Short – Term Forecasting for Daily Stock Market Indices using Discrete Fourier Transforms

Kavishka T. Rajapaksha and Dinushiya S. Rodrigo
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Kavishka T. Rajapaksha: Department of Mathematics, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
Dinushiya S. Rodrigo: Department of Mathematics, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.

International Journal of Research and Innovation in Social Science, 2023, vol. 7, issue 11, 2039-2044

Abstract: The stock market indices are used to gauge the financial movements in the stock markets. If the index rises, the market is growing, and if it falls, the market is declining. The only stock exchange in Sri Lanka is operated by the Colombo Stock Exchange (CSE). Its two primary stock market indices are All Share Price Index (ASPI) and Standard & Poor’s Sri Lanka 20 (S&P SL20). Market indices provide information to investors. So, they can predict the risks and returns of their investments. While ASPI forecasts assist investors in understanding the future direction of the entire market, S&P SL20 forecasts help investors make investment decisions. Therefore, in order to make the correct investment decisions, it is crucial to identify appropriate forecasting methods for those two indices to meet investor expectations. The Discrete Fourier Transform (DFT) is a technique that can be used to convert a time-domain discrete signal into a frequency-domain discrete spectrum. In this study, the ASPI and S&P SL20 indexes were modeled as the Fast Fourier Transform amplitude spectrum using the daily stock values. The daily index data from the years 2017 to 2022 were used to formulate this model. The study also examined the periodic deviations of both indices during the considered period. Additionally, this research predicts the near future of both indices by modeling the daily indices values. To further verify the accuracy of the model, data from the SET (Thailand Stock Index) were employed. According to the results, the ASPI and S&P SL20 datasets show periodic patterns ranging from 4 days to 7 days and the SET datasets show periodic patterns between 5 and 6 days. The forecasting ability of the proposed Fourier model was assessed by using metrices such as Mean Squared Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Finally, it is concluded that the proposed Fourier model is capable of forecasting the daily ASPI and S&P SL20 indices for a short period of time equivalent to their periodicities.

Date: 2023
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