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Neural Network Models for Stock Selection Based on Fundamental Analysis

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  • Yuxuan Huang
  • Luiz Fernando Capretz
  • Danny Ho

Abstract

Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS.

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  • Yuxuan Huang & Luiz Fernando Capretz & Danny Ho, 2019. "Neural Network Models for Stock Selection Based on Fundamental Analysis," Papers 1906.05327, arXiv.org.
    • Handle: RePEc:arx:papers:1906.05327
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    Cited by:

    1. Jinho Lee & Sungwoo Park & Jungyu Ahn & Jonghun Kwak, 2022. "ETF Portfolio Construction via Neural Network trained on Financial Statement Data," Papers 2207.01187, arXiv.org.
    2. Wei Pan & Jide Li & Xiaoqiang Li, 2020. "Portfolio Learning Based on Deep Learning," Future Internet, MDPI, vol. 12(11), pages 1-13, November.

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