PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

This research looks at the interplay between financial development, exchange rates, and the stock market in Bangladesh from 1995 to 2019 and employs the Nonlinear Autoregressive Distributed Lag (NARDL) model. The machine learning technique uses the iterative classifier optimizer to beat other classifiers in stock market capitalization prediction. According to our NARDL findings, changes in financial development and exchange rates positively impact stock market capitalization in Bangladesh. Negative changes in financial development and the currency rate, on the other hand, have mixed long-term and short-term consequences for the stock market. The dynamic multiplier graphs show that the response of the stock market capitalization to positive changes in financial development and exchange rates is nearly comparable to the response to negative changes. According to the Wald test, there are asymmetries among variables. We urge governments to remove barriers to development, upgrade infrastructure, expand the stock market’s capacity, and restore market participants’ confidence in the Bangladesh stock market.

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