ARIMA and FBMAP approach for forecasting daily stock price in Colombo Stock Exchange, Sri Lanka

Abstract

This study attempts to examine whether the stock prices of companies listed on the Colombo Stock Exchange (CSE) follow Random Walk Hypothesis (RWH) and presents a mathematical model of stock prices using a Fractional Brownian Motion Process with Adaptive Parameters (FBMAP) compared with Auto-Regressive Integrated Moving Average (ARIMA) time series model. The period covered by the research was January 2015 to June 2019. The main objective of the study was to investigate whether stock prices follow the RWH and to compare two major forecasting methods. To check RWH, Chi-square Test, the Runs Test, and the Auto-correlation Test were used. The Augmented Dickey-Fuller Test (ADF Test) was used to verify the stationarity of the data set. In the first phase, the best fitted ARIMA model was found using Akaike Information Criteria (AIC), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). In the second phase, the proposed FBMAP was used to predict future stock prices. The results obtained demonstrated the potential of the ARIMA model and FBMAP model to predict the stock price indices on a short-term basis. The simulation results showed that the FBMAP model is more suitable for forecasting daily closing prices than the ARIMA model.

Keywords

• stock price, random walk hypothesis, arima and fbmap model