Weekly Recap
Research on quant investing and trading.
Welcome to this week’s collection of links to the latest research and insights on quant investing. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
Bonds
Data Uncertainty in Corporate Bonds (Jostova et al.)
The paper examines how varying data choices in corporate bond research can lead to different conclusions about investment strategies.
Pricing Corporate Bonds with Credit Risk Primitives (Dickerson and Nozawa)
This paper proposes a new corporate bond risk factor that generates a meaningful Sharpe ratio, even after transaction costs.
Subjective Risk Premia in Bond and FX Markets (Pesch et al.)
Subjective risk premia, measured by professional forecasters' beliefs about bond yields and exchange rates, vary countercyclically and predict returns.
Commodities
Trading Activity of Commodity Futures and Options Around USDA Announcements (Tianyang Zhang)
The study finds that USDA reports significantly affect commodity futures more than options, with informed trading occurring pre-announcement.
Equities
Decoding Cross-Stock Predictability: Peer Strength versus Firm-Peer Disparities (Avramov et al.)
The paper presents a method to predict stock returns by analyzing industry peer connections and firm deviations within their industries.
Portfolio Size, Portfolio Composition, and the Skewness of Returns (Boyer and Boyer)
The paper investigates how the skewness of returns in portfolios changes with the number of assets, finding that larger, growth-oriented portfolios tend to have more negative skewness.
Towards a New Financial Statement Analysis (Dargenidou and Penman)
The paper proposes a revised financial statement analysis method that predicts future profitability and associated risks by incorporating risk metrics, enhancing investment valuation.
Data Arbitrage with Proprietary Dividend Forecasts - Historically Precise Updates Led to U.S. Outperformance (Zhao and Ao)
This paper finds that the divergence of their dividend forecasts from consensus predicts returns in the short term.
Lecture Notes
Advanced linear algebra for data science (Keaton Hamm)
“These are Lecture Notes to the MATH 496T / MATH 577 course entitled Advanced Linear Algebra for Data Science taught at the University of Arizona.”
Machine Learning and Large Language Models
Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market (Korniejczuk and Slepaczuk)
The authors consider a statistical arbitrage strategy using graph clustering, outperforming benchmark models.
FineWeb: decanting the web for the finest text data at scale (Penedo et al.)
The FineWeb project by Hugging Face focuses on extracting high-quality text data from the web efficiently and at scale.
Revolutionizing Finance with LLMs: An Overview of Applications and Insights (Zhao et al.)
This paper surveys the recent literature on large language models and financial applications, including sentiment analysis and forecasting.
Predict Mini Flash Crashes from High-Frequency Data by Machine Learning (Liu et al.)
The authors explore the use of machine learning to predict mini flash crashes in stock markets, identifying patterns that precede these events.
Macro
Household Inflation Expectations: An Overview of Recent Insights for Monetary Policy (D'Acunto et al.)
The paper explores how diverse household inflation expectations impact monetary policy effectiveness, highlighting significant deviations from traditional economic models.
Pension Funds
The Long-Run Performance of Public Pension Funds in the US (Richard Ennis)
Public pension funds in the US initially thrived but have struggled post 2008, largely due to changing impacts of alternative investments.
Volatility/VIX
Chicken and Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX? (Robert Hanna)
The author suggests using SPX signals to trade the VIX, instead of the other way around.
Bayesian methods for solving estimation and forecasting problems in the high-frequency trading environment (Paul Bilokon)
The paper explores advanced Bayesian techniques for improving estimation and prediction in high-frequency trading, focusing on stochastic volatility models and filtering methods.
Blogs and Podcasts
How “beta learning” improves macro trading strategies (Macrosynergy)
Revisiting Overnight vs Intraday Equity Returns (Robot Wealth)
Navigating Market Volatility Mastering Managed Futures and Carry Strategies (ReSolve Asset Management - podcast)
The Trend Following ETF Revolution ft. Katy Kaminski (Top traders unplugged - podcast)
New Volatility Based Trading Techniques with Rob Hanna (Better System Trader - podcast)
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.