The influence of AI on price forecasting. The view of the academic community
Abstract
In the context of the impressive development of Big Data, AI algorithms have proven their efficiency in processing and analyzing large volumes of data. Price prediction was no exception. In the modern economic fields, the need for advanced prediction models, with increased efficiency, has become more and more important. Thus, the interest in the potential of AI solutions in terms of price prediction for all industries has also grown progressively. The present study aims to capture, by using several Natural Language Processing techniques, the feeling that the academic community has in relation to the subject of price prediction and the way in which opinions have evolved over the years. For this purpose, the abstracts of the works indexed in the Clarivate WoS that addressed this topic are included in the current analysis. The scores obtained after the analysis reveal a slightly positive attitude towards the subject, but nevertheless quite reserved. The main topics existing in these articles are also extracted by means of Latent Dirichlet Allocation. Our analysis makes contributions to the formulation of the position that specialists in the scientific community have in relation to price prediction and AI evolution. Further, it provides new research directions for future studies.
Keyword : AI, price prediction, research publication, LDA, sentiment analysis, volatility
How to Cite
Ciuverca, A.-C.-D., & Oprea, S. (2025). The influence of AI on price forecasting. The view of the academic community. Journal of Business Economics and Management, 26(1), 231–254. https://doi.org/10.3846/jbem.2025.23544
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Amirzadeh, R., Nazari, A., & Thiruvady, D. (2022). Applying artificial intelligence in cryptocurrency markets: A survey. Algorithms, 15(11), Article 428. https://doi.org/10.3390/a15110428
Aydin, A. D., & Cavdar, S. C. (2015). Prediction of financial crisis with artificial neural network: An empirical analysis on Turkey. International Journal of Financial Research, 6(4). https://doi.org/10.5430/ijfr.v6n4p36
Bâra, A., & Oprea, S. V. (2024). The impact of academic publications over the last decade on historical bitcoin prices using generative models. Journal of Theoretical and Applied Electronic Commerce Research, 19(1), 538–560. https://doi.org/10.3390/jtaer19010029
Bharathi, S., & Geetha, A. (2017). Sentiment analysis for effective stock market prediction. International Journal of Intelligent Engineering and Systems, 10(3), 146–154. https://doi.org/10.22266/ijies2017.0630.16
Bristone, M., Prasad, R., & Abubakar, A. A. (2020). CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms. Petroleum, 6(4), 353–361. https://doi.org/10.1016/j.petlm.2019.11.009
Chen, Z. Y. (2022). A computational intelligence hybrid algorithm based on population evolutionary and neural network learning for the crude oil spot price prediction. International Journal of Computational Intelligence Systems, 15(1), Article 68. https://doi.org/10.1007/s44196-022-00130-4
Chopra, R., Sharma, G. D., & Pereira, V. (2024). Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction. Technovation, 135, Article 103067. https://doi.org/10.1016/j.technovation.2024.103067
Choy, L. H. T., & Ho, W. K. O. (2023). The use of machine learning in real estate research. Land, 12(4), Article 740. https://doi.org/10.3390/land12040740
Fazlija, B., & Harder, P. (2022). Using financial news sentiment for stock price direction prediction. Mathematics, 10(13), Article 2156. https://doi.org/10.3390/math10132156
Gupta, N., & Nigam, S. (2020). Crude oil price prediction using artificial neural network. Procedia Computer Science, 170, 642–647. https://doi.org/10.1016/j.procs.2020.03.136
Hu, J. W. S., Hu, Y. C., & Lin, R. R. W. (2012). Applying neural networks to prices prediction of crude oil futures. Mathematical Problems in Engineering, 2012, Article 959040. https://doi.org/10.1155/2012/959040
Jay, P., Kalariya, V., Parmar, P., Tanwar, S., Kumar, N., & Alazab, M. (2020). Stochastic neural networks for cryptocurrency price prediction. IEEE Access, 8, 82804–82818. https://doi.org/10.1109/ACCESS.2020.2990659
Levantesi, S., & Piscopo, G. (2020). The importance of economic variables on London real estate market: A random forest approach. Risks, 8(4), Article 112. https://doi.org/10.3390/risks8040112
Li, X., & Ming, H. (2023). Stock market prediction using reinforcement learning with sentiment analysis. International Journal on Cybernetics & Informatics, 12(1). https://doi.org/10.5121/ijci.2023.120101
Mehta, Y., Malhar, A., & Shankarmani, R. (2021). Stock price prediction using machine learning and sentiment analysis. In 2021 2nd International Conference for Emerging Technology, INCET 2021. IEEE. https://doi.org/10.1109/INCET51464.2021.9456376
Metlek, S. (2022). Forecasting of dow jones Sukuk index prices using Artificial Intelligence systems. Economic Computation and Economic Cybernetics Studies and Research, 56(1), 107–124. https://doi.org/10.24818/18423264/56.1.22.07
Moon, K. S., & Kim, H. (2023). Efficient asset allocation based on prediction with adaptive data selection. Economic Computation and Economic Cybernetics Studies and Research, 57(1), 57–72. https://doi.org/10.24818/18423264/57.1.23.04
Mostofi, F., Toǧan, V., & Başaǧa, H. B. (2022). Real-estate price prediction with deep neural network and principal component analysis. Organization, Technology and Management in Construction, 14(1). https://doi.org/10.2478/otmcj-2022-0016
Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603–9611. https://doi.org/10.1016/j.eswa.2015.07.052
Oprea, S. V., & Bâra, A. (2024). Generative literature analysis on the rise of prosumers and their influence on the sustainable energy transition. Utilities Policy, 90, Article 101799. https://doi.org/10.1016/j.jup.2024.101799
Pai, P. F., & Wang, W. C. (2020). Using machine learning models and actual transaction data for predicting real estate prices. Applied Sciences, 10(17), Article 5832. https://doi.org/10.3390/app10175832
Pour, E. S., Jafari, H., Lashgari, A., Rabiee, E., & Ahmadisharaf, A. (2022). Cryptocurrency price prediction with neural networks of LSTM and Bayesian optimization. European Journal of Business and Management Research, 7(2). https://doi.org/10.24018/ejbmr.2022.7.2.1307
Putra, P. H., Azanuddin, A., Purba, B., & Dalimunthe, Y. A. (2023). Random forest and decision tree algorithms for car price prediction. Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), 3(2). https://doi.org/10.54076/jumpa.v3i2.305
Sandu, A., Ioanăș, I., Delcea, C., Florescu, M. S., & Cotfas, L. A. (2024). Numbers do not lie: A bibliometric examination of machine learning techniques in fake news research. Algorithms, 17(2), Article 70. https://doi.org/10.3390/a17020070
Sandu, A., Ioanăș, I., Delcea, C., Geantă, L. M., & Cotfas, L. A. (2024). Mapping the landscape of misinformation detection: A bibliometric approach. Information, 15(1), Article 60. https://doi.org/10.3390/info15010060
Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2023). Forecasting cryptocurrency prices using LSTM, GRU, and bi-directional LSTM: A deep learning approach. Fractal and Fractional, 7(2), Article 203. https://doi.org/10.3390/fractalfract7020203
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), Article 16. https://doi.org/10.1186/s40854-019-0131-7
Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of Big Data, 7(1), Article 66. https://doi.org/10.1186/s40537-020-00333-6
Sun, F., Meng, X., Zhang, Y., Wang, Y., Jiang, H., & Liu, P. (2023). Agricultural product price forecasting methods: A review. Agriculture, 13(9), Article 1671. https://doi.org/10.3390/agriculture13091671
Vuong, P. H., Phu, L. H., Van Nguyen, T. H., Duy, L. N., Bao, P. T., & Trinh, T. D. (2024). A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach. Science Progress, 107(1). https://doi.org/10.1177/00368504241236557
Yang, J., Wang, Y., & Li, X. (2022). Prediction of stock price direction using the LASSO-LSTM model combines technical indicators and financial sentiment analysis. PeerJ Computer Science, 8, Article e1148. https://doi.org/10.7717/PEERJ-CS.1148
Zhang, R., Li, G., & Ma, Z. (2020). A deep learning based hybrid framework for day-ahead electricity price forecasting. IEEE Access, 8, 143423–143436. https://doi.org/10.1109/ACCESS.2020.3014241