Rethinking Role of Traditional vs. Advanced Stock Price Forecasting Models: A Critical Literature Review
DOI:
https://doi.org/10.70301/CONF.SBS-JABR.2025.1/1.10Keywords:
forecasting, models, fundamental analysis, technical analysis, sentiment analysis, machine learning, artificial intelligence, hybrid modelsAbstract
Stock price forecasting has been a central question in finance for some time, with traditional models founded on the Efficient Market Hypothesis (EMH) and technical and fundamental analysis, which are often criticized for their inability to account for sentiment market dynamics and non-linear sentiment. Recent technological advances in Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing have expanded and developed new sophisticated forecasting models. These new advanced models have expanded forecasting horizons by incorporating behavioral biases, multidimensional datasets, and investor sentiment. This paper critically reviews the literature on the evolution of stock forecasting methods, highlights the limitations of isolated approaches, and advances the development of more accurate, practically relevant forecasting models.
References
Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205–213. https://doi.org/10.1007/BF00126626
Aldhyani, T. H. H., & Alzahrani, A. (2022). Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms. Electronics, 11(19), 3149. https://doi.org/10.3390/electronics11193149
Alsing, O., & Bahceci, O. (2015). Stock Market Prediction using Social Media Analysis. KTH Royal Institute of Technology.
Arashi, M., & Rounaghi, M. M. (2022). Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model. Future Business Journal, 8(1), 14. https://doi.org/10.1186/s43093-022-00125-9
Arauco Ballesteros, M. A., & Martínez Miranda, E. A. (2024). Stock Market Forecasting Using a Neural Network Through Fundamental Indicators, Technical Indicators and Market Sentiment Analysis. Computational Economics. https://doi.org/10.1007/s10614-024-10711-4
Avijan Dutta, Gautam Bandopadhyay, & Suchismita Sengupta. (2012). Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression. The International Journal of Business and Information, 7(1). https://doi.org/10.6702/ijbi.2012.7.1.5
Avramov, D., Kaplanski, G., & Levy, H. (2015). Talking Numbers: Technical versus Fundamental Recommendations. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2648292
Aygoren, H., & Balkan, E. (2020). The role of efficiency in capital asset pricing: A research on Nasdaq technology sector. Managerial Finance, 46(11), 1479–1493. https://doi.org/10.1108/MF-12-2019-0612
Baker, M., & Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129
Bal, A., Çuvalcıoğlu, G., & Tuğrul, F. (2024). α-Interval Valued Fuzzy Sets and α-Interval Valued Fuzzy Subgroups. In P. Melin & O. Castillo (Eds.), New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms (Vol. 1146, pp. 31–54). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-53713-4_4
Ballings, M., Van Den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013
Banerjee, A. (2022). An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100. Accounting and Finance Research, 11(2), 1. https://doi.org/10.5430/afr.v11n2p1
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE, 12(7), e0180944. https://doi.org/10.1371/journal.pone.0180944
Barraza, J., Rodriguez, L., Valdez, F., Melin, P., & Castillo, O. (2024). A Hybrid Fuzzy Fireworks and Grey Wolf Metaheuristic Algorithm. In P. Melin & O. Castillo (Eds.), New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms (Vol. 1146, pp. 195–210). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-53713-4_16
Belton, P. (2021). El boom de los jóvenes que invierten en acciones a través de apps (y qué peligro tiene). https:// www. bbc. com/ mundo/ notic ias- 57653 541.
Bhardwaj, A., Narayan, Y., Vanraj, Pawan, & Dutta, M. (2015). Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty. Procedia Computer Science, 70, 85–91. https://doi.org/10.1016/j.procs.2015.10.043
Bisoi, R., & Dash, P. K. (2014). A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter. Applied Soft Computing, 19, 41–56. https://doi.org/10.1016/j.asoc.2014.01.039
Bohn, T. A. (2017). Improving Long Term Stock Market Prediction with Text Analysis. Electronic Thesis and Dissertation Repository, 4497.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
Brock, W., Lakonishok, J., & LeBARON, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731–1764. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x
Chen, P., Boukouvalas, Z., & Corizzo, R. (2024). A deep fusion model for stock market prediction with news headlines and time series data. Neural Computing and Applications, 36(34), 21229–21271. https://doi.org/10.1007/s00521-024-10303-1
Chen, T., & Chen, F. (2016). An intelligent pattern recognition model for supporting investment decisions in stock market. Information Sciences, 346–347, 261–274. https://doi.org/10.1016/j.ins.2016.01.079
Cheng, C., Xu, W., & Wang, J. (2012). A Comparison of Ensemble Methods in Financial Market Prediction. 2012 Fifth International Joint Conference on Computational Sciences and Optimization, 755–759. https://doi.org/10.1109/CSO.2012.171
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030
Christodoulaki, E., & Kampouridis, M. (2022). U sing strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading. 2022 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC55065.2022.9870240
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Creighton, J., & Zulkernine, F. H. (2017). Towards building a hybrid model for predicting stock indexes. 2017 IEEE International Conference on Big Data (Big Data), 4128–4133. https://doi.org/10.1109/BigData.2017.8258433
Cristian De La Rosa-Flores, C., Ordonez-Parada, A. I., Raul Sanchez-Acosta, L., Romelia Flores-Morales, C., & Leticia Chavez-Marquez, I. (2018). Influence of the Main Technology Companies in the NASDAQ 100 Stock Index. International Review of Management and Business Research, 7(1), 123–134. https://doi.org/10.30543/7-1(2018)-12
Das, N., Sadhukhan, B., Chatterjee, T., & Chakrabarti, S. (2022). Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak. Social Network Analysis and Mining, 12(1), 92. https://doi.org/10.1007/s13278-022-00919-3
Dash, R., & Dash, P. K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, 2(1), 42–57. https://doi.org/10.1016/j.jfds.2016.03.002
Deboeck, G. J. (Ed.). (1994). Trading on the edge: Neural, genetic, and fuzzy systems for chaotic and financial markets. Wiley.
Deep, A. (2023). A Multifactor Analysis Model for Stock Market Prediction. International Journal of Computer Science and Telecommunications, 14(1), 6.
Ding, W., Mazouz, K., Ap Gwilym, O., & Wang, Q. (2023). Technical analysis as a sentiment barometer and the cross-section of stock returns. Quantitative Finance, 23(11), 1617–1636. https://doi.org/10.1080/14697688.2023.2244991
Ding, X., Zhang, Y., Liu, T., & Duan, J. (2014). Using Structured Events to Predict Stock Price Movement: An Empirical Investigation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1415–1425. https://doi.org/10.3115/v1/D14-1148
Ding, Xiao, Yue Zhang, & Duan, Y. (2015). Deep Learning for Event-Driven Stock Prediction. Paper Presented at the 24th International Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, July 25–31.
Fama, E. F., & French, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. The Journal of Finance, 51(1), 55–84. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x
Ganser, A., Hollaus, B., & Stabinger, S. (2021). Classification of Tennis Shots with a Neural Network Approach. Sensors, 21(17), 5703. https://doi.org/10.3390/s21175703
Hernández, G., Zamora, E., Sossa, H., Téllez, G., & Furlán, F. (2020). Hybrid neural networks for big data classification. Neurocomputing, 390, 327–340. https://doi.org/10.1016/j.neucom.2019.08.095
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534–551. https://doi.org/10.1016/j.asoc.2015.07.008
Huang, Y., Capretz, L. F., & Ho, D. (2021). Machine Learning for Stock Prediction Based on Fundamental Analysis. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01–10. https://doi.org/10.1109/SSCI50451.2021.9660134
Hung, M.-C., Hsia, P.-H., Kuang, X.-J., & Lin, S.-K. (2024). Intelligent portfolio construction via news sentiment analysis. International Review of Economics & Finance, 89, 605–617. https://doi.org/10.1016/j.iref.2023.07.103
Hurwitz, J., & Kirsch, D. (2018). Machine learning for dummies (1st ed.). John Wiley and Sons.
Ivanov, S. I., Jones, F. J., & Zaima, J. K. (2013). Analysis of DJIA, S&P 500, S&P 400, NASDAQ 100 and Russell 2000 ETFs and their influence on price discovery. Global Finance Journal, 24(3), 171–187. https://doi.org/10.1016/j.gfj.2013.10.005
Jing, N., Wu, Z., & Wang, H. (2021). A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Systems with Applications, 178, 115019. https://doi.org/10.1016/j.eswa.2021.115019
K. Nirmala Devi & V. Murali Bhaskaran. (2015). Semantic Enhanced Social Media Sentiments For Stock Market Prediction. https://doi.org/10.5281/ZENODO.1100501
Kaplanoğlu, E. (2023). Financial Performance Ranking of NASDAQ Financial Technology Index Companies. Research and Evaluations in Social, Human and Administrative Sciences, 131–144.
Kour, M. (2024). Challenges and Opportunities of Machine Learning in the Financial Sector: In A. Singh, S. Taneja, & P. Kumar (Eds.), Advances in Finance, Accounting, and Economics (pp. 61–73). IGI Global. https://doi.org/10.4018/979-8-3693-1746-4.ch004
Kumar, G., Singh, U. P., & Jain, S. (2022). An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting. Soft Computing, 26(22), 12115–12135. https://doi.org/10.1007/s00500-022-07451-8
Kumar, R., & K S, S. (2024). Impact of sentimental factors on stock portfolio returns –an empirical analysis. Heliyon, 10(9), e30217. https://doi.org/10.1016/j.heliyon.2024.e30217
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lee, H., Kim, J. H., & Jung, H. S. (2024). Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators. Scientific Reports, 14(1), 10262. https://doi.org/10.1038/s41598-024-61106-2
Lee, K. C., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18(1), 63–72. https://doi.org/10.1016/0167-9236(96)00018-8
Lin, T., Guo, T., & Aberer, K. (2017). Hybrid Neural Networks for Learning the Trend in Time Series. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2273–2279. https://doi.org/10.24963/ijcai.2017/316
Lin, Y.-L., Lai, C.-J., & Pai, P.-F. (2022). Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis. Electronics, 11(21), 3513. https://doi.org/10.3390/electronics11213513
Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139084789
Lizarraga, E., Valdez, F., Castillo, O., & Melin, P. (2023). Fuzzy Dynamic Parameter Adaptation in the Mayfly Algorithm: Implementation of Fuzzy Adaptation and Tests on Benchmark Functions and Neural Networks. In O. Castillo & P. Melin (Eds.), Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design (Vol. 1061, pp. 69–84). Springer International Publishing. https://doi.org/10.1007/978-3-031-22042-5_4
Lopez-Ramos, J. E., Castillo, O., & Melin, P. (2024). Design of a Fuzzy PID Controller for the Balancing Problem of a Quadruped Robot. In P. Melin & O. Castillo (Eds.), New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms (Vol. 1146, pp. 55–60). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-53713-4_5
Markowska-Kaczmar, U., & Dziedzic, M. (2008). Discovery of Technical Analysis Patterns. 2008 International Multiconference on Computer Science and Information Technology, 195–200. https://doi.org/10.1109/IMCSIT.2008.4747239
Medsker, L. R. (1994). Hybrid Neural Network and Expert Systems. Springer US. https://doi.org/10.1007/978-1-4615-2726-8
Melin, P., Sánchez, D., & Cordero-Martínez, R. (2023). Particle Swarm Optimization of Convolutional Neural Networks for Diabetic Retinopathy Classification. In O. Castillo & P. Melin (Eds.), Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design (Vol. 1061, pp. 237–252). Springer International Publishing. https://doi.org/10.1007/978-3-031-22042-5_14
Mittal, S., & Sangwan, O. P. (2019). Big Data Analytics using Machine Learning Techniques. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 203–207. https://doi.org/10.1109/CONFLUENCE.2019.8776614
Molina, J. (2021). Los indicadores de sentimiento de mercado. https:// blogs. elcon fiden cial. com/ merca dos/ apren der-a- inver tir/ 2016- 11- 11/ indic adores- senti miento- merca do_ 12876 94
Molodovsky, N. (1953). A Theory of Price-Earnings Ratios. Financial Analysts Journal, 9(5), 65–80. https://doi.org/10.2469/faj.v9.n5.65
Mousapour Mamoudan, M., Ostadi, A., Pourkhodabakhsh, N., Fathollahi-Fard, A. M., & Soleimani, F. (2023). Hybrid neural network-based metaheuristics for prediction of financial markets: A case study on global gold market. Journal of Computational Design and Engineering, 10(3), 1110–1125. https://doi.org/10.1093/jcde/qwad039
Murphy, J. J., & Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Namdari, A., & Li, Z. S. (2018). Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron. 2018 IEEE Technology and Engineering Management Conference (TEMSCON), 1–6. https://doi.org/10.1109/TEMSCON.2018.8488440
Naseer, M., & Bin Tariq, Dr. Y. (2015). The Efficient Market Hypothesis: A Critical Review of the Literature. He IUP Journal of Financial Risk Management, XII(No. 4,).
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
Nielsen, M. (2015). Neural networks and deep learning (1st ed.). Determination Press.
Ochoa, P., Peraza, C., & Castillo, O. (2024). Dynamic Adjustment of an Artificial Fish Swarm Algorithm Using Shadowed Type-2 Fuzzy Logic for Enhancing Benchmark Function Optimization. In P. Melin & O. Castillo (Eds.), New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms (Vol. 1146, pp. 19–29). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-53713-4_3
Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. The Journal of Finance, 58(5), 1791–1819. https://doi.org/10.1111/1540-6261.00588
Owusu, A., & Gupta, A. (2024). Identifying the risk culture of banks using machine learning. International Journal of Managerial Finance, 20(2), 377–405. https://doi.org/10.1108/IJMF-09-2022-0422
Park, C., & Irwin, S. H. (2007). WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS? Journal of Economic Surveys, 21(4), 786–826. https://doi.org/10.1111/j.1467-6419.2007.00519.x
Peng, Y., & Jiang, H. (2016). Leverage financial news to predict stock price movements using word Embeddings and deep neural networks. In: Proceedings of the 2016 conference of the North American chapter of the association for computational Linguistics: Human language technologies, California, United States,. 12–17 pp. 374–379.
Psichogios, D. C., & Ungar, L. H. (1992). A hybrid neural network‐first principles approach to process modeling. AIChE Journal, 38(10), 1499–1511. https://doi.org/10.1002/aic.690381003
Ratchagit, M., & Xu, H. (2022). A Two-Delay Combination Model for Stock Price Prediction. Mathematics, 10(19), 3447. https://doi.org/10.3390/math10193447
Rather, A. M., Agarwal, A., & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234–3241. https://doi.org/10.1016/j.eswa.2014.12.003
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
Saettele, J. (2017). Sentiment in the forex market: Indicators and strategies to profit from crowd behavior and market extremes (1st ed.). John Wiley & Sons, Incorporated.
Sarmiento, E. L. (2017). Análisis de flujos de información de la red social Twitter. Bogotá: University Francisco José de Caldas.
Schwager, J. D. (2012). Market wizards: Interviews with top traders: [with a new preface and afterword] (xxv, 480 Seiten eds.). John Wiley & Sons, Incorporated.
Senapati, M. R., Das, S., & Mishra, S. (2018). A Novel Model for Stock Price Prediction Using Hybrid Neural Network. Journal of The Institution of Engineers (India): Series B, 99(6), 555–563. https://doi.org/10.1007/s40031-018-0343-7
Seng, J.-L., & Yang, H.-F. (2017). The association between stock price volatility and financial news – a sentiment analysis approach. Kybernetes, 46(8), 1341–1365. https://doi.org/10.1108/K-11-2016-0307
Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies, 7(2), 26. https://doi.org/10.3390/ijfs7020026
Shah, P., Desai, K., Hada, M., Parikh, P., Champaneria, M., Panchal, D., Tanna, M., & Shah, M. (2024). A comprehensive review on sentiment analysis of social/web media big data for stock market prediction. International Journal of System Assurance Engineering and Management, 15(6), 2011–2018. https://doi.org/10.1007/s13198-023-02214-6
Shankar, P., Rohith, K. N., & Karthikeyan, M. (2024). Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis. In R. Sharma & K. Mehta (Eds.), Deep Learning Tools for Predicting Stock Market Movements (1st ed., pp. 1–37). Wiley. https://doi.org/10.1002/9781394214334.ch1
Sharma, D. K., Hota, H. S., Brown, K., & Handa, R. (2022). Integration of genetic algorithm with artificial neural network for stock market forecasting. International Journal of System Assurance Engineering and Management, 13(S2), 828–841. https://doi.org/10.1007/s13198-021-01209-5
Sharma, S., Ahmed, S., Naseem, M., Alnumay, W. S., Singh, S., & Cho, G. H. (2021). A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering. Sensors, 21(2), 463. https://doi.org/10.3390/s21020463
Shen, Shunrong, Haomiao, Jiang, & Tongda Zhang. (2012). Stock Market Forecasting Using Machine Learning Algorithms. Stanford: Department of Electrical Engineering, Stanford University, Pp. 1–5.
Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
Shi, Z., Hu, Y., Mo, G., & Wu, J. (2022). Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2204.02623
Shiller, R. J. (1980). Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends? Cambridge: National Bureau of Economic Research.
Smith, D. M., Wang, N., Wang, Y., & Zychowicz, E. J. (2014). Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2457289
Srivastava, D. K., & Bhambhu, L. (2010). Data classification using support vector machine. J. Theor. Appl. Inf. Technol., 12(1–7).
Srivinay, M., Kabadi, M. G., & Naik, N. (2022). A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network. Data. https://doi.org/%2010.3390/data7050051
Tahmasebi, P., & Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences, 42, 18–27. https://doi.org/10.1016/j.cageo.2012.02.004
Tiwari, Shewta, Rekha, Pandit, & Vineet Richhariya. (2010). Predicting Future Trends in Stock Market by Decision Tree Rough-Set Based Hybrid System with Hhmm. International Journal of Electronics and Computer Science Engineering 1, 1578–87.
Vallarino, D. (2025). Understanding the Market Trends: A Hybrid Approach to Stock Price Prediction Using RNNs and Transformer-Based Sentiment Analysis. Journal of Applied Economic Sciences (JAES), 21. https://doi.org/10.57017/jaes.v20.1(87).02
Venugopal, K. R., Srinivasa, K. G., & Patnaik, L. M. (2009). Fuzzy Based Neuro—Genetic Algorithm for Stock Market Prediction. In Soft Computing for Data Mining Applications (Vol. 190, pp. 139–166). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-00193-2_7
Viktorovich, B. V. (2024). Investment Strategies Based on Fundamental Stock Analysis. Universal Library of Business and Economics, 01(02), 26–30. https://doi.org/10.70315/uloap.ulbec.2024.0102005
Wang, J.-J., Wang, J.-Z., Zhang, Z.-G., & Guo, S.-P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766. https://doi.org/10.1016/j.omega.2011.07.008
Wang, Y., Liu, H., Guo, Q., Xie, S., & Zhang, X. (2019). Stock Volatility Prediction by Hybrid Neural Network. IEEE Access, 7, 154524–154534. https://doi.org/10.1109/ACCESS.2019.2949074
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2022). Transformers in Time Series: A Survey. https://doi.org/10.48550/ARXIV.2202.07125
Wermter, S., & Sun, R. (2000). An Overview of Hybrid Neural Systems. In S. Wermter & R. Sun (Eds.), Hybrid Neural Systems (Vol. 1778, pp. 1–13). Springer Berlin Heidelberg. https://doi.org/10.1007/10719871_1
Wu, H., Liu, Y., & Wang, J. (2020). Review of Text Classification Methods on Deep Learning. Computers, Materials & Continua, 63(3), 1309–1321. https://doi.org/10.32604/cmc.2020.010172
Yoshihara, A., Fujikawa, K., Seki, K., & Uehara, K. (2014). Predicting Stock Market Trends by Recurrent Deep Neural Networks. In D.-N. Pham & S.-B. Park (Eds.), PRICAI 2014: Trends in Artificial Intelligence (Vol. 8862, pp. 759–769). Springer International Publishing. https://doi.org/10.1007/978-3-319-13560-1_60
You, H., & Cao, K. (2021). Fundamental Analysis via Machine Learning. Conference Paper, 2021.
Yu, J., & Zhao, J. (2020). Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm. Complexity, 2020, 1–13. https://doi.org/10.1155/2020/6035372
Yu, L., Hu, L., & Tang, L. (2016). Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1891–1904. https://doi.org/10.1109/TKDE.2016.2545660
Zhang, F., Li, J., Wang, Y., Guo, L., Wu, D., Wu, H., & Zhao, H. (2021). Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment. Sensors, 21(17), 5802. https://doi.org/10.3390/s21175802
Zhang, L., Zhang, L., Teng, W., & Chen, Y. (2013). Based on Information Fusion Technique with Data Mining in the Application of Finance Early-Warning. Procedia Computer Science, 17, 695–703. https://doi.org/10.1016/j.procs.2013.05.090
Zhao, Y., & Yang, G. (2023). Deep Learning-based Integrated Framework for stock price movement prediction. Applied Soft Computing, 133, 109921. https://doi.org/10.1016/j.asoc.2022.109921
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139. https://doi.org/10.1016/j.eswa.2016.09.027
Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 24. https://doi.org/10.1186/s40854-019-0138-0
Zhu, R., Zhong, G.-Y., & Li, J.-C. (2024). Forecasting price in a new hybrid neural network model with machine learning. Expert Systems with Applications, 249, 123697. https://doi.org/10.1016/j.eswa.2024.123697
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