Weekly Recap
Latest 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!
Derivatives
Cross-Asset Liquidity Transmission (Brogaard et al.)
This paper studies how derivatives trading impacts the underlying market and finds that shocks in the derivatives market spread to stock markets as market makers hedge their positions.
Equities
ETF flows and the index effect (Howard)
This paper explores how ETFs impact the so-called index effect, the fact that stocks that are added (deleted) from an index experience positive (negative) abnormal returns.
Long-History PCA in a Dynamic Factor Model with Weak Loadings (Anderson et al.)
The authors consider a principal component approach with long histories of data, yielding more precise factor estimates and improved forecasts of risk.
Global Fund Flows: What They Reveal About Global Factors (Huynh et al.)
This paper explores the determinants of global equity fund flows and finds that fund alphas based on CAPM are more predictive of flows than alphas from the Fama-French three or four factor models.
New measures for forecasting extreme downside risk (Thomas)
This paper proposes new measures of the likelihood of crashes in individual stocks, finding that they outperform existing measures and are useful in an investment strategy.
Robust Betas in Asset Management (Bailer et al.)
This is a chapter from the Oxford Handbook of Quantitative Asset Management on the potential impact of outliers on estimation of equity betas.
Unveiling Mutual Funds’ Securities Lending Strategies: Value versus Volume (Chen et al.)
This paper proposes a method to discern whether the shorting of a stock is due to general hedging demand or due to negative firm-specific information and finds that shorting activity only predicts returns in the latter case.
Industry Effects in Beta Hedging (Schmidt)
This paper explores potential performance differences of hedging a stock based on CAPM vs. hedging with a two factor model (TFM) which incorporates a sector beta.
Machine Learning and Large Language Models
Interpretable GenAI: Synthetic Financial Time Series Generation with Probabilistic LSTM (Schwarz)
This paper presents a method for producing synthetic market data using a Long Short-Term Memory model, and which outperforms more traditional approaches.
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors (Shi et al.)
A neural-network model is presented that automatically mines and combines equity factors, and which shows promise for trading purposes.
LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies (Kashif and Ślepaczuk)
The authors build a trading strategy by combining ARIMA and LSTM models and apply the model to global equity indices, generating higher risk adjusted returns than buy-and-hold, net of costs.
Market Microstructure
Optimizing Broker Performance Evaluation through Intraday Modeling of Execution Cost (Eisler and Muhle-Karbe)
The paper presents methods to evaluate broker performance by modeling execution costs, improving cost estimation accuracy.
Timing is Money: Limit Order Cancellation and Investment Performance (Kuo et al.)
Investors can enhance returns by swiftly canceling stale limit orders, thereby reducing risks and missed opportunities in volatile markets.
Portfolio Allocation
Portfolio optimisation: bridging the gap between theory and practice (Arbex Valle)
The paper proposes a two-stage optimization framework to create practical investment portfolios by integrating realistic constraints.
Investors often make costly errors when investing in hedge funds by applying traditional methods of performance evaluation.
Volatility
Volatility Surfaces and Expected Option Returns (Höfler)
The paper demonstrates that using deep learning techniques, specifically Convolutional Neural Networks, can predict delta-hedged option returns, yielding meaningful profits.
It Takes Three to Hedge (Rolloos)
The paper demonstrates a method to hedge volatility and variance swaps using three-strike vanilla options, independent of specific volatility models.
A simple responsive covariance matrix forecaster for multiple horizons and asset classes (Guijarro-Ordonez et al.)
The authors propose a straightforward method for forecasting covariance matrices that improves accuracy and stability across various asset classes and horizons.
Forecasting the Worst: Is Implied Volatility Forward-Looking Enough? (Confalonieri and De Vincentiis)
This paper explores the potential benefits of using implied volatility in computing Value-at-Risk and the impact of different market conditions.
Blogs and Podcasts
Macro factors and sectoral equity allocation (Macrosynergy)
Volatility Forecasting: HAR Model (Portfolio Optimizer)
Bill Gebhardt - Replicating Discretionary Commodity Trading Systematically (Flirting with Models)
Consistent and Confident Trading - Laurens Bensdorp (Better System Trader)
The Negative Crisis Beta and CTA's Hidden Market Timing Skills ft. Nick Baltas (Top Traders Unplugged)
Show Us Your Portfolio: Eric Crittenden (Excess Returns)
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