Neural Network Models for Stock Selection Based on Fundamental Analysis
This is an incremental study applying existing neural network methods to financial prediction with fundamental analysis.
This paper compared feed-forward neural networks (FNN) and adaptive neural fuzzy inference systems (ANFIS) for stock selection using fundamental financial ratios, finding that both architectures outperformed a benchmark index and FNN performed better than ANFIS.
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.