Inovacije u oblasti tehnologije dovele su do promene načina funkcionisanja organizacija. Oblast mašinskog učenja je, pored mnogih oblasti, našla svoju primenu u predviđanjima na finansijskom tržištu, a veštačka inteligencija, kao još jedna od klјučnih inovacije iz oblasti tehnologije, počinje da uzima sve više maha u oblasti predviđanja. Globalizacijom i ubrzanim procesom sprovođenja digitalne transformacije pod uticajem kriza na svetskom nivou zahtevaju napredovanje u predviđanju kretanja cena akcija. Rad ima za cilј da iznađe uticaj mašinskog učenja na predviđanje cena akcija. Shodno ubrzanom razvoju mašinskog učenja i veštačke inteligencije vremenski period na osnovu kojeg je sproveden pregled literature je 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