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 useful resources. Below, you'll find a curated list, with each title linking to the source for more information. Thank you for reading!
Banking
Two Centuries of Systemic Bank Runs (Jamilov et al.)
This study examines 200 years of global bank runs, revealing their frequency, economic impact, and relationship to broader financial crises.
Crypto
The Crypto Cycle and Institutional Investors (Copestake et al.)
The authors document how large investors shape cryptocurrency markets, influencing their relationship with stocks and sensitivity to central bank actions.
Derivatives
Decoding News: How Media Risk and Ambiguity Shape CDS Spreads (Santi and Sadoghi)
Global risk and ambiguity estimated from a large set of news articles are shown to significantly explain variations in corporate CDS spreads and demonstrate some predictive power for future spreads.
Equities
Analysts Disagreement and the Cross-Section of Stock Returns (Wang)
This study documents that various forms of analyst disagreement exhibit significant predictive power for future stock returns.
The Brand Premium (Boustanifar and Kang)
Companies with strong brand recognition are found to generate a significant alpha as investors and analysts underestimate their future earnings.
Tradable Risk Factors for Institutional and Retail Investors (Johansson et al.)
The authors document significant challenges in replicating theoretical investment strategies in real-world markets, highlighting the gap between academic models and practical implementation.
Tail Risk Exposure and the Cross-Section of Expected Stock Returns (Nicolas)
This paper challenges the reliability of tail risk measures in predicting stock returns, highlighting potential biases in their estimation and interpretation.
Resolving Estimation Ambiguity (Décaire et al.)
Financial analysts' subjective choices when estimating a company's cost of capital can significantly impact investment decisions.
Do Institutional Investors Exploit Expectation Errors in Value/Glamour Stocks? (Hasan et al.)
Using institutional ownership data, the authors document that sophisticated investors strategically exploit mispricing opportunities.
Fixed Income
The Global Cross-Section of Corporate Bonds: Market, Maturity and Liquidity (Bekaert et al.)
The authors explore the pricing of international corporate bonds and find that a global three-factor model prices the cross-section of bonds well.
Machine Learning and Large Language Models
Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets (Ramdas and Wells)
This study explores how different trade characteristics influence future market movements by analyzing millions of stock transactions using neural networks.
Disentangling the sources of cyber risk premia (Maréchal and Monnet)
Using textual analysis, the authors measure companies' cyber risks and find that firms with high cyber risks are subject to a positive risk premium.
The authors introduce a reinforcement learning framework that demonstrates superior risk-adjusted returns compared to other reinforcement-learning-based portfolio optimization strategies.
Automate Strategy Finding with LLM in Quant investment (Kou et al.)
Ensembling across alpha sources mined through large language models is shown to generate robust performance.
Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds (van Brakel)
Machine learning models, particularly random forest, are shown to learn which stocks are likely to be held or avoided by top-performing fund managers in the future.
StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction (Wang et al.)
The authors present a large language model (LLM) adapted to time-series data that is shown to outperform other LLMs in predicting stock prices.
Out-of-sample predictability of firm-specific stock price crashes: A machine learning approach (Kaya et al.)
Machine learning models are shown to be useful predictors of stock price crashes, using features based on return data, accounting data, and corporate filings.
Macro
Narratives about the Macroeconomy (Andre et al.)
This study explores how differences in people's explanations for economic events shape differences in macro expectations and may influence broader economic outcomes.
Portfolio Optimization
Smoothing Out Momentum and Reversal (Chitsiripanich et al.)
A new framework for mitigating transaction costs in high-turnover strategies is proposed, yielding significant improvements in risk-adjusted returns.
Real Estate
The Future of Automated Real Estate Valuations (Xiong et al.)
This study explores how automated property valuation tools are evolving and may reshape real estate practices in the coming years.
Regime Switching
Regime-Switching Factor Models and Nowcasting with Big Data (Akbal)
The author adapts and extends the Expectation-Maximization (EM) algorithm for nowcasting with regime shifts, improving GDP forecasts and recession predictions in real time.
Volatility
Do Equity and Options Markets Agree about Volatility? (Chong and Todorov)
The authors explore potential volatility arbitrage opportunities in the zero-dated S&P 500 options market and discuss implications for the integration between equity and options markets.
Blogs
Inflation and equity markets (Macrosynergy)
The devil is in the details (Quantitativo)
GitHub
Medium
Mixture of KAN Experts for High-Performance Time Series Forecasting (Peixeiro)
Probably The Easiest Way To Automate Web Tasks Using Python (Tao)
Genetic Algorithms Simplified: A Step-by-Step Example for Beginners (Nguyen)
Podcasts
The Paradox of High-Volatility Alternatives ft. Alan Dunne (Top Traders Unplugged)
Gary Antonacci II - New Models & Research Updates (The Algorithmic Advantage)
Prof. Marco Sammon: How are Passive Investors Affecting the Stock Market? (Rational Reminder)
100 Unloved Stocks: How Rob Arnott Finds & Invests In Them (The Meb Faber Show)
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