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. Thank you for reading!
Bonds
New Issuance Premium as a Corporate Bond Factor (Traczyk)
Newly issued corporate bonds in Europe are shown to outperform existing bonds, with returns being unexplained by existing corporate bond factors.
Currencies
Understanding the Performance of Currency Basis-Momentum (Fan et al.)
Currency basis momentum generates meaningful Sharpe ratios and statistically significant alphas when controlling for carry and momentum factors, although being related to them.
Derivatives
Perpetual Futures Pricing (Ackerer et al.)
This paper presents a comprehensive theoretical framework for pricing perpetual contracts.
Cross-Sectional Variation of Risk-targeting Option Portfolios (Wu and Xu)
This paper analyzes option return risk exposures and creates risk factor portfolios that effectively explain the cross-section of option returns, while also showing strong predictive power for future returns.
Pricing American Options using Machine Learning Algorithms (Djagba and Ndizihiwe)
The study explores various machine learning techniques for pricing American options, finding significant improvements over classical approaches.
Joint Calibration to SPX and VIX Derivative Markets with Composite Change of Time Models (Cheng et al.)
A novel approach to simultaneously model equity and volatility markets is proposed, enhancing pricing accuracy and interpretability.
Equities
Intrinsic Value: A Solution to the Declining Performance of Value Strategies (Bergen et al.)
The authors propose a new value measure that strongly outperforms traditional value measures such as book-to-market, yielding significant alphas.
Betting Against (Bad) Beta (Herculano)
This paper offers a different take on the betting-against-beta factor by double-sorting on betas and Campbell and Vuolteenaho's "bad beta", improving Sharpe ratios.
Social Media-Driven Noise Trading: Liquidity Provision and Price Revelation Ahead of Earnings Announcements (Lopez Avila et al.)
The paper examines how investor activity on social media platforms before earnings announcements can lead to mispricing and proposes a trading strategy that generates significant alphas.
Investing in Megatrends with Thematic Funds and ETFs (Carvalho)
The author discusses the potential opportunities and risks of thematic investing, particularly the need for proper risk management for managing unwanted risk exposures.
The Decay of Cay (Dauber and Lawrenz)
The authors highlight the deterioration in the predictive ability of the cay indicator of Lettau and Ludvigson (2001) and present new implementations that improve return predictability.
The Disappearing Index Effect (Greenwood and Sammon)
This study examines the evolution of market efficiency in response to index-driven demand shocks, highlighting how markets adapt over time.
Movements in Yields, not the Equity Premium: Bernanke-Kuttner Redux (Nagel and Xu)
The authors challenge previous findings by demonstrating that stock market reactions to monetary policy surprises primarily reflect changes in bond yields and not changes in the equity risk premium.
The Anatomy of Lost Stock Market Decades (Feldman and Yang)
The paper examines cycles in international stock markets, highlighting that long periods of stagnation are common and have implications for asset allocation.
Expected EPS × Trailing P/E (Ben-David and Chinco)
While theory suggests pricing an asset by discounting its future expected payoffs, this paper describes how sell-side analysts in practice set their price targets
ESG
Exporting Carbon Emissions? Evidence from Space (Kundu and Ruenzi)
Multinational firms shift emissions to regions with less stringent environmental regulations in response to carbon pricing policies.
An Introduction to Carbon Pricing: Carbon Tax, Cap & Trade, ETS and Internal Carbon Price (Dao et al.)
The authors offer a detailed look at carbon taxes, emissions trading, and internal corporate carbon pricing.
Machine Learning and Large Language Models
Measuring Misinformation in Financial Markets (Fan et al.)
Utilizing machine learning and large language models to find structure in huge amounts of text, the authors construct a novel measure of misinformation that is a significant predictor of future returns, volatility, and crash risk.
Pooling and Winsorizing Machine Learning Forecasts to Predict Stock Returns with High-Dimensional Data (Mekelburg and Strauss)
The paper highlights the power of ensembling across various machine learning models to achieve robust out-of-sample performance.
Representation learning for financial time series forecasting (Krymski et al.)
A framework for automatic feature generation is presented and shown to generate meaningful Sharpe ratios when applied to FX markets.
Finance-Specific Large Language Models: Advancing Sentiment Analysis and Return Prediction with Llama 2 (Chiu and Hung)
The authors fine-tune Llama 2 on a large dataset of financial and corporate texts to generate sentiment scores and find significant return predictability, outperforming competing approaches.
QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE (Zhao et al.)
A novel reinforcement learning algorithm for mining alpha factors is presented, outperforming previous methods when applied to Chinese and U.S. stocks.
Predicting Foreign Exchange EUR/USD direction using machine learning (Guyard and Deriaz)
This paper compares the ability of a range of machine learning models to predict EURUSD returns, finding promising results.
Regime-Switching Models
Regularised jump models for regime identification and feature selection (Selig and Bilokon)
This paper presents novel regularized jump models demonstrating improved performance over existing approaches in identifying market regimes and selecting relevant features.
Volatility
Volatility Forecasting with Machine Learning and Intraday Commonality (Zhang et al.)
This study employs machine learning models to forecast intraday realized volatility, harnessing commonality across stocks and revealing superior performance of neural networks.
Blogs
Statistical learning for sectoral equity allocation (Macrosynergy)
Are Professional Forecasters Overconfident? (Del Negro - New York Fed)
The Costless Disinflation of 2022-24 (Neely - St. Louis Fed)
Adding Leveraged, Long-Short Factor Strategies to Improve Tax Alpha (Alpha Architect)
GitHub
Systematic trading examples Rob Carver
Medium
Hands-On Global Optimization Methods, with Python (Piero Paialunga)
14 pandas tricks you MUST know (Jyoti Prakash Dey)
Step-by-Step Guide to Automating Price Channel Breakouts Using Python (Ziad Francis)
Podcasts
Should You Just Buy a CTA Index? ft. Rob Carver (Top Traders Unplugged)
Inside the Mind Of A Successful Trend Follower: A Conversation with Doug Greenig (Meb Faber Show)
The Harsh Truth About Alpha with Adam Butler (Excess Returns)
Bob Elliott on US Recession Odds, Fed Policy and Equity Risks (Macro Hive)
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