Stock picking with machine learning - Ong, Glenna Xianyu (Nanyang Technological University, 2023)
This study focuses on the integration of both Fundamental and Technical Analysis in stock picking with the machine learning model, Multilayer Perceptron (MLP). We will analyze time-series stock data to identify optimal features for training the MLP model and assess the predictability of the machine learning model. The historical stock prices and financial metrics from Nasdaq will be used for analysis. The financial metrics used include Market Capitalization, Earnings Per Share (EPS), Price-to-Earnings (PE) ratio, Price-to-book (PB) ratio, Price-to-Sales (PS) ratio, Market Capitalization and Dividend Yield of stocks from Nasdaq. In this paper, we determine whether MLP can accurately forecast market movements with historical financial data and stock market indicators.