  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.
