Weekly Recap
Research on quant investing 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!
Bonds
FOMC Announcements and Secular Declines in Global Interest Rates (Li)
Global yield declines are primarily linked to U.S. FOMC announcements, which are predictive of bond returns.
Is firm-level political risk priced in the corporate bond market? (Ceballos et al.)
Political risk is priced in the cross-section of US corporate bond returns, and sorting portfolios on shocks to political risk yields a statistically significant return spread.
The Determinants of Corporate Bond Returns - a Literature Review (Mansouri and Eterovic)
This is a survey paper on the empirics of corporate bond returns, cross-sectional factors, and on the predictability of returns.
Commodities
Recovering from Shocks: Term Structure Signalling in Commodity Markets (Adams et al.)
The authors study how term structures in commodity markets evolve after price shocks, finding the recovery time after a shock to be predictable as well as future returns.
Currencies
Political uncertainty and currency markets (Leippold et al.)
The at-the-money implied volatility of short-dated FX options is documented to significantly increase during election weeks.
Forecasting bid-ask spreads in foreign exchange: Analysis and machine learning prediction (Kirkby and Andrean)
The authors discuss patterns and time-variation in FX bid-ask spreads, evaluating a range of machine-learning techniques for predicting spreads.
Derivatives and Vol
0DTE Asset Pricing (Almeida et al.)
This paper presents a range of new stylized facts for the 0DTE option market.
Salience theory and equity option returns (So and Zhang)
Focusing on delta-hedged returns, the paper finds evidence that salience theory predicts option returns in the cross-section.
Hedging Market Tail Risk Factors with Volatility Indices (Merlo)
This paper evaluates the ability of VIX options and futures to hedge against tail risk, and reviews the recently launched 1-day VIX.
Volatility of Volatility and Leverage Effect from Options (Chong and Todorov)
The paper proposes a method to estimate volatility changes and leverage effects using short-term options data, enhancing accuracy over traditional methods.
ESG and Derivatives (Janardanan et al.)
This paper discusses how ESG considerations can be applied to derivatives and presents an example of applying ESG scoring to commodity futures.
Equities
From Extinction to Resurrection: Unveiling the Role of Short-Selling Frictions in Cross-Sectional Momentum (Buraschi and Pellegrino)
The standard 12-month cross-sectional momentum effect in stocks has weakened in recent decades; however, this paper finds the effect is resurrected when controlling for short interest.
Basis portfolios (Seyfi)
The paper proposes a method to create diversified portfolios by grouping stocks with similar characteristics, enhancing Sharpe ratios.
Following the footprints: Towards a taxonomy of the factor zoo (Böll et al.)
The paper develops a metric to identify stock mispricing by analyzing option trading volumes.
Optimists, Pessimists and Stock Prices (Daniel et al.)
This paper offers a great overview of the empirics and theory of investor disagreement and its effect on stock prices.
Machine Learning and Large Language Models
Multi-factor timing with deep learning (Cotturo et al.)
The paper presents a deep learning model that effectively predicts stock factor performance by integrating economic constraints and time series dynamics.
Cyber risk and the cross-section of stock returns (Celeny and Maréchal)
The paper uses machine learning and company filings to compute firm-specific cyber risk, where going long (short) firms with high (low) cyber risk generates a significant premium.
Stock picking and machine learning (Wolf and Echterling)
This paper considers a range of machine learning algorithms for predicting weekly relative performance.
Asset pricing and machine learning: A critical review (Bagnara)
This paper reviews work in empirical asset pricing and machine learning and offers a great overview of the literature.
Predicting Bitcoin returns by machine learning (Li et al.)
Using more than 30 different features, this paper considers a range of machine learning algorithms for predicting Bitcoin returns.
A Survey of Large Language Models in Finance (FinLLMs) (Lee et al.)
This is a survey paper, reviewing large-language models in the financial domain.
Portfolio Optimization
Higher-order financial networks (Zhao et al.)
The authors evaluate various complexity indicators for higher-order financial networks and find them to be beneficial for both portfolio optimization and market timing.
An averaging framework for minimum-variance portfolios: Optimal rules for combining portfolio weights (Füss et al.)
The paper develops a framework for determining the optimal way of combining multiple portfolios, focusing on minimum-variance strategies.
Portfolio Optimization and Parameter Uncertainty (Kristensen and Vorobets)
This paper focuses on parameter uncertainty and resampled optimization, and a new technique called exposure stacking is introduced.
More Quant Stuff
High dimensional forecasting with known knowns and known unknowns (Pesaran and Smith)
This paper considers forecasting with high-dimensional data, covering in particular Lasso and OCMT and techniques for combining sparse and dense methods.
Regime-Aware Asset Allocation: A Statistical Jump Model Approach (Shu et al.)
A regime-dependent asset allocation strategy is considered where regimes are identified through a statistical jump model, outperforming both buy-and-hold and Markov-switching models.
Quant Blogs
A Simple, Effective Way to Manage Turnover and Not Get Killed by Costs (Robot Wealth)
Generic derivative returns and carry (for strategy testing) (Macrosynergy)
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