UROP Proceedings 2021-22

School of Business and Management Department of Accounting 170 Investment Analysis with Machine Learning Supervisor: YOU Haifeng / ACCT Student: LIU Zhetian / MAEC Course: UROP2100, Fall This report investigates the possibility of predicting Chinese industries’ returns using cross-industry historical information in technical analysis. Both linear and non-linear machine learning tools are used to generate predictions, and it is highly possible that such predictability exists based on out-of-sample forecasting results, with an average improvement of 47%. We do not find evidence that OLS Post − LASSO can improve OLS’s predicting performance, which contradicts existing literature. Possible applications, improvements, and generalizations of our study are also analyzed. Investment Analysis with Machine Learning Supervisor: YOU Haifeng / ACCT Student: YEUNG Man Yin Michael / MAEC Course: UROP1100, Fall UROP2100, Spring The advent of machine learning facilitated the extraction of sentiment from financial text corpus. Recent literatures have introduced state-of-the-art models to make stock return predictions. In this study, replication of FarmPredict model by Fan et al. (2021) is implemented to examine its ability to mine sentiment from Chinese analyst reports and make Chinese stock returns predictions. The framework implemented with grid-search and cross-validation can reach optimal test Spearman correlation of 11.7% comparable to that found in the SESTM framework (11.8%) in previous work (Yeung, 2021). Variant of the framework with input not transformed to word embeddings have better performance. The magnitude of correlation provides evidence that FarmPredict can generate predictions useful for investment analysis but to a similarly limited degree.