 A factor pricing model based on machine learning algorithmYi Fang, Yuzhi Chen and Hang Ren International Review of Economics & Finance, 2023, vol. 88, issue C, 280-297 Abstract: We adopt Discrete Wavelet Transform (DWT) and Support Vector Regression (SVR) algorithm to predict stock returns and form a time-varying Machine Learning (ML) factor based on the predicted returns for improving the classical asset pricing in the Chinese stock market from 2000 to 2020. The results show that the sorted portfolios formed by the predicted return rates can obtain significant excess returns in the Chinese market. Furthermore, our research shows that incorporating the ML factor into the CH4 and FF5 model can improve the pricing power significantly. It indicates that the ML factor can complement the traditional pricing models. We also find the performance of factor models depend on macroeconomy and market sentiment, that is, the better the macroeconomic and stock market performance, the stronger the pricing power of the factor models in the Chinese market. Additionally, we explore whether ML factor falls under short-term, median-term, and long-term momentum and reversal factors, and our analysis demonstrates that the ML factor is more effective than momentum and reversal factors. Keywords: Wavelet transform; SVR; Bootstrap; Factor pricing; Market anomalies (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:88:y:2023:i:c:p:280-297 DOI: 10.1016/j.iref.2023.06.012 Access Statistics for this article International Review of Economics & Finance is currently edited by H. Beladi and C. Chen More articles in International Review of Economics & Finance from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.