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Open AccessArticle

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

1
Department of Business Administration, I-Shou University, Kaohsiung City 84001, Taiwan
2
Department of Information Management, National Yunlin University of Science and Technology, Touliu Yunlin 64002, Taiwan
3
Department of Hospitality Management, I-Shou University, Kaohsiung City 84001, Taiwan
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1474; https://doi.org/10.3390/sym11121474
Received: 31 October 2019 / Revised: 27 November 2019 / Accepted: 30 November 2019 / Published: 3 December 2019
Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.
Keywords: stock forecast; stock volatility; fuzzy time series; multifactor forecast stock forecast; stock volatility; fuzzy time series; multifactor forecast
MDPI and ACS Style

Tsai, M.-C.; Cheng, C.-H.; Tsai, M.-I. A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index. Symmetry 2019, 11, 1474.

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