Innovations in the field of technology have led to a change in the way organizations function. The field of machine learning, in addition to many fields, has found its application in predictions in the financial market, and artificial intelligence, as another key innovation in the field of technology, is starting to gain more momentum in the field of predictions. Globalization and the accelerated process of implementing digital transformation under the influence of crises at the world level require progress in predicting the movement of stock prices. The paper aims to find the impact of machine learning on stock price forecasting. In accordance with the accelerated development of machine learning and artificial intelligence, the time period based on which the literature review was conducted is 2021-2023.

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Albahli, S., Irtaza, A., Nazir, T., Mehmood, A., Alkhalifah, A., & Albattah, W. (2022). A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data. Electronics, 11(20), 3414. https://doi.org/10.3390/electronics11203414

Almaskati, N. (2022). Machine learning in finance: Major applications, issues, metrics, and future trends. International Journal of Financial Engineering, 09(03). https://doi.org/10.1142/S2424786322500104

Creamer, G. G., Sakamoto, Y., Nickerson, J. V., & Ren, Y. (2023). Hybrid Human and Machine Learning Algorithms to Forecast the European Stock Market. Complexity, 2023, 1–20. https://doi.org/10.1155/2023/5847887

Gao, X., Wang, J., & Yang, L. (2022). An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic. Axioms, 11(8), 374. https://doi.org/10.3390/axioms11080374

Jabeen, A., Yasir, M., Ansari, Y., Yasmin, S., Moon, J., & Rho, S. (2022). An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning. Complexity, 2022, 1–18. https://doi.org/10.1155/2022/4646733

Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64–82. https://doi.org/10.1016/j.jfineco.2021.08.017

Lin, Y., Liu, S., Yang, H., Wu, H., & Jiang, B. (2021). Improving stock trading decisions based on pattern recognition using machine learning technology. PLOS ONE, 16(8), e0255558. https://doi.org/10.1371/journal.pone.0255558

Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H.-C. (2021). Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717

Sebastião, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation, 7(1), 3. https://doi.org/10.1186/s40854-020-00217-x

Wang, W.-J., Tang, Y., Xiong, J., & Zhang, Y.-C. (2021). Stock market index prediction based on reservoir computing models. Expert Systems with Applications, 178, 115022. https://doi.org/10.1016/j.eswa.2021.115022

Warin, T., & Stojkov, A. (2021). Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature. Journal of Risk and Financial Management, 14(7), 302. https://doi.org/10.3390/jrfm14070302

Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971