Neural Network Models for Stock Selection Based on Fundamental Analysis

Yuxuan Huang, Luiz Fernando Capretz and Danny Ho

Papers from arXiv.org

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.

Date: 2019-06
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in 32nd Canadian Conference on Electrical & Computer Engineering, Edmonton, Canada, 2019

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