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.
Alternative Assets
Modern Portfolio Diversification with Arte-Blue Chip Index (Levy and Nicolas)
Adding high-value artwork to an investment portfolio entails significant diversification benefits.
Asset Returns
Long-Run Asset Returns (Chambers et al.)
The authors study long-run risk premia across asset classes and highlight potential challenges with long-term data.
Bonds
The Cross-Section of Corporate Bond Returns: The Pre-World War I Evidence (Van Mencxel)
The author studies a unique hand-collected dataset of corporate bonds from 1868 to 1914, where momentum is the strongest and most robust anomaly.
(In)frequently Traded Corporate Bonds and Pricing Implications of Liquidity Dry-ups (Ivashchenko)
The trading history of a corporate bond is shown to impact its expected return, controlling for a range of characteristics.
Commodities
How to Improve Commodity Momentum Using Intra-Market Correlation (Vojtko and Pauchlyová)
Applying a correlation filter to commodity momentum strategies increases Sharpe ratios significantly.
On Commodity Traded Factors and the Cross-Section of Commodity Returns (Kwon and Maio)
The authors replicate three previous studies and commodity pricing models, finding they do a poor job of explaining the cross-section of commodity returns.
Currencies
Relative Monetary Policy and Exchange Rates (Karau)
Differences in monetary policy across countries are significant drivers of short-term movements in currencies.
Do Professional Forecasters Follow Uncovered Interest Rate Parity? (Bürgi and Song)
The FX forecasts of professional forecasters strongly deviate from uncovered interest rate parity.
Equity
How Important are Ex-Ante Idiosyncratic Risks for the Return on a Stock? (Ulrich and Ni)
Stock-specific risks are shown to be significant predictors of stock returns, being more effective than broader market risks.
Replicating Anomalies Using the Aggregate Average Method (Guo et al.)
Averaging across different approaches when constructing anomaly portfolios mitigates p-hacking concerns.
This study finds a significant value premium in certain Russell 1000 stocks, challenging the notion that value investing has lost its edge.
Is Smaller Better? Examining the Decrease in Trade Sizes in Financial Markets (Coccia et al.)
This study examines how decreasing trade sizes and increasing odd-lot trades in financial markets affect volatility and price discovery.
Machine Learning and Large Language Models
Machine Learning in Portfolio Decisions (Guidolin et al.)
This is a comprehensive survey paper on machine learning applications in portfolio optimization, trading strategies, sentiment analysis, and more.
Battle of Transformers: Adversarial Attacks on Financial Sentiment Models (Can Turetkan and Leippold)
The FinBERT and Fin-GPT models are found to be highly vulnerable to adversarial attacks.
Forecasting Day-Ahead Eurusd Tail Risk: Leveraging Machine Learning and the Volatility Surface (Risstad et al.)
Combining information from implied volatility with machine learning models outperforms traditional models in predicting EURUSD tail risks.
AI in Investment Analysis: LLMs for Equity Stock Ratings (Papasotiriou et al.)
This study finds that large language models, when provided with specific combinations of financial data, can outperform traditional analyst ratings in predicting stock returns over certain time horizons.
TradExpert: Revolutionizing Trading with Mixture of Expert LLMs (Ding et al.)
The authors propose a framework for combining multiple large language models, finding promising results in terms of return predictability.
Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models (Elahi and Taghvaei)
The authors combine financial news with corporate filings as input to large language models and find some evidence of stock return predictability.
Volatility
Volatility Forecasting in Global Financial Markets Using TimeMixer (Li)
The author finds that the TimeMixer model performs well in predicting short-term volatility across different asset classes.
Blogs
Understanding the Invisible Tail of a Power Law (Quant at Risk)
GitHub
Medium
The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 2) (Nóbrega)
A Mean Reversion Strategy with 2.11 Sharpe (Murtazin)
Choosing and Implementing Hugging Face Models (Kirmer)
Podcasts
Bonds: The Voldemort of Asset Allocation ft. Andrew Beer ft. Andrew Beer (Top Traders Unplugged)
A Deep Dive into Micro-Cap Investing | Ian Cassel (Excess Returns)
Meb Faber, Founder and CEO, Cambria Investment Management (Meb Faber)
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