Stock Return Predictability: A Factor-Augmented Predictive Regression System with Shrinkage Method

Saburo Ohno, Tomohiro Ando

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

To predict stock market behaviors, we use a factor-augmented predictive regression with shrinkage to incorporate the information available across literally thousands of financial and economic variables. The system is constructed in terms of both expected returns and the tails of the return distribution. We develop the variable selection consistency and asymptotic normality of the estimator. To select the regularization parameter, we employ the prediction error, with the aim of predicting the behavior of the stock market. Through analysis of the Tokyo Stock Exchange, we find that a large number of variables provide useful information for predicting stock market behaviors.

Original languageEnglish
Pages (from-to)1-43
Number of pages43
JournalEconometric Reviews
DOIs
Publication statusAccepted/In press - 2015

Fingerprint

Shrinkage
Stock return predictability
Factors
Stock market
Predictive regressions
Market behavior
Prediction error
Regularization
Return distribution
Financial variables
Tokyo Stock Exchange
Asymptotic normality
Expected returns
Estimator
Economic variables
Variable selection

Keywords

  • Common factor
  • High-dimensional predictors
  • Model selection
  • Quantiles
  • Regularization

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Stock Return Predictability : A Factor-Augmented Predictive Regression System with Shrinkage Method. / Ohno, Saburo; Ando, Tomohiro.

In: Econometric Reviews, 2015, p. 1-43.

Research output: Contribution to journalArticle

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