Sparse Signals in the Cross‐Section of Returns

By Alexander M Chinco, Adam D. Clark-Joseph, Mao Ye. Journal of Finance, 2018

215 citations recorded by the source corpus.

This paper uses a penalized regression known as the LASSO to identify rare, short-lived, “sparse” signals in the cross-section of returns. The LASSO boosts out-of-sample predictability in one-minute returns by 23% relative to OLS regressions. This out-of-sample predictive power comes from quickly identifying the right predictors at the right time, not from better estimating the effects of some new, persistent factor. The sparse signals that the LASSO identifies are associated with subsequent news announcements: the lagged returns of stocks with announcements are 18.3% more likely to be used by the LASSO as a predictor during the previous 30 minutes.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5d822abc07dbc43acd794fa1c705683515f5252b

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In Economics and Econometrics

1. Fotios Petropoulos
2. Konstantinos Nikolopoulos
3. Rob J Hyndman
4. Nikolaos Kourentzes
5. Vassilios Assimakopoulos
6. Evangelos Spiliotis
7. Spyros Makridakis
8. Christoph Bergmeir

In Finance

1. David F. Hendry
2. David E. Rapach
3. Michael P. Clements
4. Jennifer L. Castle
5. Feng Li
6. Xiaoqian Wang
7. Yanfei Kang
8. Allan G. Timmermann

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Konstantinos Nikolopoulos
4. Nikolaos Kourentzes
5. Vassilios Assimakopoulos
6. Spyros Makridakis
7. Evangelos Spiliotis
8. Christoph Bergmeir

Citations in the corpus

• Dissecting Characteristics Nonparametrically (2020, unknown): “I Introduction In his presidential address, Cochrane (2011) argues the “cross section” of the expected return is in disarray.”
• Machine Learning Advances for Time Series Forecasting (2020, methodology): “Chinco et al. (2019) use the LASSO to make 1-minute-ahead return forecasts for a vast set of stocks traded at the New York Stock Exchange.”
• On LASSO for predictive regression (2018, unknown): “In comparison with the extensive literature in cross-sectional environment, theoretical properties of shrinkage methods are less explored in time series models in spite of great empirical interest in macro and financial applications (Chinco et al., 2019; Giannone et al., 2018; Gu et al., 2019).”
• Forecasting: theory and practice (2020, methodology): “selection operator (LASSO, Tibshirani, 1996). The LASSO and ENet employ penalised regression to guard against overfitting in high-dimensional settings by shrinking the parameter estimates toward zero. Chinco et al. (2019) use the LASSO to forecast high-frequency (one-minute-ahead) individual stock re”