Weekly Recap
Latest research on investing and trading
Welcome to this week’s roundup of the latest research on investing and other valuable resources. Below, you’ll find a curated list, with each title linking to its source for more details. Thank you for reading and don’t forget to hit the like button.
Bonds
Information Flows in Trading Networks (Huber et al.)
Larger broker networks give bond investors an edge, likely due to receiving non-public information from their trading partners in exchange for trading flow.
Crypto
Twitter-Based Attention and the Cross-Section of Cryptocurrency Returns (Maître et al.)
Tweets by influential users contribute to overreaction and behavioral biases in cryptocurrency markets, and “Ticker-tweets” have the strongest impact on returns.
MicroStrategy, Bitcoin Yield, Complete Markets (Phillips and Pohl)
This paper discusses MicroStrategy's aggressive Bitcoin acquisition strategy, its evolution into a "Bitcoin Treasury Company", and its implications for shareholder value creation.
Equities
Scope Similarity and Cross-Firm Return Predictability (Jin and Li)
The authors identify cross-firm linkages based on product market similarity, showing that investors underreact to news about peer firms, leading to predictable returns and significant alphas from a long-short strategy.
Firm-specific versus systematic momentum (Schmid et al.)
Past research suggests that stock-specific momentum is driven by underlying factor momentum. This paper challenges this idea and shows that firm-specific factors are the main drivers of stock momentum.
Momentum and Factor Momentum: A Re-Examination (Gao et al.)
Related to the previous paper, this study also questions the notion of momentum merely being an aggregation of factor momentum and suggests it is a distinct anomaly driven largely by firm-specific information.
Explaining and Predicting Momentum Performance Shifts Across Time and Sectors (Mamais et al.)
The paper analyzes momentum returns across different economic conditions and time periods, finding evidence that economic conditions can help predict future momentum performance to some extent.
A factor model for the cross-section of country equity risk premia (Fieberg et al.)
A four-factor model based on instrumented principal component analysis prices the cross-section of country stock returns, outperforming traditional factor models.
The Inflation Gamble (Bonaparte et al.)
During periods of high inflation, investors are more likely to gamble on risky stocks and so-called “lottery stocks”, leading to overpricing and subsequent underperformance.
Short Sale Constraints and Stock Returns: 1926-2023 (Schultz)
The author constructs a proxy for stock borrowing fees back to 1926 and finds that difficult-to-borrow stocks consistently underperform, explaining various market anomalies across nearly a century.
Machine Learning and Large Language Models
Does One Pattern Fit All? Image Analysis for Different Equity Styles (Den and Vincent)
The authors explore stock prediction using price-chart images and Convolutional Neural Networks, finding that multi-category models outperform binary ones, especially for small caps.
Risky Words and Returns (Seyfi)
Using the risk disclosures in firms' 10-K filings, the author identifies influential words that drive stock prices. A long-short strategy based on these words generates a significant alpha.
Glass Box Machine Learning and Corporate Bond Returns (Bell et al.)
Explainable Boosting Machine models are used to predict corporate bond returns, achieving similar performance as Random Forests and XGBoost while maintaining interpretability.
Assessing the Impact of Technical Indicators on Machine Learning Models for Stock Price Prediction (Deep et al.)
The authors study the inclusion of various technical indicators in random forest regression models for predicting 1-minute SPY returns, finding that standard open, high, low, close data contribute more to model performance than technical indicators.
Blogs
FX trading signals: Common sense and machine learning (Macrosynergy)
Front-Running Seasonality in US Stock Sectors (Quantpedia)
Front Running Commodity Seasonality (Allocate Smartly)
What we’ve learnt from reading thousands of Fed communications (Turnleaf Analytics)
GitHub
Algorithmic Trading with Python
Medium
25 Github Repositories Every Python Developer Should Know (Parashar)
Podcasts
Inside the Secretive World of Pod Shops | Bob Elliott (Excess Returns)
2024's Biggest Investment & Trend Following Insights (Part 1) (Top Traders Unplugged)
Jim Caron on the Market Selloff and the Fed's Historic Adjustment (Odd Lots)
Aswath Damodaran, the Dean of Equity Valuation from the Stern School of Business, host Rick Ferri (Bogleheads on Investing)
Last Week’s Most Popular Links
Market Risk Premium: Best Linear Predictor in High Dimension (Jiang et al.)
Fast trend following (Quantitativo)
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.
This newsletter contains links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of these external sites or for that of subsequent links. Users click on these links at their own risk.
Great work, thanks for sharing