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 other valuable resources. Below, you’ll find a curated list with each title linking to its source for more details. If you find this recap helpful, please hit the like button below and share it with your friends and colleagues.
Bonds
Unveiling Insights: How S&P 500 Index Revisions Reveal More Information Through Bond Price Reactions (Chiang and Tsai)
Corporate bonds of firms being added to the S&P 500 earn abnormal returns.
Anatomy of the Treasury Market: Who Moves Yields? (Chaudhary et al.)
The authors study which investors move Treasury yields, finding that the impact of foreign investors has significantly diminished since the Great Financial Crisis.
Crypto
Cross-sectional reversal portfolios in the cryptocurrency market: Behavioral approaches (Nakagawa and Sakemoto)
This paper proposes an improved reversal strategy in crypto markets that remains profitable after transaction costs.
Currencies
A Conditional Factor Model for Currency Option Returns (Filippou et al.)
The authors propose a latent factor pricing model that prices the cross-section of currency option returns and suggest an improved option momentum strategy.
What events matter for exchange rate volatility? (Martins et al.)
This paper studies a large set of macro announcements and finds that news related to the Taylor rule such as interest rate announcements, inflation, and economic activity drives FX volatility.
Equities
Enhancing momentum strategies with fundamentals (QuantSeeker)
Incorporating free cash flow yields into momentum strategies improves Sharpe ratios by up to 40% compared to standard momentum approaches, while significantly reducing drawdowns.
Index Investing and Sentiment Spillover (Atmaz and Zhou)
This paper develops a theoretical model to study how the growing trend of index investing affects stock prices and investor behavior.
US Equity Announcement Risk Premia (Petrasek and Kukacka)
Sorting on stocks’ sensitivity to macro announcements generates a significant cross-sectional return spread.
Reaching for Beta (Genc et al.)
Fund managers increase their allocation to high-beta stocks in times of rising short-term interest rates.
Machine Learning and Large Language Models
Design choices, machine learning, and the cross-section of stock returns (Chen et al.)
The authors demonstrate how design choices in machine learning models significantly impact performance when predicting stock returns and provide practical guidance on improving model robustness.
Machine learning simplified (Andrew Wolf)
A great book and introduction to supervised learning, including Python code.
Scaling Core Earnings Measurement with Large Language Models (Shaffer and Wang)
Large language models can effectively estimate a company's true profitability from complex 10-K filings when using refined prompting techniques.
A Random Forest approach to detect and identify Unlawful Insider Trading (Neupane and Griva)
Machine learning techniques are useful for detecting unlawful insider trading, achieving high accuracy in classifying transactions across various experimental setups.
Effective Convergence Trading of Sparse, Mean Reverting Portfolios (Rácz and Fogarasi)
This paper applies Long Short-Term Memory models to trading mean-reverting stock portfolios, finding meaningful Sharpe ratios.
Deep Learning Networks and Machine Learning Technology: Using Bitcoin Price Trends to Compare Model Efficiencies (Wang et al.)
This study compares the ability of various machine learning models to predict Bitcoin prices, discussing the strengths and limitations of each approach.
Deep Learning in Long-Short Stock Portfolio Allocation: An Empirical Study (Guo)
The author applies a range of deep learning models to stock return prediction finding promising results, particularly for Long-Short-Term Memory and Transformer models.
The Value of Information from Sell-side Analysts (Lv)
The author uses large language models to gauge the value of sell-side analysts, finding that they provide substantial value to investors, especially when interpreting new financial data after earnings announcements.
The Role of AI in Financial Forecasting: ChatGPT's Potential and Challenges (Bi et al.)
This study explores how AI can enhance financial forecasting while highlighting the need for careful integration and human oversight.
A Review of Reinforcement Learning in Financial Applications (Bai et al.)
This is a comprehensive survey paper on the use of reinforcement learning in finance.
A Survey of Financial AI: Architectures, Advances and Open Challenges (Liu)
Recent AI advancements in finance have improved market predictions, trading strategies, and portfolio optimization, but challenges in real-world implementation remain significant.
Macro
Inflation Cyclicality and Stock-Bond Comovement: Evidence from News Media (Roppel and Uhrig-Homburg)
Media coverage of inflation, particularly its content, and sentiment, captures the changing cyclical relationship between inflation and growth expectations and influences stock-bond correlations.
Portfolio Optimization
Indirect and Direct Forecasting of Volatility-Timing Portfolio Weights (Xie)
When using volatility timing strategies that ignore asset correlations, the indirect method of forecasting volatility and then deriving portfolio weights consistently achieves higher Sharpe ratios than direct weight forecasting.
Blogs
CTA index replication and the curse of dimensionality (Rob Carver)
How random forests can improve macro trading signals (Macrosynergy)
The Risk-Constrained Kelly Criterion: From the foundations to trading (QuantInsti)
Takeaways from QuantMinds 2024 in London (Turnleaf Analytics)
GitHub
Medium
Which Feature Engineering Techniques improve Machine Learning Predictions? (Maddali)
Automating Forex Analysis with ChatGPT and APIs (Adithyan)
The 21 Free Apps Every Investor Should Know to Invest Like a Pro (Gonzalez)
Podcasts
Timeless Lessons from a Quant Legend | Cliff Asness (Excess Returns)
The Concept of Return Stacking with Corey Hoffstein (Masters in Business)
Avoiding the Herd: A Different Path to Alpha ft. Jerome Callut (Top Traders Unplugged)
Randy Cohen & Michael Green: How Concerned Should We Be About Index Funds? (Rational Reminder)
Last Week’s Most Popular Links
A Market Timing Indicator (QuantSeeker)
The Myth of Profitable Day Trading: What Separates the Winners from the Losers? (Gallegos-Erazo)
Risk Factor, Risk Premium and Black-Litterman Model (Abou Rjaily et al.)
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