Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links featuring the latest research on quant investing and useful resources. Below, you'll find a curated list, with each title linking to the source for more information.
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Bonds
The Past, Present, and Future of Low-Risk Corporate Bonds (Muskens and Houweling)
The authors construct new measures of systematic risk and find significant evidence of a low-risk anomaly in corporate bond markets.
Equities
A Multifactor Perspective on Volatility-Managed Portfolios (DeMiguel et al.)
The study considers volatility timing of a multifactor portfolio and finds that it outperforms benchmark portfolios, also after transaction costs.
Leveraging the Low-Volatility Effect (van der Linden et al.)
This paper explores strategies for capitalizing on the low-volatility effect in investing and offers solutions for various investor objectives and constraints.
Sector and Style Factor Rotations in Equity Markets: Detection and Towards Causation (Mokhtarian et al.)
The authors examine the detection of stock-market rotations and their determinants.
It's All About Timing: Analyst Forecasts During Weekday Non-Trading Hours (Xi et al.)
Financial analysts are shown to strategically time the release of negative earnings forecasts to minimize market impact, especially when uncertain about forecast accuracy.
Leverage Volatility Risk (Shentu et al.)
Sorting stocks on their exposure to financial leverage volatility generates a significant risk premium.
Divergence of Fear Gauges and Stock Returns (Ruan and Wei)
The divergence between the MOVE index and the VIX significantly predicts stock returns.
Price-Path Convexity and Short-Horizon Return Predictability (Gulen and Woeppel)
This study finds that the shape of recent stock price movements can predict future short-term returns better than traditional indicators.
ESG
Carbon Burden (Pastor et al.)
This study quantifies the substantial environmental impact of U.S. corporations by estimating the future costs of their greenhouse gas emissions.
Climate Capitalists (Gormsen et al.)
This paper examines how firms' environmental practices affect their perceived cost of capital and investment decisions, especially since sustainable investing became prominent.
Oil-Driven Greenium (Shi and Zhang)
Time variation in the greenium is shown to be more closely related to changes in oil demand than to investor attention to climate issues.
Machine Learning and Large Language Models
Quantifying Uncertainty: A New Era of Measurement Through Large Language Models (Audrino et al.)
This study demonstrates how large language models can enhance the measurement and analysis of economic uncertainty across various domains.
Learning from Memory: Asset Pricing Via Recurrent Neural Network and Attention Mechanism (Zhou and Wang)
The authors propose a non-linear asset pricing model that outperforms benchmark models, tested on U.S. equities.
The Future of Accounting: Sentiment Analysis in AI-Rewritten SEC Filings (Lehner)
This paper explores how AI-powered rewriting of financial reports can influence market sentiment and potentially impact stock performance.
Extracting Alpha from Financial Analyst Networks (Gorduza et al.)
The authors propose a trading strategy that trades on momentum spillover effects between firms, using analyst coverage networks to define firm connections.
StockGPT: A GenAI Model for Stock Prediction and Trading (Mai)
A transformer model trained on U.S. stock returns is presented and is shown to learn historic patterns, generating strong out-of-sample performance.
Dynamic graph neural networks for enhanced volatility prediction in financial markets (Kumar et al.)
A model based on Graph Neural Networks is shown to outperform benchmark models in volatility forecasting, tested on global equity indices.
Macro
Can Inflation and Monetary Policy Predict Asset Prices? (Fleischer)
The author studies how inflation and central bank policies influence asset prices through the lens of a consumption-based asset pricing model.
Portfolio Selection
Conformal Predictive Portfolio Selection (Kato)
The author studies portfolio allocation across US and Japanese stocks using conformal inference.
Online Portfolio Selection Using Macroeconomic Pattern Matching (Sajadi et al.)
This paper forms stock portfolios based on clustering and pattern-matching techniques, utilizing information from macro variables.
Blogs
Vol Estimators and Vol Targeting (QuantSeeker)
Using principal components to construct macro trading signals (Macrosynergy)
Lognormal Stochastic Volatility – Youtube Seminar and Slides (Artur Sepp)
GitHub
ffn - Financial Functions for Python
FinRL: Financial Reinforcement Learning
Medium
Principal Component Analysis with Python (A Deep Dive) (Franco)
A Sentiment-Driven Algo Trading Strategy that Beats the Market (Adithyan)
Building A Pairs-Trading Strategy With Python (Murtazin)
Podcasts
The Untold Side of the Turtle Trading Legacy ft. Bill Eckhardt & Rob Sorrentino (Top Traders Unplugged)
The Surprising Factors Behind CTA Performance: Is Less More? ft. Rob Carver (Top Traders Unplugged)
Price-Action, Swing Trading Equities from the Long Side w/ Mark Ritchie II (Chat with Traders)
The Practical Implications of the Rise of Passive Investing | Mike Green (Excess Returns)
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