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!
Currencies
Global Currency Variance Risk and Currency Return Predictability (Li et al.)
Global measures of currency at-the-money implied volatility and realized variance are found to positively predict currency returns out-of-sample.
Global Uncertainty and the Cross-Section of FX Returns (Bosnic)
Global economic uncertainty is shown to price the cross-section of currency returns and is associated with a significant risk premium.
Empirical Asset Pricing
Predicting the Equity Premium with A High-Threshold Risk Level and the Price of Risk (Bansal and Stivers)
Market returns are found to be high after the VIX has exceeded its 80th percentile and low after periods of high investor sentiment, and used for predicting returns out-of-sample.
Retail Investors and Analysts (McLean et al.)
Retail investors are found to react quite strongly to recommendation changes and trade in the direction of those changes, with an even stronger effect when the recommendation is issued by an All-Star analyst.
Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2024 Edition (Damodaran)
The 2024 update of Aswath Damodaran’s paper on the equity risk premium, containing a lengthy discussion of the determinants of the equity risk premium and how to measure it.
3D-PCA: Factor Models with Restrictions (Lettau)
This paper introduces a three-dimensional PCA factor model that utilizes multidimensional panel data, and whose factors have significantly higher Sharpe ratios than standard factor models.
Trended Momentum (Cai et al.)
Stocks with smoother price paths are more likely to experience a return continuation.
Most claimed statistical findings in cross-sectional return predictability are likely true (Chen)
The paper argues that most statistical findings in cross-sectional stock return predictability are accurate, challenging previous claims of high false discovery rates.
The author develops a five-step risk premium estimator, yielding estimates of continuous, jump, and overnight risk premia.
Machine Learning and Large Language Models
Long Short-Term Memory Pattern Recognition in Currency Trading (Pal)
This paper utilizes convolutional networks and LSTM models to detect Wykov patterns in FX data.
Improving Merger Arbitrage Returns with Machine Learning (Halskov)
This recent paper applies machine learning to merger arbitrage, estimating the conditional probability of deal success, and outperforming more conventional approaches.
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies (Wood et al.)
The paper introduces a trend-following model that swiftly adapts to changing market conditions, producing a meaningful increase in Sharpe ratios compared to standard trend strategies.
Robust Rolling Regime Detection (R2-RD): A Data-Driven Perspective of Financial Markets (Hirsa et al.)
A new framework for detecting regime shifts is introduced, identifying nonstationarities and regime shifts in macro and futures data.
Market Microstructure
Limit Order Book Simulations: A Review (Jain et al.)
This paper reviews the literature on various types of limit order book simulators and discusses stylized facts regarding order book dynamics.
Jump Detection in High-frequency Order Prices (Bibinger et al.)
The authors introduce methods for estimating and testing for price jumps, finding improved jump detection in limit order book data.
Options/Volatility
Lever up! An analysis of options trading in leveraged ETFs (Gilstrap et al.)
This paper finds that changes in the implied volatility of options on ETFs predict returns on the underlying ETF, and a trading strategy is proposed.
Volatility-Managed Volatility Trading (Yang)
This study applies volatility timing to volatility trading in the forms of variance swaps, VIX futures and straddles and is shown to increase Sharpe ratios and reduce negative skewness.
Option-driven volatility forecasting (Michael et al.)
This paper augments the Heterogeneous Autoregressive Regression (HAR) model model by incorporating information from option prices and finds improvements in forecasting daily volatilities.
Option Trading Volume and the Cross-Section of Option Returns (Yuan et al.)
Option volume is found to be a significant predictor of delta-hedged option returns, producing meaningful returns in a trading strategy.
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