Scaled PCA: A New Approach to Dimension Reduction

21 Pages Posted: 14 May 2019

See all articles by Dashan Huang

Dashan Huang

Singapore Management University - Lee Kong Chian School of Business

Fuwei Jiang

Central University of Finance and Economics (CUFE)

Guoshi Tong

Renmin University

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Date Written: March 23, 2019

Abstract

We propose a novel modification to the popular principal component analysis (PCA) by scaling each predictor according to its predictive power on the target to be forecasted. Unlike the PCA that maximizes the variations of predictors, our scaled PCA, s-PCA, identifies factors that are particularly useful for forecasting the target. Asymptotically, the s-PCA factors converge to true latent factors that are important for the target. Empirically, we find that the s-PCA outperforms the popular PCA substantially in forecasting market return with a variety of investor sentiment proxies and forecasting inflation with a large panel of macro variables.

Keywords: Forecasting, PCA, Big Data, Machine Learning, Supervised Learning

JEL Classification: C22, C23, C53

Suggested Citation

Huang, Dashan and Jiang, Fuwei and Tong, Guoshi and Zhou, Guofu, Scaled PCA: A New Approach to Dimension Reduction (March 23, 2019). Available at SSRN: https://ssrn.com/abstract=3358911 or http://dx.doi.org/10.2139/ssrn.3358911

Dashan Huang

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
Singapore, 178899
Singapore

HOME PAGE: http://dashanhuang.weebly.com/

Fuwei Jiang

Central University of Finance and Economics (CUFE) ( email )

39 South College Road
Haidian District
Beijing, Beijing 100081
China

Guoshi Tong

Renmin University ( email )

59 Zhongguancun Street
Beijing, 100872
China

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

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