A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index

Tai-Liang Chen (), Ching-Hsue Cheng and Jing-Wei Liu ()
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Tai-Liang Chen: Department of Digital Content Application and Management, Wenzao Ursuline University of Languages, Kaohsiung, Taiwan 807, R.O.C.
Ching-Hsue Cheng: Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan 640, R.O.C.
Jing-Wei Liu: Department of Sport Information and Communication, National Taiwan University of Sport, Taichung, Taiwan 404, R.O.C.

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 06, 1967-1987

Abstract: Stock forecasting technology is always a popular research topic because accurate forecasts allow profitable investments and social change. We postulate, based on past research, three major drawbacks for using time series in forecasting stock prices as follows: (1) a simple time-series model provides insufficient explanations for inner and external interactions of the stock market; (2) the variables of a time series behave in strict stationarity, but economic time-series are usually in a nonlinear or nonstationary state and (3) the forecasting factors of multivariable time-series are selected based on researcher’s knowledge, and such a method is a “subjective” way to construct a forecasting model. Therefore, this paper proposes a causal time-series model to select forecasting factors and builds a machine learning forecast model. The “Granger causality test” is utilized first in the proposed model to select the critical factors from technical indicators and market indexes; next, a “multilayer perceptron regression (MLPR)” is employed to construct a forecasting model. This paper collected financial data over a 13-year period (from 2003 to 2015) of the Taiwan stock index (TAIEX) as experimental datasets. Furthermore, the root mean square error (RMSE) was used as a performance indicator, and we use five forecasting models as comparison models. The results reveal that the proposed model outperforms the comparison models in forecasting accuracy and performs well for three key indicators. LAG1, S&P500 and DJIA, are critical factors in all 11 of our time sliding windows (T1–T11). We offer these results to investors to aid in their decision-making processes.

Keywords: Granger causality test; time-series; MLPR; forecasting technology (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622019500421

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