Journal article Open Access
Stock market indexes provide a yardstick with which investors can compare the performance of their individual stock portfolios. The propose of this paper is to examine a suitable model for forecasting stock prices under the volatility in the Colombo Stock Exchange (CSE), Sri Lanka.Since the data has a non-seasonal linear trend, an autoregressive integrated moving average model has used for modeling and forecasting. The results suggested that ARIMA model is more suitable for forecasting ASPI index under the volatility.
Selection of Best ARIMA Modeling Approach for Forecasting Time Series Patterns; A Case Study on Colombo Stock Exchange.pdf