Bin LUO, a PhD graduate of the Class of 2025 from the Department of Finance in the College of Business at the Southern University of Science and Technology (SUSTech), had a paper titled “Estimating the Pricing Kernel: A Simple Regression-Based Dimension-Reduction Approach” published online in the top international management journal Management Science.

Bin LUO (right) receiving the award

The field of financial asset pricing struggles with the “factor zoo,” and scholars still haven’t reached a consensus on which factors truly belong in the pricing kernel. This paper uses the equivalence between mean-variance efficient portfolios and the pricing kernel to turn the problem of solving high-dimensional mean-variance efficient portfolios into a LASSO-regularized regression. It introduces the “Regularized Efficient Portfolio” (REP) method, which overcomes the problem of a non-invertible covariance matrix. On the theoretical side, the paper proves that the out-of-sample Sharpe ratio of REP converges to the theoretical maximum and pricing errors converge to zero. On the empirical side, using 174 U.S. stock characteristic factors and the market factor from 1980 to 2022, and employing a rolling window method to construct out-of-sample strategies, the study finds that REP performs better out-of-sample than mainstream methods in the existing literature, achieving the highest out-of-sample Sharpe ratio (3.59) and the lowest factor pricing errors. At the same time, REP can identify which of the 174 high-dimensional factors are more important for asset pricing. Additionally, the REP framework can be used to compare the asset pricing power of different sets of factors. The paper was completed collaboratively by Bin LUO, Professor Guofu ZHOU from the Olin Business School at Washington University in St. Louis, and Associate Professor Ti ZHOU from the School of Economics and Management at Harbin Institute of Technology (Shenzhen). Bin LUO is the first author, and SUSTech is the first affiliated institution of the paper.

Management Science is one of the UTD24 journals and was first published in 1954 and is sponsored by the Institute for Operations Research and the Management Sciences (INFORMS) in the U.S. It is a top-tier journal with a long history and excellent reputation in the fields of management science and operations research. Management Science is widely recognized as a leader in research areas like decision science, and because of its rigorous acceptance standards and broad academic influence, it is listed as a key academic evaluation indicator by top business schools worldwide, such as Harvard, Stanford, and MIT Sloan.

Bin LUO completed his undergraduate, master’s, and doctoral studies entirely within the Department of Finance, College of Business, SUSTech, representing a typical success story of the department’s long-term independent talent cultivation. This paper was submitted during his doctoral studies and was officially published recently. Previously, Dr. Nanhui ZHU, a joint master’s-doctoral graduate, received recognition for his dissertation in the “2025 Contemporary Economics Doctoral Innovation Project,” marking the only selection from Guangdong Province to date. During his doctoral studies, he also published an independent correspondent-author paper in the top finance journal JFQA. In the future, the Department of Finance will continue to focus on talent cultivation at all levels, systematically building students’ theoretical framework in modern finance and quantitative research skills from the undergraduate stage, encouraging students to boldly explore the academic frontier, and contributing more young scholars with solid theoretical knowledge, innovative methods, and original application capabilities to the field of business education in China.