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 click the like button below and share it with your friends and colleagues. I’d love to hear your thoughts—let me know in the comments which paper(s) or resource(s) you found most interesting!
Currencies
Interest Rates, Convenience Yields, and Inflation Expectations: Drivers of US Dollar Exchange Rates (Bernoth et al.)
The paper identifies three main drivers of the US dollar exchange rate: Shocks to inflation expectations, global demand for a safe US dollar, and changes in short-term interest rates.
Equity
Finding Alpha in Short Interest (QuantSeeeker)
This article explores the predictive power of short-interest data, its applications in forecasting individual stock and market returns, and practical insights for investors.
End-of-Day Reversal (Soebhag et al.)
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.
Insider filings as trading signals - Does it pay to be fast? (Oenschläger and Möllenhoff)
Fast reactions to insider trading announcements don't guarantee profitable strategies due to limited trading volumes and liquidity constraints.
Crowding and Downside Risk: International Evidence (Jain et al.)
An increase in a stock’s crowdedness, measured as institutional ownership in relation to daily turnover, is associated with higher downside risks and a greater likelihood of future stock price crashes.
Hedge Funds
Downside Risk and Hedge Fund Returns (Argyropoulos et al.)
Hedge funds with high downside risks have higher returns than funds with low downside risks, suggesting a positive risk-return relation for hedge funds.
Machine Learning and Large Language Models
Predicting FX Market Movements Using GAN with Limit Order Event Data (Peng et al.)
A generative adversarial network model using limit order book data as input features is shown to predict 5-minute FX returns.
The Promise and Peril of Generative AI: Evidence from GPT-4 as Sell-Side Analysts (Li et al.)
GPT-4 is shown to forecast earnings less accurately than human analysts, both pre- and post-the knowledge cutoff date.
Predictive Power of LLMs in Financial Markets (Shi and Hollifield)
This paper examines GPT’s performance in economic forecasting, finding that it struggles to handle noisy data and exhibits look-ahead bias, underperforming much simpler models.
Macro
DFROG: A Nowcasting Model for GDP Growth (van Dijk et al.)
This paper describes the GDP nowcasting model used by the Dutch central bank, explaining in detail its components and forecasting performance.
Portfolio Optimization
Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy (Lu et al.)
As recent research on “double descent” suggests that overparameterized models can outperform parsimonious models, this paper studies this effect for portfolio optimization and provides theoretical insights on when complex models outperform.
The Impact of Rebalancing Strategies on ETF Portfolio Performance (Bányai et al.)
This study examines how rebalancing affects different ETF types, finding that equities and commodities benefit from rebalancing, while bonds and REITs may not.
Private Equity
Do Public Equities Span Private Equity Returns? (Ghysels et al.)
While a significant part of private equity returns can be explained by factors related to public equities, the remaining private equity-specific factors significantly enhance portfolio performance through improved Sharpe ratios and better diversification.
Volatility
Forecasting and Managing Volatility: An S&P 500 Case Study (Dai et al.)
The authors explore volatility targeting strategies for allocating between S&P 500 and T-bills, finding that simple methods are as effective as more complex methods and gains from volatility targeting survive transaction costs.
A Multifactor Perspective on Volatility-Managed Portfolios (DeMiguel et al.)
A multifactor portfolio that allows the relative weights to vary with market volatility outperforms an unconditional portfolio out-of-sample and after transaction costs.
On the performance of volatility-managed equity factors — International and further evidence (Schwarz)
The author studies volatility targeting of equity factors across numerous countries and finds that after accounting for transaction costs, significant performance gains are primarily observed for the market and momentum factors.
Blogs
AWS Trading Part 2 - The Strategy (Blackarbs)
Modelling UVXY trading strategies with Excel (Robot Wealth)
GitHub
Eiten - Algorithmic Investing Strategies for Everyone
FinTA (Financial Technical Analysis)
Medium
Python QuickStart for People Learning AI (Talebi)
Can Machine Learning Outperform Statistical Models for Time Series Forecasting? (Chaudhuri)
Python Coding Tips: Lessons I Wish I Knew When I Started Coding (Yadav)
Podcasts
Goldman's Hatzius and Kostin on Markets and Macro in 2025 (Odd Lots)
Morgan Housel: Understand & Apply the Psychology of Money to Gain Greater Happiness (Andrew Huberman)
Embracing Uncertainty using Adaptive Models ft. Richard Brennan (Top Traders Unplugged)
Last Week’s Most Popular Links
Enhancing Momentum Strategies with Fundamentals (QuantSeeker)
Machine Learning Simplified (Andrew Wolf)
Cross-sectional reversal portfolios in the cryptocurrency market: Behavioral approaches (Nakagawa and Sakemoto)
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.