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.

• Adeniyi, O., & Kumeka, T. (2020). Exchange rate and stock prices in Nigeria: Firm-level evidence. Journal of African Business, 21(2), 235-263. https://doi.org/10.1080/15228916.2019.1607144

• Ahmed, S. F., Islam, K. M., & Khan, M. (2015). Relationship between inflation and stock market returns: Evidence from Bangladesh. DIU Journal of Business and Economics, 9(1), Article 14.

• Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the relationship between financial news and the stock market. Scientific Reports, 3(1), Article 3578. https://doi.org/10.1038/srep03578

• Alquraan, T., Alqisie, A., & Al Shorafa, A. (2016). Do behavioral finance factors influence stock investment decisions of individual investors? (Evidences from Saudi Stock Market). American International Journal of Contemporary Research, 6(3), 159-169.

• Bahmani-Oskooee, M., & Bohl, M. T. (2000). German monetary unification and the stability of the German M3 money demand function. Economics Letters, 66(2), 203-208. https://doi.org/10.1016/S0165-1765(99)00223-2

• Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological), 37(2), 149-163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x

• Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., & Vlachogiannakis, N. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112, 353-371.

• Churchill, S. A., Inekwe, J., Ivanovski, K., & Smyth, R. (2019). Dynamics of oil price, precious metal prices and the exchange rate in the long-run. Energy Economics, 84, Article 104508. https://doi.org/10.1016/j.eneco.2019.104508

• Delgado, N. A. B., Delgado, E. B., & Saucedo, E. (2018). The relationship between oil prices, the stock market and the exchange rate: Evidence from Mexico. The North American Journal of Economics and Finance, 45, 266-275. https://doi.org/10.1016/j.najef.2018.03.006

• Drehmann, M., & Juselius, M. (2014). Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting, 30(3), 759-780. https://doi.org/10.1016/j.ijforecast.2013.10.002

• Drucker, P. F. (1978). Managing the third sector. The Wall Street Journal, 3, 26-26.

• Elliott, G., & Timmermann, A. (2016). Forecasting in economics and finance. Annual Review of Economics, 8, 81-110. https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080315-015346

• Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. https://doi.org/10.1006/jcss.1997.1504

• Habiba, U. E., & Zhang, W. (2020). The dynamics of volatility spillovers between oil prices and stock market returns at the sector level and hedging strategies: Evidence from Pakistan. Environmental Science and Pollution Research, 27, 30706-30715. https://doi.org/10.1007/s11356-020-09351-6

• Hasan, M. A., & Zaman, A. (2017). Volatility nexus between stock market and macro-economic variables in Bangladesh: An extended GARCH approach. Scientific Annals of Economics and Business, 64(2), 233-243.

• Hou, Q., Bing, Z. T., Hu, C., Li, M. Y., Yang, K. H., Mo, Z., Xie, X. W., Liao, J. L., Lu, Y., Horie, S., & Lou, M. W. (2018). RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine, 32, 234-244.

• Ibrahim, M. H. (2015) Oil and food prices in Malaysia: A nonlinear ARDL analysis. Agricultural and Food Economics, 3, Article 2. https://doi.org/10.1186/s40100-014-0020-3.

• Khan, M. M., & Yousuf, A. S. (2013). Macroeconomic forces and stock prices: Evidence from the Bangladesh stock market. Munich Personal RePEc Archive.

• Kolapo, F. T., & Adaramola, A. O. (2012). The impact of the Nigerian capital market on economic growth (1990-2010). International Journal of Developing Societies, 1(1), 11-19.

• Kumar, G., & Misra, A. K. (2019). Liquidity-adjusted CAPM - An empirical analysis on Indian stock market. Cogent Economics & Finance, 7(1), Article 1573471. https://doi.org/10.1080/23322039.2019.1573471

• Lacheheb, M., & Sirag, A. (2019). Oil price and inflation in Algeria: A nonlinear ARDL approach. The Quarterly Review of Economics and Finance, 73, 217-222. https://doi.org/10.1016/j.qref.2018.12.003

• Musallam, S. R. (2018). Exploring the relationship between financial ratios and market stock returns. Eurasian Journal of Business and Economics, 11(21), 101-116.

• Okere, K. I., Muoneke, O. B., & Onuoha, F. C. (2021). Symmetric and asymmetric effects of crude oil price and exchange rate on stock market performance in Nigeria: Evidence from multiple structural break and NARDL analysis. The Journal of International Trade & Economic Development, 30(6), 1-27. https://doi.org/10.1080/09638199.2021.1918223

• Pan, X., Uddin, M. K., Han, C., & Pan, X. (2019). Dynamics of financial development, trade openness, technological innovation and energy intensity: Evidence from Bangladesh. Energy, 171, 456-464. https://doi.org/10.1016/j.energy.2018.12.200

• Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031

• Rahman, A. S. A., Abdul-Rahman, S., & Mutalib, S. (2017, November). Mining textual terms for stock market prediction analysis using financial news. In A. Mohamed, M. Berry & B. Yap (Eds.), Soft Computing in Data Science (pp. 293-305). Springer. https://doi.org/10.1007/978-981-10-7242-0_25

• Raza, N., Shahzad, S. J. H., Tiwari, A. K., & Shahbaz, M. (2016). Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets. Resources Policy, 49, 290-301. https://doi.org/10.1016/j.resourpol.2016.06.011

• Sarwar, S., & Wasim, H. (2016) Oil prices and Asian emerging stock markets: Pakistan and Bangladesh. European Journal of Economic Studies, 2(16), 353-357. https://doi.org/10.13187/es.2016.16.353

• Sheikh, U. A., Asad, M., Ahmed, Z., & Mukhtar, U. (2020). Asymmetrical relationship between oil prices, gold prices, exchange rate, and stock prices during global financial crisis 2008: Evidence from Pakistan. Cogent Economics & Finance, 8(1), Article 1757802. https://doi.org/10.1080/23322039.2020.1757802

• Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. Sickles & W. Horrace (Eds.), Festschrift in honor of Peter Schmidt (pp. 281-314). Springer. https://doi.org/10.1007/978-1-4899-8008-3_9

• Singhal, S., Choudhary, S., & Biswal, P. C. (2019). Return and volatility linkages among international crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico. Resources Policy, 60, 255-261. https://doi.org/10.1016/j.resourpol.2019.01.004

• Tuyon, J., & Ahmad, Z. (2016). Behavioural finance perspectives on Malaysian stock market efficiency. Borsa Istanbul Review, 16(1), 43-61. https://doi.org/10.1016/j.bir.2016.01.001

• Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K. (2020). Financial crisis prediction model using ant colony optimization. International Journal of Information Management, 50, 538-556. https://doi.org/10.1016/j.ijinfomgt.2018.12.001