Taming the Factor Zoo: A Test of New Factors

Feng, Guanhao; Giglio, Stefano; Xiu, Dacheng

doi:10.1111/jofi.12883

Cited by 491 publications

(190 citation statements)

References 144 publications

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“…In the search for factors that explain the cross-sectional expected stock returns, numerous potential candidates have been found by using econometric models. For example, accounting data, macroeconomic data, and news [16,17,18,19,20]. Stock price predictions that consider a few pre-specified factors may lead to incorrect forecasting as they reflect partial information or an inefficient combination of the factors.…”

Section: Connections With Previous Studiesmentioning

confidence: 99%

Multimodal deep learning for finance: integrating and forecasting international stock markets

Lee

Yoo

2019

J Supercomput

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In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, information regarding foreign markets is one of the most important factors in forecasting domestic stock prices. However, the cross-correlation between domestic and foreign markets is highly complex. Hence, it is extremely difficult to explicitly express this cross-correlation with a dynamical equation. In this study, we develop stock return prediction models that can jointly consider international markets, using multimodal deep learning. Our contributions are three-fold: (1) we visualize the transfer information between South Korea and US stock markets by using scatter plots; (2) we incorporate the information into the stock prediction models with the help of multimodal deep learning; (3) we conclusively demonstrate that the early and intermediate fusion models achieve a significant performance boost in comparison with the late fusion and single modality models. Our study indicates that jointly considering international stock markets can improve the prediction accuracy and deep neural networks are highly effective for such tasks.

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Section: Connections With Previous Studiesmentioning

confidence: 99%

Multimodal deep learning for finance: integrating and forecasting international stock markets

Lee

Yoo

2019

J Supercomput

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“…Our paper is also closely related to recent literature on high dimensionality of cross-sectional asset pricing models. Feng, Giglio, and Xiu (2019) provide a high-dimensional inference method to tame the factor zoo for independent risk price. Gu, Kelly, and Xiu (2018) present a comprehensive empirical investigation of forecasting performance for multiple machine learning algorithms, whereas Han, He, Rapach, and Zhou (2018) give a forecast combination approach on the same characteristics library.…”

Section: Our Unconditional Predictive Regression Contains Both Fundammentioning

confidence: 99%

Factor Investing: Hierarchical Ensemble Learning

Feng

2019

SSRN Journal

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“…Given that the two streams of literature have attracted a large amount of attention, we consider both exercises. Specifically, we use the signal return-on-equity as a proxy for the expected return, which has been proven to have statistically significant explanatory power for cross-sectional anomalies (Feng et al, 2020). For robustness check, we consider the widely-used signal earnings-to-price as an alternative proxy.…”

Section: Introductionmentioning

confidence: 99%

Risk Reduction and Efficiency Increase in Large Portfolios: Leverage and Shrinkage

2019

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We investigate the effects of constraining leverage and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller vs. larger universe of stocks, alternative portfolio formation objectives (Global Minimum Variance vs. exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously-chosen shrinkage method always outperforms an arbitrarily-determined leverage constraint. By extending the mathematical connection between leverage and shrinkage from static to dynamic, we provide a new theoretical explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the DCC-NL estimator results in risk reduction and efficiency increase in large portfolios as long as a small amount of leverage is allowed, whereas tightening the leverage constraint often hurts a DCC-NL portfolio.

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Taming the Factor Zoo: A Test of New Factors

Cited by 491 publications

References 144 publications

Multimodal deep learning for finance: integrating and forecasting international stock markets

Multimodal deep learning for finance: integrating and forecasting international stock markets

Factor Investing: Hierarchical Ensemble Learning

Risk Reduction and Efficiency Increase in Large Portfolios: Leverage and Shrinkage

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