Weekly Recap
Research on investing, macro, 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!
Empirical Asset Pricing
News-based Investor Disagreement and Stock Returns (Li and Luan)
The paper develops a new method to measure investor disagreement using trading data, finding that higher disagreement predicts lower future stock returns.
Information Transmission in Stock and Bond Markets (Hameed et al.)
The paper examines the predictive relationship between stock and corporate bond returns, finding that bond returns often lead stock returns.
Above Up, Below Down: The Impact of Limit Order Clustering on Stock Price Movements (Zhang)
The author finds academic support for the existence of support and resistance levels, due to limit order clustering at round numbers.
Uncertain Firm Profits and (Indirectly) Priced Idiosyncratic Volatility (Parajuli et al.)
The authors present a model explaining the negative relationship between firm-specific risk and expected returns, emphasizing low profitability and high uncertainty.
Machine Learning and Natural Language Processing
Can Machines Learn Weak Signals? (Shen and Xiu)
The paper evaluates machine learning algorithms' ability to detect weak signals, finding Ridge regression superior to Lasso.
Generative AI for European Asset Pricing: Alleviating the Momentum Anomaly (Mattusch)
A generative AI asset pricing model for European stocks is introduced, that yields a sizeable Sharpe ratio compared to benchmark models.
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing (Ye et al.)
This paper reviews the literature on machine learning in asset pricing, focusing on return prediction and portfolio optimization.
Options
Option Expected Hedging Demand (Tang et al.)
This study introduces a method using real-time option data to predict stock returns by analyzing expected hedging demand and its effects on stock prices.
Optimal Option Market Making and Volatility Arbitrage (Lucic and Tse)
The paper presents a model for option market making that optimizes bid and ask quotes by considering the market maker’s view on volatility.
Portfolio Optimization
Overcoming Markowitz's Instability with the Help of the Hierarchical Risk Parity (HRP): Theoretical Evidence (Antonov et al.)
The authors compare the classical Markowitz approach to portfolio optimization to that of the clustering approach of hierarchical risk parity (HRP).
Statistical Arbitrage
Finding Moving-Band Statistical Arbitrages Convex-Concave Optimization (Johansson et al.)
Rather than traditional pairs trading, this paper focuses on identifying a mean-reverting portfolio of multiple assets.
Volatility
The daily rise and fall of the VIX1D: Causes and solutions of its overnight bias (Albers and Kestner)
This paper explores the dynamics of the 1-day volatility index, VIX1D, and shows how it differs markedly from the dynamics of longer-term VIX indices.
Log-normal stochastic volatility model with quadratic drift (Sepp and Rakhmonov)
The authors present a closed-form solution for vanilla options under the log-normal stochastic volatility model, calibrated to Bitcoin options.
Quant Blogs
Fitting with: exponential weighting, alpha and the kitchen sink (Rob Carver)
Getting Started with the Interactive Brokers Native API (Robot Wealth)
A Two-Factor Model for Capturing Momentum and Mean Reversion in Stock Returns (Jonathan Kinlay)
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