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Title: A new approach of bivariate fuzzy time series analysis to the forecasting of a stock index
Authors: Wu, Berlin
Tse, S.-M.
Hsu, Y.-Y.
Contributors: 應數系
Keywords: Decision making;Finance;Forecasting;Knowledge based systems;Markov processes;Mathematical models;Matrix algebra;Regression analysis;Time series analysis;Bivariate fuzzy time series analysis;Fuzzy relation equations;Mean absolute forecasting accuracy;Stock index;Fuzzy sets
Date: 2003-12
Issue Date: 2015-07-20 17:21:42 (UTC+8)
Abstract: In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy tune series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.
Relation: International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 11(6), 671-690
Data Type: article
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