PREDICTION OF THE INDONESIA STOCK EXCHANGE COMPOSITE WITH TIME-SERIES EXTERNAL AND TECHNICAL FACTORS USING ARTIFICIAL NEURAL NETWORK

Indonesia Stock Exchange Composite is an indicative of the movement of all stocks traded on Indonesia stock exchange. Investor uses this to measure the level of capital gains and benchmark the investment portfolio performance. Accurate prediction of stock market composite remains a challenging task, particularly in emerging markets where external economic factors such as interest rates of central bank, world oil prices, inflation rates, and USD/IDR exchange rate exert significant influence. Existing research in prediction models using artificial neural network often focus on specific stocks and overlook the combined influence of macroeconomic and technical indicators. This research addresses the gap by exploring artificial neural network models to predict the stock exchange composite price by integrates composite technical indicators with external macroeconomic factors. Cross-industry standard process for data mining uses as framework to guide the development. Secondary time-series data period 2019 to 2024 of each factors taken. Empirical results demonstrate that the Neural Net model achieve high predictive accuracy and outperform Deep Learning model. The findings highlight the effectiveness of combining macroeconomic and technical factors in data mining processing with artificial neural network for composite price forecasting and offer practical implications for investors and analysts in emerging markets.