-neutral Carry Strategies: Harvesting Carry Without Market Risk (Tzotchev)
This paper describes how to construct market-neutral carry strategies across asset classes.
Chicken and Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX? (Hanna)
The author suggests using SPX signals to trade the VIX, instead of the other way around.
The Power Of Price Action Reading (Zarattini and Stamatoudis)
There might be benefits of combining discretionary and systematic trading, with the authors presenting a trading strategy around gaps.
I hope you enjoyed this year-end roundup! Let me know which paper was your favorite or if there’s a topic you’d like to see covered in 2025. Wishing you a great start to the new year!
Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse any specific securities or investments mentioned. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.
This content does not offer personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed without prior notification.
The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.
The brief summaries and descriptions of research papers and articles provided in this newsletter are the author's own interpretations of the findings and content. These summaries should not be considered as definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.
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