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!
Bonds
Bonds: Hedges or Risky Opportunities? (Cochrane)
The riskiness of bonds depends on each investor's unique circumstances.
Commodities
Hedging Geopolitical Risks with Diverse Commodities (Parnes and Parnes)
This study explores how various commodity futures can be used to protect investors against financial risks arising from global geopolitical events.
Crypto
A Trend Factor for the Cross-Section of Cryptocurrency Returns (Fieberg et al.)
The authors propose a new crypto factor that effectively predicts cryptocurrency returns, unexplained by existing predictors.
Currencies
Monetary Policy Transmission through the Exchange Rate Factor Structure (Somogyi et al.)
The authors show how U.S. monetary policy affects the risk appetite of banks and investment funds.
Derivatives
On the implied volatility of Inverse options under stochastic volatility models (Alòs et al.)
This paper models the implied volatility of Inverse options traded on the Deribit exchange, providing theory and empirical evidence.
Equity
Disagreement and Market Returns: Evidence from Institutional Investors' Portfolio Churn Rate (Bayar et al.)
This paper suggests a new measure of investor disagreement, predicting returns out-of-sample.
Speculative Media Tone and Long-Term Return Reversals (Deuskar et al.)
Media coverage with speculative content, rather than factual reporting, is associated with significant stock mispricing and subsequent price corrections.
Improving Estimation of Portfolio Risk Using New Statistical Factors (Liu et al.)
This paper introduces new statistical equity risk factors that significantly improve portfolio risk estimation beyond traditional factors.
Predicting Anomalies (Bowles et al.)
The signals of accounting-based anomalies are shown to be predictable one quarter before information releases.
Finance Without Exotic Risk (Bordalo et al.)
This study challenges traditional asset pricing models by showing that cross-sectional stock return patterns can largely be explained by predictable errors in earnings expectations.
Lecture Notes
Notes on Statistics (Justin L. Ripley)
Machine Learning and Large Language Models
Visualizing Earnings to Predict Post-Earnings Announcement Drift: A Deep Learning Approach (Garfinkel and Hsiao)
This paper applies convolutional neural networks to bar charts of past earnings, finding that they strongly predict post-earnings announcement drifts.
ChatGPT and Corporate Policies (Jha et al.)
Firms' future investment plans are predicted by applying ChatGPT to earnings calls, where high-investment firms underperform in the long run.
WhiteBox inside blackbox: Using interpretable language models to analyze financial narratives (Cao et al.)
This paper proposes an interpretable language model that improves sentiment analysis, which is applied to earnings calls.
Transfer learning for financial data predictions: a systematic review (Lanzetta)
This comprehensive review explores how transfer learning techniques can enhance predictability in financial markets, addressing data scarcity and model performance across domains.
APT or “AIPT”? The Surprising Dominance of Large Factor Models (Didisheim et al.)
A pricing model featuring a large number of factors outperforms classical parsimonious models like the Arbitrage Pricing Theory model.
Private Equity
The Private Capital Alpha (Brown et al.)
Buyout funds have produced a statistically significant alpha while venture capital and real estate funds have not, accounting for NAV smoothing and various investor constraints.
Statistics
Tail Risk Analysis for Financial Time Series (Kiriliouk and Zhou)
This is a book chapter on the estimation of tail risk, highlighting the challenges of serial dependence in financial time series data.
Trading
When a Crystal Ball Isn’t Enough to Make You Rich (Haghani and White)
An experiment showing that knowing tomorrow’s news doesn’t guarantee success in trading.
Blogs
Macro information changes as systematic trading signals (Macrosynergy)
A different indicator (Quantitativo)
GitHub
bt - Flexible Backtesting for Python
Medium
Fetching Interactive Brokers historical data (Laignel)
GPs vs. Linear Regression vs. XGBoost (Lövström)
Mastering Data Streaming in Python (Shakhomirov)
Podcasts
Corey Hoffstein - Return Stacking, ETFs & Trend Replication (The Algorithmic Advantage)
The Carry Risk Lurking in Your Portfolio ft. Kevin Coldiron (Top Traders Unplugged)
Dr. Bryan Taylor: Lessons from Market History (1600-2024) (Rational Reminder)
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Very good work! Very useful. Thanks!