Forecasting ASEAN-5 Stock Index Price Movement Using Machine Learning Techniques

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Muneer Shaik
Abhishek Sahjwani
Kesava Sai Krishna Kondepudi


This research investigates the effectiveness of various machine learning models, including Random Forest, Neural Networks, Adaboost, Discriminant Analysis, Logit Model, Support Vectors, and Kernel Factory. The study aims to forecast fluctuations in the ASEAN-5 stock index prices within an eleven-year period. The study provides useful information about how well machine learning techniques can predict changes in the stock market, with potential implications for both academic researchers and market participants. The findings imply that Adaboost consistently outperforms all others in predicting price changes accurately. This shows that machine learning algorithms are capable of accurately forecasting the movement of the ASEAN-5 stock index values. This study contributes to the growing body of research on the use of machine learning techniques in finance and provides investors with information to make informed decisions about investments in the ASEAN-5 region, ultimately leading to increased returns and improved portfolio performance.

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