Weekly Recap
Research on investing, macro, 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
How to Construct a Long-Only Multifactor Credit Portfolio? (Blonk and Messow)
The paper finds that integrating single factors into a multifactor corporate bond portfolio provides better risk-adjusted returns than mixing.
Empirical Asset Pricing
Proprietary trading: truth and fiction (Muller)
Peter Muller's classical article on prop trading/stat arb from 2001. The article is kindly provided by Campbell Harvey at Duke.
Equity Premium Events (Knox et al.)
The paper introduces a method to identify days with unusually high equity premiums, highlighting significant economic and political events.
Seeking Alpha: More Sophisticated Than Meets the Eye (Pei et al.)
The text sentiment of Seeking Alpha articles is found to predict stock returns, controlling for earnings information and standard risk factors.
Overnight post-earnings announcement drift and SEC Form 8-K disclosures (Chan and Marsh)
The paper investigates how extreme earnings misses by companies lead to significant overnight stock price drifts, driven by increased information acquisition and heightened market uncertainty.
Mutual Fund Strategy Changes and Performance (Ding)
Mutual funds that frequently adjust strategies based on stock characteristics demonstrate superior performance, highlighting active management skills.
Beta Horizons (Karehnke and de Roon)
The paper examines how beta estimates vary with different estimation horizons, finding that the CAPM model performs better when using longer horizon betas.
Transaction-cost-aware Factors (Baldi-Lanfranchi)
The paper introduces transaction-cost-aware factors that optimize trading strategies to balance risk exposure and transaction costs, enhancing investment returns.
Lecture Notes
Lecture notes on Asset Pricing (Avramov)
Extensive lecture notes, covering both theory and empirics.
Lecture notes on machine learning and the cross section of returns (Avramov)
Comprehensive lectures on machine learning, applied specifically to asset pricing.
Machine Learning and Large Language Models
StockGPT: A GenAI Model for Stock Prediction and Trading (Mai)
The author introduces StockGPT which is trained directly on daily stock returns, finding it to be a significant predictor of returns.
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing (Ye et al.)
This paper reviews the literature on applications of machine learning in finance and asset pricing.
Applied AI for Finance and Accounting: Alternative Data and Opportunities (Cao et al.)
This is a broad survey paper on use cases of machine learning in finance covering research on for example text sentiment, accounting data, image data, and more.
Deep Reinforcement Learning: Extending Traditional Financial Portfolio Methods (Benhamou et al.)
The paper explores how deep reinforcement learning can enhance traditional financial portfolio strategies by adapting to dynamic market conditions.
Maximally Machine-Learnable Portfolios (Goulet Coulombe and Göbel)
The paper introduces a machine learning algorithm that optimizes portfolio weights to enhance predictability and profitability, outperforming benchmarks.
Machine Learning for Quant Macro (Feng)
A book on machine learning for quantitative macro.
Macro
The Global Credit Cycle (Boyarchenko and Elias)
The paper examines how global credit conditions influence local credit markets and economic cycles, highlighting the distinct impact of global credit cycles on asset returns and economic growth.
Portfolio Construction
Optimal Conditional Mean-Variance Portfolio Averaging (Yao et al.)
This paper considers a portfolio strategy of averaging across a range of mean-variance portfolios, achieving superior performance compared to competing approaches.
Untangling Universality and Dispelling Myths in Mean-Variance Optimization (Benveniste et al.)
This paper reviews the classical mean-variance optimization (MVO) framework, describes its relation to expected utility maximization and debunks a series of myths concerning MVO.
More Quant Stuff
FX trading signals with regression-based learning (Macrosynergy)
The Biggest Investment Mistake: Billionaire Hedge Fund Cliff Asness Reveals (Meb Faber)
How to Test the Assumption of Persistence (Robot Wealth)
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