Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links to the latest research on quant investing and useful resources. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
Bonds
Monetary policy and fragility in corporate bond mutual funds (Kuong et al.)
This study reveals how changes in central bank interest rates can unexpectedly destabilize certain investment funds, particularly during challenging market conditions.
Do Stocks Lead Bonds? New Evidence from Corporate Bond Etfs (Jiang et al.)
Lagged stock returns of firms issuing bonds are shown to predict short-term returns on bond ETFs containing those firms’ bonds.
Crypto
Early Warning Systems for Cryptocurrency Markets: Predicting 'Zombie' Assets Using Machine Learning (Bedowska-Sojka et al.)
The study uses machine learning to predict which cryptocurrencies will become inactive, achieving a high accuracy with random forest and significant predictors like trading volume.
Equities
Is There Alpha in Analyst Forecasts? (QuantSeeker)
In a new longer-form article, I review recent research on analyst forecasts and return predictability and find several intriguing potential sources of alpha.
Stealthy Shorts: Informed Liquidity Supply (Goyal et al.)
Short sellers who provide liquidity to the market appear to possess more valuable information about future stock performance than those who demand it.
What is the value of retail order flow? (Hoffman and Jank)
The paper analyzes the profitability of retail market making in Germany, finding an average Sharpe ratio of almost 18, substantially higher than that of prop-trading firms.
Categorical Factors for Asset Pricing (Zhao et al.)
The paper finds that grouping financial characteristics into categories and combining nonlinear feature extraction with linear pricing models can enhance both the accuracy and interpretability of asset pricing models.
Cross-Firm Information in Analyst Reports (Miwa)
The author shows how analyst revisions for one company can predict future analyst revisions and returns on related companies, suggesting inefficiencies and delays in information processing.
The study finds that technical trading rules generally do not outperform simple buy-and-hold strategies, especially when accounting for transaction costs.
Investing
The Less-Efficient Market Hypothesis (Asness)
Financial markets have become less efficient over time, presenting both greater opportunities and challenges for disciplined long-term investors.
The Risk and Reward of Investing (Doeswijk and Swinkels)
A very broad market portfolio across asset classes exhibits a similar Sharpe ratio to global equities, but with improved drawdowns.
Machine Learning and Large Language Models
Machine Learning in Enhancing the Performance of Prediction and Trading Strategies in FX Markets (Lin and Liu)
This paper demonstrates how machine learning can enhance currency forecasting and trading strategies, outperforming traditional methods across various timeframes and market conditions.
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors (Shi et al.)
The authors present a framework for extracting and optimally combining alpha factors, applied on Chinese stocks and with results from live trading.
Macro
Inflation Tail Risk (Luber)
The study finds that inflation tail risks significantly influence stock returns and credit spreads, with stocks acting as a hedge against inflation.
Macroeconomic modelling of CBDC: a critical review (Bindseil and Senner)
This is an extensive review of central bank digital currencies (CBDCs) that critically examines existing macroeconomic models and their assumptions about CBDC design and implementation.
Mutual Funds
A Skew is a Skill: Portfolio Skewness of Mutual Fund Holdings (Drienko et al.)
The return skewness of the holdings of mutual funds is shown to predict fund returns positively while the time-series skewness of the fund's own returns is not predictive of future performance.
Options
Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates (Hao et al.)
The authors present an option pricing model, integrating the Vasicek, Heston, and Campbell-Viceira models into the Black-Scholes model.
Portfolio Optimization
Portfolio optimisation using alternative risk measures (Lorimer et al.)
The study evaluates various risk measures for portfolio optimization, finding that asymmetric and unsquared deviation measures enhance risk-adjusted performance.
Volatility
Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning (Taylor)
The author explores the ability of machine learning models to predict the implied volatility of credit default swaps on European corporate debt.
Direct and Indirect Volatility Timing Strategies (Xie)
The paper compares the effectiveness of using volatility forecasts versus directly forecasting realized portfolio weights for determining weights in a Global Minimum Variance Portfolio.
Blogs
Coding live forward tests (Quantitativo)
GitHub
Podcasts
Giuseppe Paleologo - Multi-Manager Hedge Funds & Thinking Deeply About Simple Things (Flirting with Models)
BREAKING: New CTA Index being revealed ft. Andrew Beer (Top Traders Unplugged)
Jordan Doyle and Genevieve Hayman: The Rise of Index-Based Strategies (Enterprising Investor)
J. Doyne Farmer on Making Sense of Chaos (Macro Hive Conversations)
Laurens Bensdorp - Balancing 55 Supermodels (The Algorithmic Advantage)
Overcrowded Trades: What Everyone Else is Not Seeing w/ Jason Shapiro (Chat with Traders)
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