Weekly Recap
Recent research on quant investing 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!
Commodities
This is the first article in a new series of longer-form articles that I intend to write. Several recent papers have documented that sentiment of various forms predicts commodity returns. I review the latest literature in this area and conclude with key takeaways for investors.
Crypto
What Drives Crypto Asset Prices? (Adams et al.)
This paper decomposes the variation in Bitcoin returns into different shocks and finds that most of the return variation can be attributed to crypto-specific shocks.
This paper examines the stability, transaction costs, and market structure of stablecoins.
Trend-following Strategies for Crypto Investors (Le and Ruthbah)
The authors explore trend-following strategies for major cryptocurrencies and find that investors can benefit from trend following.
Equities
Hidden Alpha (Ammann et al.)
The authors study hidden connections on Facebook between asset managers and firm officers and find that trades associated with these connections yield a significant alpha.
The Cnn Fear and Greed Index as a Predictor of Us Equity Index Returns (Farrell and O’Connor)
This paper finds that the fear and greed index constructed by CNN Business predicts returns on US equity indices.
A New Equity Investment Strategy with Artificial Intelligence, Multi Factors, and Technical Indicators (Mita and Takahashi)
The paper presents a new investment strategy using machine learning, multi-factor models, and technical indicators to outperform traditional equity funds.
Machine Learning and Large Language Models
How to Choose a Reinforcement-Learning Algorithm (Bongratz et al.)
The paper provides a structured guide for selecting reinforcement-learning algorithms and action-distribution families.
An Augmented Financial Intelligence Multi-Factor Model (Mihov et al.)
This study explores integrating human expertise with machine learning for financial forecasting.
This paper explores a pure data-driven option-trading model applied on S&P 100 stocks, outperforming benchmark models even after assumed transaction costs.
NeuralBeta: Estimating Beta Using Deep Learning (Liu et al.)
The authors introduce a deep learning model for estimating betas, capturing complex relationships and adapting to changing conditions.
Does sentiment help in asset pricing? A novel approach using large language models and market-based labels (Audrino et al.)
This paper collects an extensive dataset on financial text, fine-tunes a pre-trained large language model, and constructs a long/short sentiment-based trading strategy that generates a meaningful Sharpe ratio after transaction costs.
Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information (François et al.)
A data-driven approach to optimal hedging of S&P 500 options based on reinforcement learning is presented, where features of the implied volatility surface are found to be important.
This paper uses XGBoost together with the Refinitiv news database to predict next-day volatility jumps, formulating a trading strategy that yields a significant alpha.
This paper introduces a machine learning model that enhances traditional factor models by discovering factors automatically and improves risk forecasting, synthetic data generation, and portfolio optimization.
The authors present a machine learning model using CatBoost to evaluate municipal bonds' relative value, outperforming traditional methods.
Private Funds
Understanding Private Fund Performance (Hendrix and Medhat)
This is a comprehensive evaluation of the performance of private funds, across asset classes.
Blogs
Macro-quantamental scorecards: A Python kit for fixed-income markets (Macrosynergy)
Trading ETFs while fear and greed rise (Quantitativo)
Intuitive Options Pricing (Robot Wealth)
Podcasts
Andreas Clenow - A Most Private Discussion on Building Long Term Wealth through Trading (The Algorithmic Advantage)
Lots More: Did the Fed Just Make a Policy Mistake? (Odd Lots)
Scott Patterson's Chaos Kings, Part 3 (Nassim Taleb)
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