The strategy of investment in the stock market using modified support vector regression model

Abstract

Stock indices forecasting has become a popular research issue in recent years. Although many statistical time series models have been applied to stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, sliding window algorithm, and support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than those of the existing models in terms of average and standard deviation of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.