Mixed chaotic FOA with GRNN to construction of a mutual fund forecasting model

Abstract

This article attempts to collect the research data of mutual fund in Taiwan, evaluates the management performance of each fund by using the Data Envelopment Analysis and selects the mutual fund whose technical efficiency value is 1 as the investment goal. Then, this article optimizes the parameter of GRNN by using a variety of intelligence algorithms including AFSA, PSO and relatively new FOA, build the forecasting model of mutual fund net value and analyzes the prediction ability with GRNN model that is not optimized. After the compares of five prediction performance evaluation indicators, the model that optimizes the GRNN by using FOA has the highest prediction accuracy among four prediction models.