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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.

A Nonparametric Approach to Portfolio Shrinkage (2020)
Journal Article
Han, C. (2020). A Nonparametric Approach to Portfolio Shrinkage. Journal of Banking and Finance, 120, Article 105953. https://doi.org/10.1016/j.jbankfin.2020.105953

This paper develops a shrinkage model for portfolio choice. It places a layer on a conventional portfolio problem where the optimal portfolio is shrunk towards a reference portfolio. The model can accommodate a wide range of portfolio problems with v... Read More about A Nonparametric Approach to Portfolio Shrinkage.

Dynamics and Determinants of Credit Risk Discovery: Evidence from CDS and Stock Markets (2017)
Journal Article
Chau, F., Han, C., & Shi, S. (2018). Dynamics and Determinants of Credit Risk Discovery: Evidence from CDS and Stock Markets. International Review of Financial Analysis, 55, 156-169. https://doi.org/10.1016/j.irfa.2017.11.004

This paper investigates the dynamics and drivers of credit risk discovery between stock and CDS markets in the US. Our research is distinguished from the existing literature in three aspects: 1) we employ an improved method to measure the information... Read More about Dynamics and Determinants of Credit Risk Discovery: Evidence from CDS and Stock Markets.

Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies (2017)
Journal Article
Chong, E., Han, C., & Park, F. (2017). Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies. Expert Systems with Applications, 83, 187-205. https://doi.org/10.1016/j.eswa.2017.04.030

We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentiall... Read More about Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies.

Partial Structural Break Identi fication (2017)
Journal Article
Han, C., & Taamouti, A. (2017). Partial Structural Break Identi fication. Oxford Bulletin of Economics and Statistics, 79(2), 145-164. https://doi.org/10.1111/obes.12153

We propose an extension of the existing information criterion-based structural break identification approaches. The extended approach helps identify both pure structural change (break) and partial structural change (break). A pure structural change r... Read More about Partial Structural Break Identi fication.

A Geometric Treatment of Time-Varying Volatilities (2017)
Journal Article
Han, C., Park, F. C., & Kang, J. (2017). A Geometric Treatment of Time-Varying Volatilities. Review of Quantitative Finance and Accounting, 49(4), 1121-1141. https://doi.org/10.1007/s11156-017-0618-0

In this article, we propose a new framework for addressing multivariate time-varying volatilities. By employing methods of differential geometry, our model respects the geometric structure of the covariance space, i.e., symmetry and positive definite... Read More about A Geometric Treatment of Time-Varying Volatilities.