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The prediction of stock returns with regression approaches and feature extraction

Author

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  • Chrits Liew

    (Chaoyang University of Technology, Taiwan)

  • Tsung-Nan Chou

    (Chaoyang University of Technology, Taiwan)

Abstract

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. Two regression approaches, including Multilayer Perceptron model and Meta Regression by discretization model, were incorporated with feature extraction to evaluate the forecast performance for two different industries in Taiwan. The results demonstrated that the prediction errors were improved for both models by the feature extraction strategy which reducing the original 16 variables into 5 variables. Besides that, both models achieved better prediction result in concrete industry rather than rubber industry. Finally, this paper concluded that the overall performance of the Multilayer Perceptron model is better than the other model.

Suggested Citation

  • Chrits Liew & Tsung-Nan Chou, 2016. "The prediction of stock returns with regression approaches and feature extraction," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 2(3), pages 107-112.
    • Handle: RePEc:apb:jabsss:2016:p:107-112
    DOI: 10.20474/jabs-2.3.1
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