Mixed chaotic FOA with GRNN to construction of a mutual fund forecasting model
| Autor: | Ying-Ying Zhou, Shi-Zhuan Han, Zong-Li Liu, Li-Hui Huang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Cognitive Neuroscience Chaotic Experimental and Cognitive Psychology 02 engineering and technology computer.software_genre Net asset value 020901 industrial engineering & automation Artificial Intelligence Value (economics) 0202 electrical engineering electronic engineering information engineering Data envelopment analysis 020201 artificial intelligence & image processing Data mining business computer Software Predictive modelling Mutual fund Research data |
| Zdroj: | Cognitive Systems Research. 52:380-386 |
| ISSN: | 1389-0417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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