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!
Commodities
Commodity Markets in an Uncertain World: Geopolitical Uncertainty and Wheat Futures Prices (Di Luigi and Puglisi)
The authors study how geopolitical events impact wheat futures prices, finding significant and time-varying effects.
Prices of Risk Estimation for Commodity Factors (Nakagawa and Sakemoto)
Commodity basis and value factors are priced in the cross-section of commodity returns and offer diversification to equity portfolios.
Crypto
The Nature of the Beast: A Study of Crypto Volatility (Gosal et al.)
This is a comprehensive study on the properties of volatility in crypto returns. It finds similarities to traditional markets but also patterns unique to crypto markets.
Derivatives
Inflation-Adjusted Bonds, Swaps, and Derivatives (Jarrow and Yildirim)
A literature review on inflation-linked products, covering bonds, derivatives, pricing models, and empirical findings.
Managing Volatility for Profitable Options Trading: Evidence from Pairs Trading Strategies (Aldridge and Jiang)
The authors explore pairs trading in option markets, finding promising results.
Equity
Sum-of-the-Parts Revised: Economic Regimes and Flexible Probabilities (Haase)
The paper combines the sum-of-the-parts model for predicting stock returns with a regime-switching approach using economic variables.
Hidden neighbours: extracting industry momentum from stock networks (Chul et al.)
This paper captures industry momentum by identifying related firms through a combination of price-based analysis and text similarities in their corporate filings, outperforming standard approaches.
ESG
Carbon Returns across the Globe (Zhang)
While some studies have found that brown firms outperform green firms, this paper corrects for a look-ahead bias in data and finds that green firms have outperformed in the U.S. in recent data.
Machine Learning and Large Language Models
Introduction to Machine Learning (Younes)
A comprehensive introduction to the key mathematical concepts underlying many of today’s machine learning algorithms.
Man vs. Machine: The Influence of AI Forecasts on Investor Beliefs (Stradi and Verdickt)
The authors survey investors, finding they generally trust AI-generated financial forecasts less than those made by human analysts.
Keeping the Faith (and the Returns): An AI Approach to Values-based Investing (O´Hara and Streltsov)
This paper shows how using large language models and reinforcement learning can enhance portfolio construction for faith-based investing, with potential for wider applications in values-based investing.
Machine Learning-Based Prediction of Mini Flash Crashes (Liu et al.)
Machine-learning models are found to predict mini flash crashes, where liquidity, order book imbalances, and measures of informed trading are important predictors.
Detecting Misreported Accounting: A Machine Learning Approach using Text Data (Bui et al.)
The authors develop an approach using machine learning and text analysis to detect financial misreporting in corporate filings.
A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks (Yang)
This paper proposes a model that predicts corporate mergers and acquisitions by analyzing industry relationships and historical patterns over time.
GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets (Xu et al.)
The authors combine a GARCH model with a Long Short-Term Memory model to predict stock market volatility, finding superior performance.
Portfolio Optimization
Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets (Fan et al.)
This paper proposes a robust portfolio optimization method that adapts to market shocks and outperforms existing approaches in simulations and empirical tests.
Private Equity
Predicting Returns of Listed Private Equity (Enders et al.)
The ratio of net asset value to price is found to predict returns on listed private equity.
Trend Following
In Pursuit of Trend-Following Beta: The Promise and Pitfalls of Replication (Braun et al.)
This paper explores the replication of trend-following indices, discusses its potential and challenges, and presents an approach that tracks the index while delivering outperformance.
Volatility
The Impact of Uncertainty on Volatility-Managed Investment Strategies (Harris et al.)
Volatility scaling is shown to be more beneficial for stocks with low uncertainty and in periods of overall low market uncertainty.
The Variance Risk Premium Over Trading and Non-Trading Periods (Papagelis and Dotsis)
The authors document distinct patterns in the variance risk premium between overnight and intraday periods.
Blogs
Mind the gap (Quantitativo)
From Volatility Forecasting to Covariance Matrix Forecasting: The Return of Simple and Exponentially Weighted Moving Average Models (Portfolio Optimizer)
GitHub
Medium
The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 3) (Nóbrega)
Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting (Dancker)
An Extensive Starter Guide For Causal Discovery Using Bayesian Modeling (Taskesen)
Podcasts
Meb Faber on the Big Bear Market in Diversification and Tactical Allocation (Odd Lots)
Do Factor Portfolios Survive Transaction Costs? (Alpha Architect)
An Evidence-Based Look at the Struggles of Value Investing | Larry Swedroe (Excess Returns)
Building Better Portfolios With Don Calcagni (Rational Reminder)
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