Weekly Recap
Research on investing, macro, 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!
Crypto
Economics of Ethereum (John et al.)
The authors provide an overview and background of the Ethereum protocol and the various incentives of the participants.
Empirical Asset Pricing
The Valuation of Private Companies (Selvam and Whittaker)
The paper presents a model to estimate private company valuations by considering various dynamic factors, including market conditions and company characteristics, to address data limitations and biases.
Pre-Refunding Announcement Gains in US Treasurys (Wang and Zhao)
The paper identifies significant positive returns on U.S. Treasury securities the day before quarterly refunding announcements, driven by reduced market uncertainty.
Options Trading Imbalance, Cash-Flow News, and Discount-Rate News (Chichernea et al.)
The authors develop new measures of option trading imbalances that predict future stock returns, and link their results to cash-flow and discount-rate news.
Why Has Factor Investing Failed? (Seminar Slides) (de Prado and Zoonekynd)
Seminar slides arguing that model specification errors are the likely reason for why factor investing strategies have underperformed.
Financial History
Five Financial Eras (Taylor)
Using long-term data from Global Financial Data, the author goes back eight centuries and describes movements in financial assets over time.
Lecture Notes
Linear models and extensions (Ding)
These lecture notes by Peng Ding at Berkeley are great. 400 pages on OLS, Ridge, Lasso, WLS, Logistic regressions, Quantile regressions, and much more.
Time Series Analysis (Kotzé)
Extensive lecture notes on time series analysis.
Valuation and Security Analysis (Swaminathan)
Great lecture notes by Bhaskaran Swaminathan at LSV Asset Management.
Machine Learning and Large Language Models
Evaluating LLMs in Financial Tasks - Code Generation in Trading Strategies (Noguer i Alonso and Dupouy)
The authors compare the ability of a range of large language models to generate Python code for various technical indicators.
Leading Stocks and the Stock Market Expected Returns (Chen et al.)
The study identifies leading stocks using LASSO, finding that negative leaders have strong predictive power for future market returns.
Stress index strategy enhanced with financial news sentiment analysis for the equity markets (Lefort et al.)
The paper presents a risk-on/off strategy combining stress indicators and news sentiment analysis, delivering significantly higher Sharpe ratios than a buy and hold strategy.
Predicting Individual Corporate Bond Returns (Feng et al.)
This paper evaluates the ability of a range of machine learning models to predict corporate bond returns, finding significant return predictability.
Machine Learning and the Cross-Section of Emerging Market Corporate Bond Returns (Mansouri and Eterovic)
Similar in nature to the previous paper, the authors evaluate the ability of machine learning models to predict corporate bond returns, but focusing on emerging markets.
Advancing Markowitz: Asset Allocation Forest (Bettencourt et al.)
The paper introduces a novel machine learning-based model, Asset Allocation Forest, which optimizes portfolio weights by dynamically responding to market conditions, outperforming traditional methods.
Macro
Macroeconomic Expectations and Expected Returns (Deng et al.)
The paper develops a macro index from professional forecasters which is found to predict stock returns, highlighting countercyclical equity premium patterns.
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