Weekly Recap

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!

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Crypto

Cryptocurrency Systematic Risk Dynamics (Doan et al.)

This paper estimates the beta of Bitcoin and Ethereum w.r.t to the stock market and finds a sharp rise in the beta since 2019, suggesting worse diversification benefits for investors.

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Econometrics

Spatial Data Analysis (Rüttenauer)

This paper offers a very comprehensive introduction to spatial econometrics offering a rich overview of various models and theoretical considerations.

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Empirical asset pricing

War Discourse and Disaster Premia: 160 Years of Evidence from the Stock Market (Hirshleifer et al.)

The authors measure investors’ perception of disaster risk using a vast dataset of news articles starting in 1861, finding that certain news topics like war significantly predict market returns.

Ambiguity and the Skewness Premium (Elsas et al.)

The return spread from sorting stocks on skewness is found to be further amplified when also conditioning on the ambiguity of returns, capturing uncertainty in future return distributions of individual stocks.

Investor Sentiment, Social Media and Stock Returns: Wisdom of Crowds or Power of Words? (Lachana and Schröder)

The predictive power of social media sentiment is compared to that of traditional media sentiment, with the former found to be better at predicting stock returns.

Common Risk Factors in the Returns on Stocks, Bonds (and Options), Redux (Chen et al.)

The authors identify a strong set of common risk factors across assets classes but finds large Sharpe ratios for portfolios with zero exposure to the dominant factors.

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Machine learning and natural language processing

True Value Investing in Credits through Machine Learning ('t Hoen et al.)

The paper reviews existing value factors in the credit market, finding low correlations across factors, and use machine learning to construct a new improved value factor.

Sentiment-semantic word vectors - A new method to estimate management sentiment (Phan)

This paper introduces a new method for measuring sentiment from 10-K filings of U.S. firms, finding it to be a significant predictor of stock returns.

Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment (Yuan et al.)

This paper introduces Alpha-GPT 2.0, a multi-agent architecture enhancing the Human-in-the-Loop approach for quantitative investment research.

Can ChatGPT Plan Your Retirement?: Generative AI and Financial Advice (Lo and Ross)

The authors examine the evolution of large language models and their application in various fields, focusing on financial advising.

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Market microstructure

The Market Impact of Leveraged ETFs: A Survey of the Literature (Lenkey)

This paper reviews the academic evidence on the potential impact of leveraged ETFs on markets and finds the economic impact to be insignificant.

Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying (Macrì and Lillo)

The authors apply reinforcement learning to the problem of optimal execution and trading policies, allowing liquidity to be latent and time-varying.

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Portfolio Optimization

The Famous American Economist H. Markowitz and Mathematical Overview of his Portfolio Selection Theory (Gasparavičius and Grigutis)

This paper provides a detailed mathematical overview of Markowitz’s portfolio selection theory.

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Private Equity

The economics of private equity: A critical review (Ljungqvist)

This is a very comprehensive review of research on private equity and its performance over time.

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Trading

A Profitable Day Trading Strategy For The U.S. Equity Market (Zarattini et al.)

This paper offers a comprehensive review of open-range breakout (ORB) strategies in U.S. stocks, finding that 5-minute ORB strategies are more profitable than those using longer time frames, with relative volume playing a pivotal role.

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Quant Blogs

How to model features as expected returns (Robot Wealth)

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