The Prediction of Stock Returns Using Financial Ratios and Feature Dimensionality Reduction

• Main Authors: Liew Ban Lee, 廖萬里
• Other Authors: Tsung-Nan Chou
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/49466243470776802352

碩士 === 朝陽科技大學 === 財務金融系 === 104 === Value investing is one of the most popular investment strategy for investors to search for the undervalued stocks based on their financial reports and balance sheets. However, the numerous metrics derived from the financial statements are not easy for the investor to analyze and determine the financial health of a company. The main purpose of this study is to employ feature extraction to identify a smaller number of financial ratios for the prediction of stock return which reflects the quality of a company. Four Data Mining Models approaches, including Multilayer Perceptron Model, Meta Regression Model, Random Forest Model and Random Tree Models were incorporated with feature extraction to evaluate the forecast performance of five different industries in Taiwan. The results demonstrated that the prediction errors were improved for Multilayer Perceptron Model and Meta Regression Model by the feature extraction strategy which reducing the original 16 variables into 5 variables. Finally, this paper concluded that the feature extraction strategy might improve some prediction error, but not suitable in every data mining model.