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  3. 2024
  4. Program
  5. Asset Pricing: Machine Learning
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Asset Pricing: Machine Learning

Paper Session

Saturday, Jan. 6, 2024 2:30 PM - 4:30 PM (CST)

Marriott Rivercenter, Grand Ballroom Salon A
Hosted By: American Finance Association
  • Chair: Serhiy Kozak, University of Maryland

Asset-Pricing Factors with Economic Targets

Svetlana Bryzgalova
,
London Business School
Victor Demiguel
,
London Business School
Sicong Li
,
London Business School
Markus Pelger
,
Stanford University

Abstract

We propose a novel method to estimate latent asset-pricing factors that incorporate economic structure. Our estimator generalizes principal component analysis by including economically motivated cross-sectional and time-series moment targets that help to detect weak factors. Cross-sectional targets may capture monotonicity constraints on the loadings of factors or their correlation with fundamental macroeconomic innovations. Time-series targets may reward explaining expected returns or reducing mispricing relative to a benchmark reduced-form model. In an extensive empirical study, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

Forecasting and Managing Correlation Risks

Tim Bollerslev
,
Duke University
Sophia Zhengzi Li
,
Rutgers University
Yushan Tang
,
Rutgers University

Abstract

We propose a novel and easy-to-implement framework for forecasting correlation risks based on a large set of salient realized correlation features and the sparsity-encouraging LASSO technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically superior out-of-sample forecasts compared to commonly used procedures. We further demonstrate how the forecasts translate into significant economic gains in the form of higher pairs trading profits, better equity premium predictions, more accurate portfolio risk targeting, and superior overall risk control and minimization.

Machine Learning and the Implementable Efficient Frontier

Theis Jensen
,
Yale University
Bryan Kelly
,
Yale University
Lasse Pedersen
,
Copenhagen Business School
Semyon Malamud
,
Swiss Federal Institute of Technology Lausanne

Abstract

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the ``implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of ``economic feature importance."

Maximizing the Sharpe Ratio: A Genetic Programming Approach

Yang Liu
,
Hunan University
Guofu Zhou
,
Washington University-St. Louis
Yingzi Zhu
,
Tsinghua University

Abstract

While existing studies focus on minimizing model errors, we consider maximizing the Sharpe ratio of investing in the usual spread portfolio. In contrast to popular machine learning methods, we find that GP can double their performance in the US, and outperform them internationally, because GP captures nonlinearity in comparison with linear methods like the LASSO and it requires smaller sample size than the nonlinear neural network. We also apply GP to maximize the Sharpe ratio of all the underlying stocks, and find that its value is 60% greater than before, indicating the loss of relying on spread portfolios can be substantial.

Discussant(s)
Andreas Neuhierl
,
Washington University-St. Louis
Nicola Fusari
,
Johns Hopkins University
Andrew Chen
,
Federal Reserve Board
Serhiy Kozak
,
University of Maryland
JEL Classifications
  • G1 - General Financial Markets