Outputs (4)

Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning (2021)
Journal Article
Han, C. (2022). Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning. Management Science, 68(10), 7701-7741. https://doi.org/10.1287/mnsc.2021.4189

This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alle... Read More about Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning.

A machine learning approach for the short-term reversal strategy (2021)
Journal Article
Tan, Z., Li, Y., & Han, C. (2021). A machine learning approach for the short-term reversal strategy. International journal of data science and analysis, 7(6), 150-160. https://doi.org/10.11648/j.ijdsa.20210706.13

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in m... Read More about A machine learning approach for the short-term reversal strategy.

Betting against analyst target price (2021)
Journal Article
Han, C., Kang, J., & Kim, S. (2022). Betting against analyst target price. Journal of Financial Markets, 59(Part B), Article 100677. https://doi.org/10.1016/j.finmar.2021.100677

Using a robust measure that captures the market’s reaction to analysts’ target price releases, we show that the initial stock price reaction corresponds to target prices, but the price drifts in the opposite direction for a long period, resulting in... Read More about Betting against analyst target price.

A Geometric Framework for Covariance Dynamics (2021)
Journal Article
Han, C., & Park, F. C. (2022). A Geometric Framework for Covariance Dynamics. Journal of Banking and Finance, 134, Article 106319. https://doi.org/10.1016/j.jbankfin.2021.106319

Employing methods of differential geometry, we propose a new framework for covariance dynamics modeling. Our approach respects the intrinsic geometric properties of the space of covariance matrices and allows their natural evolution. We develop covar... Read More about A Geometric Framework for Covariance Dynamics.