Maximizing the Sharpe Ratio: A Genetic Programming Approach

Yang, Liu; Zhou, Guofu; Zhu, Yingzi

doi:10.2139/ssrn.3726609

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…We consider three dimension-reduction techniques: The first takes the cross-sectional average of the individual predictors, the second extracts the first principal component from the set of predictors Ng (2007, 2009)), and the third, an implementation of partial least squares (PLS; Wold (1966)), extracts the first target-relevant factor from the set of predictors Pruitt (2013, 2015), Huang et al (2015)). As indicated previously, the forecasts based on strategies 3 Our study complements recent studies that employ machine-learning techniques to predict stock returns using alternative predictor variables in high-dimensional settings, including Rapach, Strauss, and Zhou (2013); Chinco, Clark-Joseph, and Ye (2019); Rapach et al (2019);Freyberger, Neuhierl, and Weber (2020); Gu, Kelly, and Xiu (2020); Kozak, Nagel, and Santosh (2020); Avramov, Cheng, and Metzker (2021); Chen, Pelger, and Zhu (2021); Cong et al (2021); Han et al (2021);and Liu, Zhou, and Zhu (2021).…”

supporting

confidence: 52%

Anomalies and the Expected Market Return

Dong¹,

Li²,

Rapach³

et al. 2021

The Journal of Finance

Self Cite

122

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We provide the first systematic evidence on the link between long-short anomaly portfolio returns-a cornerstone of the cross-sectional literature-and the timeseries predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that longshort anomaly portfolio returns evince statistically and economically significant outof-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence. STOCK RETURN PREDICTABILITY IS A fundamental topic in finance. Two major-and voluminous-lines of research focus on this subject. The first examines whether firm characteristics can predict the cross-sectional dispersion in stock returns. These studies identify numerous equity market anomalies (e.g., Fama and French (2015), Harvey, Liu, and Zhu (2016), Pontiff (2016), Hou, Xue, andZhang (2020)). The second line of research investigates the time-series predictability of the aggregate market excess return based on a variety of economic and financial variables, such as valuation ratios, interest rates, and inflation (e.g., Nelson (1976), Campbell (1987, Fama and French

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supporting

confidence: 52%

Anomalies and the Expected Market Return

Dong¹,

Li²,

Rapach³

et al. 2021

The Journal of Finance

Self Cite

122

View full text Add to dashboard Cite

show abstract

“…It reduces overfitting in a complex model with many predictors and helps mitigate the problem of multicollinearity. LASSO regression automates the variable selection process in multiple linear models by penalizing large coefficients in absolute values [12].…”

Section: Lasso Regressionmentioning

confidence: 99%

Portfolio Allocation with Time Series and Machine Learning Models

Liao¹

2023

HBEM

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The historical average stock return is commonly used to predict expected stock return in portfolio management for its better prediction and more straightforward calculations. However, most regressive models empirically proved to underperform the historical average return are explanatory models using predictor variables. This article takes the challenge to further explore the regressive power in predicting stock return but focuses on time series (TS) forecasting models and machine learning (ML) models. The results of this article show that many of these TS and ML regression models beat the historical average return, delivering higher portfolio Sharpe ratios and realized returns in backtesting. Although TS and ML models have inherently weaker explanatory power for their predictions, these results are still meaningful for retail and institutional mean-variance investors, opening up a new angle to forecast expected returns in portfolio allocation.

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“…Given the choice of target return, most ML studies focus on minimizing a loss function such as the mean squared error between predicted and realized returns. Wang et al (2019) and Liu, Zhou, and Zhu (2021) propose alternative ML methods that attempt to directly maximize portfolio riskadjusted returns and find that these give superior Sharpe ratios. Standard loss functions may result in top-minus-bottom quintile portfolios that are not necessarily optimal from a Sharpe ratio perspective.…”

Section: Choice Of Targetmentioning

confidence: 99%

How Can Machine Learning Advance Quantitative Asset Management?

Blitz¹,

Hoogteijling²,

Lohre³

et al. 2023

JPM

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The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and pitfalls of applying machine learning to asset management, by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.

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Maximizing the Sharpe Ratio: A Genetic Programming Approach

Cited by 8 publications

References 49 publications

Anomalies and the Expected Market Return

Anomalies and the Expected Market Return

Portfolio Allocation with Time Series and Machine Learning Models

How Can Machine Learning Advance Quantitative Asset Management?

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