Scaled PCA: A New Approach to Dimension Reduction

50 Pages Posted: 14 May 2019 Last revised: 2 Apr 2020

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)

Kunpeng Li

Capital University of Economics and Business

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 with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of predictors, our scaled PCA, sPCA, puts more weights on those predictors that have stronger forecasting power. Asymptotically, we provide a set of sufficient conditions under which the sPCA forecast outperforms the PCA and partial least squares (PLS) forecasts. Simulated and real data show that the sPCA forecast outperforms the PCA forecast in general, and performs similarly as, and in some cases better than, the PLS forecast.

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

JEL Classification: C22, C23, C53

Suggested Citation

Huang, Dashan and Jiang, Fuwei and Li, Kunpeng 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

Kunpeng Li

Capital University of Economics and Business ( email )

Beijing
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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