A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources

References

[1] Alshater, M. M., Kampouris, I., Marashdeh, H., Atayah, O. F., & Banna, H. (2022). Early warning system to predict energy prices: The role of artificial intelligence and machine learning. Ann. Operat. Res. https://doi.org/10.1007/s10479-022-04908-9

[2] Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Sys. Applications, 42(20), 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013

[3] Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange. Expert Sys. Applications, 37(12), 7908–7912. https://doi.org/10.1016/j. eswa.2010.04.045

[4] Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications.

[5] Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, Article 100015. https://doi.org/10.1016/j. dajour.2021.100015

[6] Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Sys. Applications, 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030

[7] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

[8] Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. Int. J. Circuits, Sys. Signal Processing, 10(2016), 403–413.

[9] Fama, E. F. (1965). The behavior of stock-market prices. J. Business, 38(1), 34–105. https://doi.org/10.1086/294743

[10] Fama, E. F. (1995). Random walks in stock market prices. Financial Analysts J, 51(1), 75–80. https://doi.org/10.2469/faj.v51.n1.1861

[11] Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. Int. Econo. Rev, 10(1), 1–21. https://doi.org/10.2307/2525569

[12] Geva, T., & Zahavi, J. (2014). Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Sys, 57, 212–223. https://doi.org/10.1016/j.dss.2013.09.013

[13] Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. Int. J. Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008

[14] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

[15] Islam, M. R., V, V., Al-Shaikhli, I. F., & Nor, R. B. M. (2018). Tech. appr. text mining for stock market prediction: A systematic review, 10(2). https://doi.org/10.11591/ijeecs. v10.i2.pp770-777

[16] Jabbar Alkubaisi, G. A. A., H, H., & Kamaruddin, S. S. (2017). A systematic review on the relationship between stock market prediction model using sentiment analysis on Twitter based on machine learning method and features selection. 95(24).

[17] Jakhar, D., & Kaur, I. (2020). Artificial intelligence, machine learning and deep learning: Definitions and differences. Clin. Exp. Dermatol, 45(1), 131–132. https://doi.org/ 10.1111/ced.14029

[18] Januschowski, T., Gasthaus, J., Wang, Y., Salinas, D., Flunkert, V., Bohlke-Schneider, M., et al. (2020). Criteria for classifying forecasting methods. Int. J. Forecasting, 36(1), 167–177. https://doi.org/10.1016/j.ijforecast.2019.05.008

[19] Kaplan, A., & Haenlein, M. (2019). Siri, siri, in my hand: Who’s the fairest in the land?

[20] On the interpretations, illustrations, and implications of artificial intelligence.

[21] Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 Ketsetsis, A. P., T, D., Kourounis, C., Spanos, G., Giannoutakis, K. M., Pavlidis, P.,

[22] Vazakidis, D., Champeris, T., & Thomas, D. (2020). Deep learning techniques for stock market prediction in the European union: A systematic review.

[23] Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.

[24] Kumar, D., Sarangi, P. K., & Verma, R. (2022a). A systematic review of stock market prediction using machine learning and statistical techniques. Mate. Today Proc, 49 (8), 3187–3191. https://doi.org/10.1016/j.matpr.2020.11.399

[25] Kumar, D., Sarangi, P. K., & Verma, R. (2022b). A systematic review of stock market prediction using machine learning and statistical techniques. Mat. Today Proc, 49(8), 3187–3191. https://doi.org/10.1016/j.matpr.2020.11.399

[26] Li, A. W., & Bastos, G. S. (2020). Stock market forecasting using deep learning and technical analysis: A systematic review. IEEE Access, 8, 185232–185242. https://doi. org/10.1109/ACCESS.2020.3030226

[27] Liang, C., Tang, L., Li, Y., & Wei, Y. (2020). Which sentiment index is more informative to forecast stock market volatility? Evidence from China. International Review of Financial Analysis, 71, Article 101552. https://doi.org/10.1016/j.irfa.2020.101552

[28] Macchiarulo, A. (2018). Predicting and beating the stock market with machine learning and technical analysis. J. Int. Banking and Commerce, 23(1), 1–22.

[29] Makridakis, S., Hyndman, R. J., & Petropoulos, F. (2020). Forecasting in social settings: The state of the art. Int. J. Forecasting, 36(1), 15–28. https://doi.org/10.1016/j. ijforecast.2019.05.011

[30] Misra, M., Yadav, A. P., & Kaur, H. (2018). Stock market prediction using machine learning algorithms: A classification study. In 2018 international conference on recent innovations in electrical, electronics & communication engineering (pp. 2475–2478).

[31] ICRIEECE. https://doi.org/10.1109/ICRIEECE44171.2018.9009178.

[32] Mokhtari, S., Yen, K. K., & Liu, J. (2021). Effectiveness of artificial intelligence in stock market prediction based on machine learning. Int. J. Comp. App, 183(7), 1–8. https://doi.org/10.5120/ijca2021921347

[33] Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. ARTIFICIAL IN℡LIGENCE REVIEW, 53(4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z

[34] Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Sys. App, 73, 125–144. https://doi.org/10.1016/j. eswa.2016.12.036

[35] Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Sys. Rev, 5(1), 210. https://doi.org/10.1186/ s13643-016-0384-4

[36] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71

[37] Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and

[38] exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/ bmj.n160

[39] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Sys. App, 42(1), 259–268. https://doi.org/10.1016/j. eswa.2014.07.040

[40] Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., et al. (2022). Forecasting: Theory and practice. Int. J. Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001

[41] Pinto, N., da Silva Figueiredo, L., & Garcia, A. C. (2021). Automatic prediction of stock market behavior based on time series, text mining and sentiment analysis: A systematic review. 2021 ieee 24th international Conference on computer supported cooperative Work in design (cscwd), 1203–1208. https://doi.org/10.1109/ CSCWD49262.2021.9437732

[42] Pinto, N., Figueiredo, L. da S., & Garcia, A. C. (2021). Automatic Prediction of stock market behavior Based on time series, text Mining and sentiment analysis: A systematic review—proceedings of the 2021 IEEE 24TH international conference on computer supported cooperative work in design (CSCWD). 1203–1208.

[43] Samitas, A., Kampouris, E., & Kenourgios, D. (2020). Machine learning as an early warning system to predict financial crisis. Int. Rev. Fin. Analys, 71, Article 101507. https://doi.org/10.1016/j.irfa.2020.101507

[44] Shmueli, G., & Tafti, A. (2022). How to “improve” prediction using behavior modification. Int. J. Forecasting. https://doi.org/10.1016/j.ijforecast.2022.07.008. S0169207022001066.

[45] Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE International Conference on

[46] Machine Learning and Applications (ICMLA) (pp. 1394–1401). https://doi.org/ 10.1109/ICMLA.2018.00227

[47] Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. Int. Jo. Forecasting, 36(1), 75–85. https://doi. org/10.1016/j.ijforecast.2019.03.017

[48] Song, G. H., & Jain, A. (2022). Can artificial intelligence beat the stock market? Studies in Economics and finance. https://doi.org/10.1108/SEF-03-2022-0133. ahead-of-print (ahead-of-print).

[49] Soni, P., K, D., & Tewari, Y. (2022). Mach. Learn. App.Stock Price Prediction: Sys. Rev, 2161

[50] (1). https://doi.org/10.1088/1742-6596/2161/1/012065

[51] Strader, T. J., Rozycki, J. J., Root, T. H., & Huang, Y.-H. J. (2020). Machine learning stock market prediction studies: Review and research directions. J. Int. Tech. Info. Manage, 28(4), 63–83.

[52] Sun, A., Lachanski, M., & Fabozzi, F. J. (2016). Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. Int. Rev. Fin. Analy, 48, 272–281. https://doi.org/10.1016/j.irfa.2016.10.009

[53] Tk´aˇc, M., & Verner, R. (2016). Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, 788–804. https://doi.org/10.1016/j. asoc.2015.09.040

[54] Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Sys. App, 79, 153–163. https://doi.org/10.1016/j.eswa.2017.02.041. Scopus.

[55] Zavadzki, S., Kleina, M., Drozda, F., & Marques, M. (2020). Computational intelligence techniques used for stock market prediction: A systematic review. IEEE LATIN AMERICA TRANS, 18(4), 744–755. https://doi.org/10.1109/TLA.2020.9082218

@article{1719627,
      author = {Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C.},
      title = {A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {555-506},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719627.pdf},
      abstract = {There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.},
      keywords = {Machine Learning, Deep Learning, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Neural Networks (NN)},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719627}
  }