Abstract:
The Colombo Stock Exchange (CSE) experiences frequent fluctuations driven by political
and economic factors, which undermine investor confidence and complicate investment
decisions. The objective of this research is to evaluate the forecasting accuracy of the Mixed
Data Sampling (MIDAS) model in predicting the All Share Price Index (ASPI) within the
Sri Lankan context. This study employs the Mixed Data Sampling (MIDAS) model to
forecast stock prices using the All Share Price Index (ASPI) and the Standing Lending
Facility Rate (SLFR) as key variables. Monthly ASPI and quarterly SLFR data from January
2018 to December 2024 were utilized. The MIDAS regression is estimated using polynomial
lag structures to capture the delayed impact of interest rate changes on stock market
performance. Unit root test, including the ADF & PP results, confirmed that all variables are
integrated of order one (I(1)). The MIDAS model was then used to forecast the All Share
Price Index (ASPI) for the period January to December 2025, yielding an average predicted
value of 4.65%. The forecasting accuracy metrics showed a low MAE of 0.1390%, RMSE
of 0.2459%, and MAPE of 2.3042%. According to Lewis’s (1982) criteria, a MAPE below
10% indicates high forecasting accuracy. This study highlights the effectiveness of the
MIDAS model in integrating mixed-frequency data and its practical value for emerging
markets like Sri Lanka. The findings offer valuable insights for investors, financial analysts,
and policymakers aiming to enhance decision-making through advanced forecasting
techniques. Therefore, they should integrate mixed-frequency forecasting models like
MIDAS into monetary and financial decision-making to enhance predictive accuracy,
support data-driven policy formulation, improve investor confidence, and promote
sustainable economic growth.
