Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links featuring the latest research on quant investing as well as useful resources. Below, you'll find a curated list, with each title linking to the source for more information.
Asset Allocation
Risk Factor, Risk Premium and Black-Litterman Model (Abou Rjaily et al.)
The authors present a framework where the Black-Litterman model is applied to risk factors as opposed to specific assets in the portfolio, making it a practical framework for expressing market views.
Crypto
How Wash Traders Exploit Market Conditions in Cryptocurrency Markets (Ng)
This paper studies wash trading in crypto markets, finding that wash traders act strategically, timing their activities based on various market conditions to maximize their impact and exploit opportunities.
Are cryptocurrencies priced in the cross-section? A portfolio approach (Assamoi et al.)
The authors show that the cross-section of crypto returns is strongly linked to economic uncertainty and to traditional asset class factors.
Where is the Price of Bitcoin Determined? Price Discovery in a Fragmented Market (Cosenza and Stalder)
Unregulated crypto exchanges play a dominant role for the price discovery of Bitcoin.
Equity
A market timing indicator (QuantSeeker)
A simple market timing strategy based on a lending standards indicator derived from the Federal Reserve's Senior Loan Officer Survey has historically increased Sharpe ratios by up to 50% compared to a buy-and-hold approach, while significantly reducing drawdowns.
Investor Emotions and Asset Prices (Bin Hasan et al.)
The authors construct a new measure of firm-specific sentiment and find that it predicts stock returns in the cross-section.
Stock Returns and Macroeconomic Uncertainty (Iania et al.)
This paper constructs several measures of economic uncertainty and find that higher uncertainty is a robust negative predictor of stock returns.
Stock-Bond Return Dynamics and the Expected Country Stock Returns (Pyun)
Countries with more positive stock-bond correlations tend to have higher equity returns, significantly outperforming countries with low or negative stock-bond correlations.
Taylor Rule Monetary Policy and Equity Market Risk Premia (Guo et al.)
The authors estimate a Taylor rule based on U.S. data and find that it is a robust negative predictor of stock returns.
Dynamics of Factor Crowding (Hua and Sun)
This paper studies factor crowding among equity factors, its variation over time and factors, and determinants.
Machine Learning and Large Language Models
Harnessing Generative AI for Economic Insights (Jha et al.)
ChatGPT is used to analyze conference call transcripts and gauge managers' views on future business conditions, finding that an aggregate of these assessments is a strong predictor of future economic activity.
Reinforcement Learning Framework for Quantitative Trading (Yasin and Gill)
This paper reviews the use of reinforcement learning in trading, finding promising results when combined with a range of technical indicators, while acknowledging potential limitations and challenges.
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding (Rahimikia et al.)
The authors develop an NLP model that uses word embeddings from financial news to predict realized volatility, which boosts performance when combined with a traditional HAR model.
FinVision: A Multi-Agent Framework for Stock Market Prediction (Fatemi and Hu)
This study presents a multi-agent framework using large language models to analyze financial news, price data, and technical indicators, showing promising out-of-sample trading results that outperform reinforcement learning models.
FinRobot: AI Agent for Equity Research and Valuation with Large Language Models (Zhou et al.)
This paper introduces an AI-powered system that mimics human analysts to produce comprehensive and adaptable equity research reports.
On the (Mis)Use of Machine Learning with Panel Data (Cerqua et al.)
The authors caution researchers about potential pitfalls when applying machine learning to panel data and offer guidelines to prevent misleading results.
Reinforcement Learning applied to dynamic portfolio management: An article review (Mastrogiovanni)
This review explores how reinforcement learning techniques are being applied to improve portfolio management strategies and adapt to market uncertainties.
AI and Finance (Eisfeldt and Schubert)
This paper explores how AI, particularly generative models, is reshaping finance research and corporate strategies.
Trading
The Myth of Profitable Day Trading: What Separates the Winners from the Losers? (Gallegos-Erazo)
Most day traders struggle to profit consistently, with success hinging on disciplined risk management and emotional control rather than just technical knowledge.
Blogs
SPY, SSO, and TLT strategy (Alvarez Quant Trading)
Arbitrage In DEFI (p1) (Tr8dr)
Arbitrage In DEFI (p2) (Tr8dr)
GitHub
Hands-On Machine Learning for Algorithmic Trading
Medium
Empirical Techniques for Enhanced Predictive Modeling: Beyond Traditional ARMA (Li)
TimeGPT: The First Foundation Model for Time Series Forecasting (Peixeiro)
Introducing Causal Feature Learning (Styppa)
Podcasts
Market Narratives and the Unpredictable Alpha ft. Mark Rzepczynski (Top Traders Unplugged)
Prof. Damodaran Reveals His Magnificent Seven Investment Approach (Meb Faber)
MUST Watch For Investors: A Masterclass In Macro And The Market (David Kelly of JPMorgan) (Meb Faber)
Richard Clarida on This Tricky Moment for the Federal Reserve (Odd Lots)
Last Week’s Most Popular Links
Fixed-income Investing Primer (Dubofsky)
Novel and old news sentiment in commodity futures markets (Chi et al.)
Overnight Return Momentum and the Timing of Trading Volume (Perreten and Wallmeier)
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