Professor Guoshi Tong’s Paper is Awarded | Heavyweight

Release time：2025-10-30    

Recently, FISF Professor Guoshi Tong’s paper, entitled Scaled PCA: A New Approach to Dimension Reduction, was awarded the second prize in the Thesis Category of the Academic Achievement Award (Disciplinary) under the 17^th Shanghai (2022-2023) Outstanding Achievement Awards in Philosophy and Social Sciences.

The paper conducts a profound exploration, from both theoretical and empirical perspectives, of the predictive superiority of scaled principal component analysis over traditional principal component analysis, and was published on Management Science in 2022, a journal boasting an average Impact Factor of up to 6.1 over the past five years. According to Google Scholar, the paper has been cited 182 times to date.

The Shanghai Outstanding Achievement Awards in Philosophy and Social Sciences are municipal-level awards established to recognize research in the Party’s innovative theories and the field of philosophy and social sciences. The awards are conferred biennially, organized and administered by the Publicity Department of the Shanghai Municipal Committee of the Communist Party of China and the Shanghai Federation of Social Science Circles.

Guoshi Tong

Associate Professor at Fudan International School of Finance, Undergraduate Program Academic Director

Mainly studying asset pricing, international finance, big data and Chinese financial market

Abstract

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional 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 the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example of macroeconomic forecasting shows that the sPCA has a better performance in general.