Short-term forecasting on technology industry stocks return indices by swarm intelligence and time-series models

Abstract

Forecasting is an important technique in many industries and business fields for reading the terrain. The category of technology industry stock, which includes 7 independent stocks, in Taiwan Stock Exchange (TWSE) is selected to be the study subject in this paper. The goal is to forecast the return index of the individual stocks base on the information observed from the trading historical da-ta of the subjects. By including the trading volume, the number of trading rec-ords, the opening price, and the closing price in the inputs to the representative models in time-series and computational intelligence: EGARCH(1,1) and the In-teractive Artificial Bee Colony (IABC), respectively, the forecasting accuracy are compared by the Mean Absolute Percentage Error (MAPE) value. The experi-mental results indicate that the IABC forecasting model with the selected input variables presents superior results than the EGARCH(1,1).