Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links to the latest research on quant investing and useful resources. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
Asset Management
Alternative Data in Active Asset Management (Green and Zhang)
The authors provide an overview of alternative data and discuss how nontraditional data sources provide unique insights and competitive advantages in active asset management.
Crypto
Crypto Tax Evasion (Meling et al.)
The paper quantifies crypto tax evasion using Norwegian data, finding widespread noncompliance and evaluating cost-effective enforcement strategies.
Equities
Analyst recommendations and mispricing across the globe (Azevedo and Müller)
While analyst recommendations have been found to be poor return predictors in U.S. data, this paper finds them to be strong predictors in international data, delivering a meaningful alpha.
ESG
Climate Disaster Risk and Stock Returns (Silva)
The study examines how weather-related risks affect stock performance, revealing that companies less vulnerable to climate events tend to have lower returns because they act as a hedge against climate risk.
ESG Reputational Risk and Debt Risk Premia: Evidence from the Secondary Market for U.S. Corporate Bonds (Huh et al.)
U.S. corporate bonds from companies with higher ESG-related reputational risk incur increased borrowing costs, particularly in blue states.
Fixed Income
Monetary Policy Expectations and Risk Premiums in the U.S.: Evidence from the Ois Curve (Kısacıkoğlu)
Overnight index swap rates are separated into rate expectations and risk premiums and are found to be good measures of monetary policy expectations, better than other interest rates.
Government Debt in Mature Economies. Safe or Risky? (Gomez Cram et al.)
The authors identify two government debt regimes: safe and risky, with the latter involving unfunded fiscal spending, explaining yield increases during COVID.
Machine Learning and Large Language Models
Technical Patterns and News Sentiment in Stock Markets (Leippold et al.)
Integrating CNN-based image recognition on candlestick charts with news sentiment analysis is found to significantly improve predictive performance.
Deep Learning in Finance: A Survey of Applications and Techniques (Mienye et al.)
The paper surveys deep learning applications in finance, highlighting models, challenges, and future research directions.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications (Xie et al.)
The authors introduce Open-FinLLMs, a series of financial language models that enhance financial data analysis with multimodal capabilities and show potential utility in trading.
Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching (Bie et al.)
The paper develops a model using tree-based algorithms to analyze macroeconomic regime shifts impacting U.S. Treasury bond yield curves, offering economically interpretable regimes.
Sentiment trading with large language models (Kirtac and Germano)
The OPT model is found to outperform other large language models and dictionary-based approaches in measuring news sentiment and predicting returns, delivering a highly significant alpha.
Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data (Bo and Xiao)
The authors integrate Time-Series K-means into an ANM Mixture Model for improved causal inference, outperforming traditional K-means.
Macro
Measurement and Theory of Core Inflation (Almuzara and Sbordone)
This is a new handbook chapter on core inflation, and a great resource for learning how it is measured and why it is closely monitored by policymakers.
Momentum Informed Inflation-at-Risk (Szendrei and Bhattacharjee)
The paper develops a model to predict inflation risks by incorporating inflation momentum and quantile variations, improving forecast accuracy.
Portfolio Management
Quick Introduction into the General Framework of Portfolio Theory (Kreins et al.)
The authors provide a concise overview of portfolio theory, integrating risk measures and optimal growth strategies.
On the average sensitivity of unconstrained Markowitz optimization (O´Cinneide)
The paper provides a framework for measuring sensitivity of gross exposure to risk in an unconstrained Markowitz optimization problem.
Statistics
The Role of Causal Inference in the Scientific Method (Presentation Slides) (Lopez de Prado)
Understanding causality is argued to be crucial for scientific research to distinguish true cause-effect relationships from mere correlations.
Trading
Pre-selection in cointegration-based pairs trading (Brunetti and De Luca)
This study examines how different pre-selection metrics impact the profitability of cointegration-based pairs trading strategies, finding great variation in performance across metrics.
Volatility
The Impact of Volatility Jumps on Implied Volatility (Ait-Sahalia et al.)
The paper analyzes how simultaneous jumps in asset prices and volatility significantly influence the implied volatility surface's shape, compared to isolated volatility jumps.
Blogs
Optimal allocation to cryptocurrencies in diversified portfolios – update on research paper (Sepp)
Bear Markets Through the Decades (Alvarez)
Long & Short Mean Reversion Machine Learning (Quantitativo)
GitHub
150+ quantitative finance Python programs
Podcasts
Trading Think Tank 02 – Battle of the Back-Testers (The Algorithmic Advantage)
The Illusion of Backtests and Drivers of Price ft. Richard Brennan (Top Traders Unplugged)
Catastrophe Bond Crash Course: Man AHL's Tarek Abou Zeid & Andre Rzym (Meb Faber Show)
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