Stock Market Forecasting Trends and Price using Different Machine Learning Techniques: Systematic Review
DOI:
https://doi.org/10.31642/JoKMC/2018/130109Keywords:
stock market, time series, forecasting, Deep learningAbstract
Financial stock markets play a significant role in supporting the economy by enhancing market liquidity, facilitating risk-sharing, minimizing the costs of financing, and contributing to the capital market. These functions offer more opportunities for economic growth. Stock market forecasting of prices or trends is a critical endeavor for investors and financial institutions to mitigate risk, optimize returns, and improve portfolio management. To address these challenges, numerous approaches have been proposed, involving time series forecasting, machine learning models, deep learning, ensemble techniques, and hybrid forecasting, which enhance the accuracy and reliability of forecasts. This study provides a systematic review of the literature on stock market forecasting, highlighting methodologies through comparative tables.
Downloads
References
[1] A. Buche and M. B. Chandak, “Stock Market Forecasting Techniques: A Survey,” ARPN J. Eng. Appl. Sci., vol. 14, no. 5, pp. 1649–1655, 2019, doi: 10.36478/JEASCI.2019.1649.1655.
[2] Q. Q. Abuein, M. Q. Shatnawi, E. Y. Aljawarneh, and A. Manasrah, “Time Series Forecasting Model for the Stock Market using LSTM and SVR,” Int. J. Adv. Soft Comput. its Appl., vol. 16, no. 1, pp. 169–185, 2024, doi: 10.15849/IJASCA.240330.10.
[3] Rhoda Adura Adeleye, Tula Sunday Tubokirifuruar, Binaebi Gloria Bello, Ndubuisi Leonard Ndubuisi, Onyeka Franca Asuzu, and Oluwaseyi Rita Owolabi, “Machine Learning for Stock Market Forecasting: a Review of Models and Accuracy,” Financ. Account. Res. J., vol. 6, no. 2, pp. 112–124, 2024, doi: 10.51594/farj.v6i2.783.
[4] F. Kamalov, I. Gurrib, and K. Rajab, “Financial Forecasting with Machine Learning: Price Vs Return,” J. Comput. Sci., vol. 17, no. 3, pp. 251–264, 2021, doi: 10.3844/jcssp.2021.251.264.
[5] K. Maung and N. R. Swanson, “A survey of models and methods used for forecasting when investing in financial markets,” Int. J. Forecast., no. December 2023, 2025, doi: 10.1016/j.ijforecast.2025.03.002.
[6] A. Rjumohan, “Stock Markets: An Overview and A Literature Review,” Munich Pers. RePEc Arch., no. 1855, pp. 12–52, 2019, [Online]. Available: https://mpra.ub.uni-muenchen.de/101855/1/MPRA_paper_101855.pdf
[7] R. Chopra and G. D. Sharma, “Chopra, R., & Sharma, G. D. (2021). Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda. Journal of Risk and Financial Management, 14(11). https://doi.org/10.3390/jrfm14110526Application of Artificia,” J. Risk Financ. Manag., vol. 14, no. 11, 2021.
[8] M. Darwish, E. E. Hassanien, and A. H. B. Eissa, “Stock Market Forecasting: From Traditional Predictive Models to Large Language Models,” Comput. Econ., 2025, doi: 10.1007/s10614-025-11024-w.
[9] K. R. Baskaran and B. Kaviya, “Stock Market Prediction Using Machine Learning and Deep Learning Algorithms,” Sustain. Digit. Technol. Smart Cities Heal. Commun. Transp., pp. 127–138, 2023, doi: 10.1201/9781003307716-12.
[10] X. Kong et al., Deep learning for time series forecasting: a survey, vol. 16, no. 7. Springer Berlin Heidelberg, 2025. doi: 10.1007/s13042-025-02560-w.
[11] K. Macharia, “Enhancing Stock Market Forecasting with ARIMA and Artificial Neural Networks,” Africa J. Tech. Vocat. Educ. Train., vol. 10, no. 1, pp. 129–137, 2025, doi: 10.69641/afritvet.2025.101186.
[12] M. R. Pathak and J. M. Kapadia, “Indian Stock Market Predictive Efficiency using the ARIMA Model,” Srusti Manag. Rev., vol. 14, no. 1, pp. 10–15, 2021.
[13] S. Kumar, M. Srivastava, and V. Prakash, “Comparative Analysis of ARIMA, Deep Learning, and Lasso Regression Models for Time Series Forecasting: Assessing Accuracy, Robustness, and Computational Efficiency,” CEUR Workshop Proc., vol. 3619, pp. 12–22, 2023.
[14] A. Mondal, S. Mukherjee, and S. Sinha, “Forecasting stock prices using ARIMA model,” no. November 1995, pp. 48–55, 2019.
[15] A. Ashok and C. P. Prathibhamol, “Improved Analysis of Stock Market Prediction: (ARIMA-LSTM-SMP),” 2021 Int. Conf. Nascent Technol. Eng. ICNET 2021 - Proc., no. Icnte, 2021, doi: 10.1109/ICNTE51185.2021.9487745.
[16] S. Khan and H. Alghulaiakh, “ARIMA model for accurate time series stocks forecasting,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 7, pp. 524–528, 2020, doi: 10.14569/IJACSA.2020.0110765.
[17] W. Dassanayake, I. Ardekani, N. Gamage, C. Jayawardena, and H. Sharifzadeh, “Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand,” ICAC 2021 - 3rd Int. Conf. Adv. Comput. Proc., pp. 13–18, 2021, doi: 10.1109/ICAC54203.2021.9671132.
[18] F. Hossain, D. C. Nandi, K. Mohammed, and K. Uddin, “Prediction of Banking Sectors Financial Data of Dhaka Stock Exchange Using Autoregressive Integrated Moving Average (ARIMA) Approach,” Int. J. Mater. Math. Sci., vol. 2, no. 4, pp. 64–70, 2020, doi: 10.34104/ijmms.020.064070.
[19] Fajar Dwi Wibowo, Thanh-Tuan Dang, and Chia-Nan Wang, “Forecasting Indonesia Stock Price Using Time Series Analysis and Machine Learning in R,” Indones. Sch. Sci. Summit Taiwan Proceeding, vol. 4, no. August, pp. 103–108, 2022, doi: 10.52162/4.2022166.
[20] J. Shah, D. Vaidya, and M. Shah, “A comprehensive review on multiple hybrid deep learning approaches for stock prediction,” Intell. Syst. with Appl., vol. 16, no. July, p. 200111, 2022, doi: 10.1016/j.iswa.2022.200111.
[21] M. More, “Analyzing the Impact of Multiple Stock Indices in Prediction of US Dollar Index,” p. 20, 2020.
[22] Y. AKER, “Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model,” Eur. J. Sci. Technol., no. 35, pp. 89–93, 2022, doi: 10.31590/ejosat.1066722.
[23] K. Sharma, R. Bhalla, and G. Ganesan, “Time Series Forecasting Using FB-Prophet,” CEUR Workshop Proc., vol. 3445, pp. 59–65, 2022.
[24] S. N. Pindiga, “Time-Series Forecasting: Predicting Stock Index Using Arima and Facebooks Prophet Model,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 6, pp. 4832–4839, 2022, doi: 10.22214/ijraset.2022.45073.
[25] L. S. Sheeba, N. Gupta, A. R. Ragavender M, and D. Divya Chennai, “Time Series Model for Stock Market Prediction Utilising Prophet,” Turkish J. Comput. Math. Educ. __________________________________________________________________________________ 4529 Res. Artic., vol. 12, no. 6, pp. 4529–4534, 2021.
[26] Suresh N, Priya B, Sai Teja M, Sai Krishna B, and Lakshmi G, “Historical analysis and forecasting of stock market using fbprophet,” South Asian J. Eng. Technol., vol. 12, no. 3, pp. 152–157, 2022, doi: 10.26524/sajet.2022.12.43.
[27] Daryl, A. Winata, S. Kumara, and D. Suhartono, “Predicting Stock Market Prices using Time Series SARIMA,” Proc. 2021 1st Int. Conf. Comput. Sci. Artif. Intell. ICCSAI 2021, no. October, pp. 92–99, 2021, doi: 10.1109/ICCSAI53272.2021.9609720.
[28] A. Zatri and K. Zine-Dine, “Time Series Analysis of Netflix’s Stock Closing Prices: From Data Processing to Forecasting,” Ing. des Syst. d’Information, vol. 30, no. 4, pp. 1005–1013, 2025, doi: 10.18280/isi.300417.
[29] M. Kishor, B. Supervisor, and M. A. Makki, “Comparative study among ARIMA, SARIMA & XGBoost for prediction of NIFTY IT index,” no. January, pp. 1–38, 2024.
[30] M. S. Boudrioua and A. Boudrioua, “Modeling and Forecasting the Algerian Stock Exchange Using the Box-Jenkins Methodology,” J. Econ. , Financ. Account. Stud. ( JEFAS ), vol. 2, no. 1, pp. 1–15, 2020, doi: 10.5281/zenodo.3903241.
[31] Y. T. Choy, M. H. Hoo, and K. C. Khor, “Price Prediction Using Time-Series Algorithms for Stocks Listed on Bursa Malaysia,” 2021 2nd Int. Conf. Artif. Intell. Data Sci. AiDAS 2021, pp. 0–4, 2021, doi: 10.1109/AiDAS53897.2021.9574445.
[32] G. Kemalbay and O. Berak Korkmazoglu, “Sarima-arch versus genetic programming in stock price prediction,” Sigma J. Eng. Nat. Sci., vol. 39, no. 2, pp. 110–122, 2021, doi: 10.14744/sigma.2021.00001.
[33] I. K. Nti, A. F. Adekoya, and B. A. Weyori, “Efficient Stock-Market Prediction Using Ensemble Support Vector Machine,” Open Comput. Sci., vol. 10, no. 1, pp. 153–163, 2020, doi: 10.1515/comp-2020-0199.
[34] P. Chhajer, M. Shah, and A. Kshirsagar, “The applications of artificial neural networks , support vector machines , and long – short term memory for stock market prediction,” Decis. Anal. J., vol. 2, no. June 2021, p. 100015, 2022, doi: 10.1016/j.dajour.2021.100015.
[35] J. Grudniewicz and R. Slepaczuk, “Research in International Business and Finance Application of machine learning in algorithmic investment strategies on global stock markets ☆,” vol. 66, 2023, doi: 10.1016/j.ribaf.2023.102052.
[36] D. Das and A. S. Sadiq, “A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period,” 2022, doi: 10.1088/1742-6596/2287/1/012018.
[37] H. H. Htun, M. Biehl, and N. Petkov, “Forecasting relative returns for S&P 500 stocks using machine learning,” Financ. Innov., vol. 10, no. 1, 2024, doi: 10.1186/s40854-024-00644-0.
[38] I. R. Parray, S. S. Khurana, M. Kumar, and A. A. Altalbe, “Time series data analysis of stock price movement using machine learning techniques,” Soft Comput., vol. 24, no. 21, pp. 16509–16517, 2020, doi: 10.1007/s00500-020-04957-x.
[39] A. Info, “Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms : The Sample of the Istanbul Stock Exchange,” vol. 34, no. 1, pp. 63–82, 2021, doi: 10.35378/gujs.679103.
[40] N. Ayyildiz and O. Iskenderoglu, “Heliyon How effective is machine learning in stock market predictions ?,” Heliyon, vol. 10, no. 2, p. e24123, 2024, doi: 10.1016/j.heliyon.2024.e24123.
[41] A. Bin Omar, S. Huang, A. A. Salameh, H. Khurram, and M. Fareed, “Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period,” Front. Environ. Sci., vol. 10, no. July, pp. 1–10, 2022, doi: 10.3389/fenvs.2022.917047.
[42] M. Vijh, D. Chandola, V. A. Tikkiwal, and A. Kumar, “Stock Closing Price Prediction using Machine Learning Techniques,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 599–606, 2020, doi: 10.1016/j.procs.2020.03.326.
[43] A. U. Haq, A. Zeb, Z. Lei, and D. Zhang, “Forecasting daily stock trend using multi-filter feature selection and deep learning,” Expert Syst. Appl., vol. 168, no. September 2020, p. 114444, 2021, doi: 10.1016/j.eswa.2020.114444.
[44] E. K. Ampomah, Z. Qin, and G. Nyame, “Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement,” Inf., vol. 11, no. 6, 2020, doi: 10.3390/info11060332.
[45] S. S. Roy, R. Chopra, K. C. Lee, C. Spampinato, and B. Mohammadi-Ivatlood, “Random forest, gradient boosted machines and deep neural network for stock price forecasting: A comparative analysis on South Korean companies,” Int. J. Ad Hoc Ubiquitous Comput., vol. 33, no. 1, pp. 62–71, 2020, doi: 10.1504/IJAHUC.2020.104715.
[46] H. J. Park, Y. Kim, and H. Y. Kim, “Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework,” Appl. Soft Comput., vol. 114, 2022, doi: 10.1016/j.asoc.2021.108106.
[47] L. Yin, B. Li, P. Li, and R. Zhang, “Research on stock trend prediction method based on optimized random forest,” CAAI Trans. Intell. Technol., vol. 8, no. 1, pp. 274–284, 2023, doi: 10.1049/cit2.12067.
[48] B. S. Abunasser, S. M. Daud, and S. S. Abu-Naser, “Predicting Stock Prices using Artificial Intelligence: A Comparative Study of Machine Learning Algorithms,” Int. J. Adv. Soft Comput. its Appl., vol. 15, no. 3, pp. 41–53, 2023, doi: 10.15849/IJASCA.231130.03.
[49] K. Olorunnimbe and H. Viktor, Deep learning in the stock market—a systematic survey of practice, backtesting, and applications, vol. 56, no. 3. Springer Netherlands, 2023. doi: 10.1007/s10462-022-10226-0.
[50] H. Oukhouya et al., “Predictive modeling for the Moroccan financial market: a nonlinear time series and deep learning approach,” Futur. Bus. J., vol. 11, no. 1, 2025, doi: 10.1186/s43093-025-00646-z.
[51] A. Yadav, C. K. Jha, and A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 2091–2100, 2020, doi: 10.1016/j.procs.2020.03.257.
[52] Y. Wang, Y. Liu, M. Wang, and R. Liu, “LSTM Model Optimization on Stock Price Forecasting,” Proc. - 2018 17th Int. Symp. Distrib. Comput. Appl. Bus. Eng. Sci. DCABES 2018, pp. 173–177, 2018, doi: 10.1109/DCABES.2018.00052.
[53] W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-based model to forecast stock prices,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6622927.
[54] M. Jarrah and M. Derbali, “Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning,” Appl. Sci., vol. 13, no. 14, 2023, doi: 10.3390/app13148356.
[55] M. Buczyński, M. Chlebus, K. Kopczewska, and M. Zajenkowski, “Financial Time Series Models—Comprehensive Review of Deep Learning Approaches and Practical Recommendations †,” Eng. Proc., vol. 39, no. 1, pp. 1–13, 2023, doi: 10.3390/engproc2023039079.
[56] M. T. Gholami and A. Shahabi, “Comparison of deep learning methods with traditional financial time series models for forecasting the TEPIX index in the Tehran Stock Exchange,” Syst. Soft Comput., vol. 7, p. 200395, 2025, doi: 10.1016/j.sasc.2025.200395.
[57] S. Teixeira, Z. De Pauli, M. Kleina, and W. Hugo, “for Brazilian Stock Market Prediction,” Ann. Data Sci., no. 123456789, 2020, doi: 10.1007/s40745-020-00305-w.
[58] G. W. R. I. Wijesinghe and R. M. K. T. Rathnayaka, “Stock market price forecasting using ARIMA vs ANN; A Case study from CSE,” ICAC 2020 - 2nd Int. Conf. Adv. Comput. Proc., pp. 269–274, 2020, doi: 10.1109/ICAC51239.2020.9357288.
[59] P. V. Chandrika and K. S. Srinivasan, “Predicting stock market movements using artificial neural networks,” Univers. J. Account. Financ., vol. 9, no. 3, pp. 405–410, 2021, doi: 10.13189/ujaf.2021.090315.
[60] P. Misra and S. Chaurasia, “Data-driven trend forecasting in stock market using machine learning techniques,” J. Inf. Technol. Res., vol. 13, no. 1, pp. 130–149, 2020, doi: 10.4018/JITR.2020010109.
[61] D. Kumar, P. K. Sarangi, and R. Verma, “A systematic review of stock market prediction using machine learning and statistical techniques,” Mater. Today Proc., vol. 49, no. xxxx, pp. 3187–3191, 2020, doi: 10.1016/j.matpr.2020.11.399.
[62] G. W. R. I. Wijesinghe and R. M. K. T. Rathnayaka, “ARIMA and ANN Approach for forecasting daily stock price fluctuations of industries in Colombo Stock Exchange, Sri Lanka,” Proc. ICITR 2020 - 5th Int. Conf. Inf. Technol. Res. Towar. New Digit. Enlight., 2020, doi: 10.1109/ICITR51448.2020.9310826.
[63] G. Liu and W. Ma, “A quantum artificial neural network for stock closing price prediction,” Inf. Sci. (Ny)., vol. 598, pp. 75–85, 2022, doi: 10.1016/j.ins.2022.03.064.
[64] T. B. Shahi, A. Shrestha, A. Neupane, and W. Guo, “Stock price forecasting with deep learning: A comparative study,” Mathematics, vol. 8, no. 9, pp. 1–15, 2020, doi: 10.3390/math8091441.
[65] V. Polepally, N. S. N. Reddy, M. Sindhuja, N. Anjali, and K. J. Reddy, “A Deep Learning Approach for Prediction of Stock Price Based on Neural Network Models: LSTM and GRU,” 2021 12th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2021, pp. 6–9, 2021, doi: 10.1109/ICCCNT51525.2021.9579782.
[66] H. Hadhood, “Stock trend prediction using deep learning models LSTM and GRU with non-linear regression Haret Hadhood,” no. October, 2022.
[67] Z. Zhong, D. Wu, and W. Mai, “Stock Prediction Based on ARIMA Model and GRU Model,” Acad. J. Comput. Inf. Sci., vol. 6, no. 7, pp. 114–123, 2023, doi: 10.25236/ajcis.2023.060715.
[68] C. Chen, L. Xue, and W. Xing, “Research on Improved GRU-Based Stock Price Prediction Method,” Appl. Sci., vol. 13, no. 15, 2023, doi: 10.3390/app13158813.
[69] A. Irsyad, A. Prafanto, M. B. Firdaus, H. J. Setiyadi, P. P. Widagdo, and M. A. Rahmat, “Forecasting Stocks Prices with GRU and Attention Mechanism,” Proc. Int. Conf. Electr. Eng. Informatics, pp. 114–119, 2024, doi: 10.1109/ICELTICs62730.2024.10776108.
[70] A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,” Procedia Comput. Sci., vol. 170, pp. 1168–1173, 2020, doi: 10.1016/j.procs.2020.03.049.
[71] Y. Zhu, “Stock price prediction using the RNN model,” J. Phys. Conf. Ser., vol. 1650, no. 3, 2020, doi: 10.1088/1742-6596/1650/3/032103.
[72] M. R. Pahlawan, E. Riksakomara, R. Tyasnurita, A. Muklason, F. Mahananto, and R. A. Vinarti, “Stock price forecast of macro-economic factor using recurrent neural network,” IAES Int. J. Artif. Intell., vol. 10, no. 1, pp. 74–83, 2021, doi: 10.11591/ijai.v10.i1.pp74-83.
[73] N. K. Upadhyay, “Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors,” J. Manag. Serv. Sci., vol. 3, no. 1, pp. 1–9, 2023, doi: 10.54060/jmss.v3i1.42.
[74] J. M. T. Wu, Z. Li, G. Srivastava, J. Frnda, V. G. Diaz, and J. C. W. Lin, “A CNN-based Stock Price Trend Prediction with Futures and Historical Price,” Proc. - 2020 Int. Conf. Pervasive Artif. Intell. ICPAI 2020, pp. 134–139, 2020, doi: 10.1109/ICPAI51961.2020.00032.
[75] S. Mehtab and J. Sen, “Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models,” 2020 Int. Conf. Decis. Aid Sci. Appl. DASA 2020, pp. 447–453, 2020, doi: 10.1109/DASA51403.2020.9317207.
[76] N. B. Korade and M. Zuber, “Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 11, pp. 378–385, 2022, doi: 10.14569/IJACSA.2022.0131142.
[77] N. D. Ak and E. Kili, “How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting,” IEEE Access, 2022.
[78] O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 8, no. 4, pp. 1–18, 2018, doi: 10.1002/widm.1249.
[79] M. Jeyakarthic and S. Punitha, “Hybridization of Bat Algorithm with XGBOOST Model for Precise Prediction of Stock Market Directions,” no. February, 2022, doi: 10.35940/ijeat.C5535.029320.
[80] J. Xu et al., “Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks,” vol. 16, 2022, doi: 10.46300/9106.2022.16.79.
[81] S. Deng, X. Huang, Y. Zhu, Z. Su, and Z. Fu, “North American Journal of Economics and Finance Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments,” vol. 64, no. December 2021, 2023.
[82] Y. Z. B, Stock Price Prediction Method Based on XGboost Algorithm. Atlantis Press International BV, 2023. doi: 10.2991/978-94-6463-030-5.
[83] H. Oukhouya, “Comparing Machine Learning Methods — SVR , XGBoost , LSTM , and MLP — For Forecasting the Moroccan Stock Market †,” 2023.
[84] O. P. Uhunmwangho, “Comparing XGBoost and LSTM Models for Prediction of Microsoft Corp â€TM s Stock Price Direction,” vol. 4, no. 2, pp. 64–88, 2024.
[85] T. Universitas, B. Luhur, J. C. Raya, and J. Selatan, “XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting,” vol. 9, no. 4, pp. 1179–1195, 2024, doi: 10.26555/jiteki.v9i4.27712.
[86] I. K. Nti, A. F. Adekoya, and B. A. Weyori, “A comprehensive evaluation of ensemble learning for stock-market prediction,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00299-5.
[87] R. K. Ying, Y. Shou, and C. Liu, “Prediction Model of Dow Jones Index Based on LSTM-Adaboost,” 2021 IEEE 3rd Int. Conf. Commun. Inf. Syst. Comput. Eng. CISCE 2021, no. Cisce, pp. 808–812, 2021, doi: 10.1109/CISCE52179.2021.9445928.
[88] S. Mohapatra, R. Mukherjee, A. Roy, A. Sengupta, and A. Puniyani, “Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?,” J. Risk Financ. Manag., vol. 15, no. 8, 2022, doi: 10.3390/jrfm15080350.
[89] M. Nabipour, P. Nayyeri, H. Jabani, S. Shahab, and A. Mosavi, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; A Comparative Analysis,” IEEE Access, vol. 8, pp. 150199–150212, 2020, doi: 10.1109/ACCESS.2020.3015966.
[90] S. Tuarob et al., “DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction,” Financ. Innov., vol. 7, no. 1, 2021, doi: 10.1186/s40854-021-00269-7.
[91] N. Zhang, C. Gao, and M. Xiao, “LightGBM stock forecasting model based on PCA,” Proc. - 2021 2nd Int. Semin. Artif. Intell. Netw. Inf. Technol. AINIT 2021, pp. 396–399, 2021, doi: 10.1109/AINIT54228.2021.00083.
[92] Z. Li, W. Xu, and A. Li, “ScienceDirect ScienceDirect LightGBM Research on on multi multi factor factor stock stock selection selection model model based based on on LightGBM and Bayesian Bayesian Optimization Optimization and use random,” Procedia Comput. Sci., vol. 214, pp. 1234–1240, 2022, doi: 10.1016/j.procs.2022.11.301.
[93] X. Zhao, Y. Liu, and Q. Zhao, “Cost Harmonization LightGBM-Based Stock Market Prediction,” IEEE Access, vol. 11, no. August, pp. 105009–105026, 2023, doi: 10.1109/ACCESS.2023.3318478.
[94] O. Guennioui, D. Chiadmi, and M. Amghar, “Improving Global Stock Market Prediction with XGBoost and LightGBM Machine Learning Models,” Rev. Econ. Financ., vol. 21, no. 271, pp. 2603–2610, 2023, [Online]. Available: https://refpress.org/ref-vol21-a278/
[95] O. Guennioui, D. Chiadmi, and M. Amghar, “Global Stock Price Forecasting during a Period of Market Stress Using LightGBM,” Int. J. Comput. Digit. Syst., vol. 15, no. 1, pp. 19–27, 2024, doi: 10.12785/ijcds/150103.
[96] Y. Qiu, H. Y. Yang, S. Lu, and W. Chen, “A novel hybrid model based on recurrent neural networks for stock market timing,” Soft Comput., vol. 24, no. 20, pp. 15273–15290, 2020, doi: 10.1007/s00500-020-04862-3.
[97] S. Kim, S. Ku, W. Chang, W. Chang, W. Chang, and J. W. Song, “Predicting the Direction of US Stock Prices Using Effective Transfer Entropy and Machine Learning Techniques,” IEEE Access, vol. 8, pp. 111660–111682, 2020, doi: 10.1109/ACCESS.2020.3002174.
[98] D. K. Padhi, N. Padhy, A. K. Bhoi, J. Shafi, and M. F. Ijaz, “A fusion framework for forecasting financial market direction using enhanced ensemble models and technical indicators,” Mathematics, vol. 9, no. 21, 2021, doi: 10.3390/math9212646.
[99] G. R. Patra and M. N. Mohanty, “An LSTM-GRU based hybrid framework for secured stock price prediction,” J. Stat. Manag. Syst., vol. 25, no. 6, pp. 1491–1499, 2022, doi: 10.1080/09720510.2022.2092263.
[100] A. Safari, “Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction,” 2022 8th Int. Conf. Web Res. ICWR 2022, pp. 115–120, 2022, doi: 10.1109/ICWR54782.2022.9786223.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 mohammed jabardi, Hawraa Abdulnabi
This work is licensed under a Creative Commons Attribution 4.0 International License.
which allows users to copy, create extracts, abstracts, and new works from the Article, alter and revise the Article, and make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work.
How to Cite
