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!
Derivatives
Peer Option Momentum (Li and Yang)
An option momentum strategy based on momentum in peer firms' straddle returns generates meaningful returns after transaction costs.
Back to the Seventies: How a Free Lunch Falsifies the Black-Scholes Option Pricing Formula (Mink and De Weert)
This paper challenges the validity of the Black-Scholes option pricing formula by demonstrating a potential arbitrage opportunity.
Forecasting Option Returns with News (Cao et al.)
Signals from newspaper articles, derived using machine learning, are shown to be significant predictors of delta-hedged option returns.
Equity
Presidential Cycles in PEAD (Wang and Zeng)
Returns of post-earnings announcement drift strategies differ significantly depending on whether a Republican or Democratic president holds office.
Forecasting Stock Returns with Sparse Scaled Pca (Xie and Zhang)
This paper introduces a variant of principal component analysis (PCA) that combines feature selection and scaling techniques, outperforming classical PCA and partial least squares.
Regret in Global Equity Markets (Atilgan et al.)
Stocks with high investor regret, measured as their short-term underperformance vs. similar firms, outperform over the next month.
Sentiment, Social Media, and Meme Stock Return Predictability (Li and Li)
The authors construct social-media sentiment indices that predict returns on meme stocks over different time horizons.
This paper explores various machine learning models to predict stock returns, finding the most promising results for tree-based models although not consistently outperforming buy-and-hold.
Exploiting Price Discrepancies: A Comprehensive Study of Leveraged ETF Arbitrage Between SPY and TQQQ (Li et al.)
The authors explore a short-term trading strategy between SPY and TQQQ, finding meaningful Sharpe ratios while being highly sensitive to transaction costs.
Retail Limit Orders (Anand et al.)
This study explores the usage of limit orders among retail investors, finding that patient investors can benefit from such orders.
The Zero-Beta Rate Revisited (Wang)
This paper examines issues estimating the zero-beta rate and proposes new tests to evaluate asset pricing factor models.
Fixed Income
Bond Pairs and the Term Structure (Diaz and Livingston)
A novel method for estimating zero-coupon interest rates is proposed, demonstrating improved accuracy over traditional yield curve models.
Hedge Funds and Mutual Funds
The Rise of Alternatives (Begenau et al.)
This study examines why some U.S. public pension funds have shifted more heavily into alternative investments than others since the early 2000s.
Mutual Fund Strategy: Swing for the Fences or Bat for Average (Chalmers and Dayani)
The authors examine how mutual fund managers' stock-picking strategies affect fund performance, risk, investor flows, and fees over time.
Animal Spirits: Superstitious Behavior by Mutual Fund Managers (Chen et al.)
Superstitious beliefs are shown to influence professional investors' risk-taking behavior, while the effect changes throughout their careers.
Investing
Rules of Thumb and Retirement Accounts (Horneff et al.)
This study evaluates how well simple financial strategies perform compared to optimal decisions in retirement planning across different demographic groups.
Machine Learning and Large Language Models
Re(Visiting) Large Language Models in Finance (Rahimikia and Drinkall)
This paper finds that smaller, domain-specific, large language models outperform larger models in trading, and where the predictive power seems significantly larger for small-cap stocks.
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT (Shen and Zhang)
The authors explore the ability of different large language models to classify text sentiment across prompting techniques.
AI and Bond Values: How Large Language Models Predict Default Signals (Khoja)
This paper estimates firm default likelihoods by applying ChatGPT to corporate transcripts, finding predictive power for credit spreads.
Machine Learning for Interest Rates: Using Auto-Encoders for the Risk-Neutral Modeling of Yield Curves (Lyashenko et al.)
The authors propose a new approach to interest-rate modeling under the risk-neutral measure, incorporating latent state variables using autoencoders.
Dissecting Machine Learning Anomalies (Jo and Kim)
Machine learning models allocate more weight to volatility and fundamental factors versus previously emphasized variables when predicting stock returns, especially for non-microcap stocks.
AI-Driven Failed Trials in Investment Strategies: A Network Data Analysis Approach (Bolesta et al.)
This study explores how AI and network analysis can improve investment strategies by learning from past failures and simulating potential risks.
Harnessing Generative AI for Economic Insights (Jha et al.)
Corporate transcripts fed into ChatGPT are used to estimate managers’ economic expectations, predicting future economic growth.
GitHub
Awesome Artificial Intelligence
Medium
VWAP Algorithmic Strategy In Python? (Francis)
Bayesian Methods: From Theory to Real-World Applications (Li)
Convenient Time Series Forecasting with sktime (Kübler)
Podcasts
Farouk Jivraj - The Art & Science of Using Alternative Risk Premia (Flirting with Models)
The Math That Explains How Multi-Strategy Hedge Funds Make Money (Odd Lots)
Ray Ozzie - The Future of Intelligent Machines (Invest Like the Best)
Solving the "How Much" Trend Following Question ft. Nick Baltas (Top Traders Unplugged)
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