A Novel AutoCNN Model for Stock Market Index Prediction
Main Article Content
Keywords
Stock index prediction, automated deep learning, crisis periods, financial decision-making, digitalised economy
Abstract
Stock markets have a significant impact on the economic growth of countries. Predicting stock market indices has been a complex task in recent years. Indeed, many researchers and financial analysts are keenly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN, based on artificial intelligence techniques, to predict future stock market indices. AutoCNN is composed mainly of three stages: (1) A Convolutional Neural Network (CNN) for Automatic Feature Extraction; (2) The Halving Grid Search algorithm combined with a second CNN model for prediction of stock indices; and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events, such as economic, health and geopolitical international crises. The performance of the AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves to have strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.
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References
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Kumar, R., Kumar, P., & Kumar, Y. (2022). Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm. Neural Computing and Applications, 34(21), 18421–18437. https://doi.org/10.1007/s00521-022-07431-x
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Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba, 333(2006), 2013. Available from https://cmps-people.ok.ubc.ca/rlawrenc/research/Papers/nn.pdf
Leung, C. K. S., MacKinnon, R. K., & Wang, Y. (2014, July). A machine learning approach for stock price prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 274-277). https://doi.org/10.1145/2628194.2628211
Li, S., Gao, L., Han, C., Gupta, B., Alhalabi, W., & Almakdi, S. (2023). Exploring the effect of digital transformation on Firms’ innovation performance. Journal of Innovation & Knowledge, 8(1), 100317. https://doi.org/10.1016/j.jik.2023.100317
Liu, Q., Tao, Z., Tse, Y., & Wang, C. (2022). Stock market prediction with deep learning: The case of China. Finance Research Letters, 46, 102209. https://doi.org/10.1016/j.frl.2021.102209
Liu, Z., Xu, Z., Rajaa, S., Madadi, M., Junior, J. C. J., Escalera, S., ... & Guyon, I. (2020, 08–14 Dec). Towards automated deep learning: Analysis of the autodl challenge series 2019. In H. J. Escalante & R. Hadsell (Eds.), Proceedings of the neurips 2019 competition and demonstration track (Vol. 123, pp. 242–252). PMLR. Retrieved from https://proceedings.mlr.press/v123/liu20a.html
Lv, P., Wu, Q., Xu, J., & Shu, Y. (2022). Stock index prediction based on time series decomposition and hybrid model. Entropy, 24(2), 146. https://doi.org/10.3390/e24020146
Markova, M. (2022, April). Convolutional neural networks for forex time series forecasting. In AIP Conference Proceedings (Volume 2459, No. 1). AIP Publishing. https://doi.org/10.1063/5.0083533
Mgadmi, N., Moussa, W., Béjaoui, A., Sadraoui, T., & Afef, G. (2021). Revisiting the nexus between digital economy and economic prosperity: evidence from a comparative analysis. Journal of Telecommunications and the Digital Economy, 9(2), 69–91. https://doi.org/10.18080/jtde.v9n2.384
Mukherjee, S., Sadhukhan, B., Sarkar, N., Roy, D., & De, S. (2023). Stock market prediction using deep learning algorithms. CAAI Transactions on Intelligence Technology, 8(1), 82–94. https://doi.org/10.1049/cit2.12059
Murkute, A., & Sarode, T. (2015). Forecasting market price of stock using artificial neural network. International Journal of Computer Applications, 124(12), 11–15. https://doi.org/10.5120/ijca2015905681
Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
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Reddy, C. V. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. http://dx.doi.org/10.2139/ssrn.3451677
Reid, D., Hussain, A. J., & Tawfik, H. (2014). Financial time series prediction using spiking neural networks. PloS one, 9(8), e103656. https://doi.org/10.1371/journal.pone.0103656
Romano, J. P., & Wolf, M. (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73(4), 1237–1282. https://doi.org/10.1111/j.1468-0262.2005.00615.x
Samarawickrama, A. J. P., & Fernando, T. G. I. (2017, December). A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICIINFS.2017.8300345
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Srivinay, Manujakshi, B. C., Kabadi, M. G., & Naik, N. (2022). A hybrid stock price prediction model based on pre and deep neural network. Data, 7(5), 51. https://doi.org/10.3390/data7050051
Strauss, H., Fawcett, T., & Schutte, D. (2020). Tax risk assessment and assurance reform in response to the digitalised economy. Journal of Telecommunications and the Digital Economy, 8(4), 96–126. https://doi.org/10.18080/jtde.v8n4.306
Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017, June). Deep learning for stock market prediction from financial news articles. In 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA) (pp. 60–65). IEEE. https://doi.org/10.1109/CIVEMSA.2017.7995302
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599–606. https://doi.org/10.1016/j.procs.2020.03.326
Wu, J. M. T., Li, Z., Srivastava, G., Tasi, M. H., & Lin, J. C. W. (2021). A graph‐based convolutional neural network stock price prediction with leading indicators. Software: Practice and Experience, 51(3), 628–644. https://doi.org/10.1002/spe.2915
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Zheng, J., Wang, Y., Li, S., & Chen, H. (2021). The stock index prediction based on SVR model with bat optimization algorithm. Algorithms, 14(10), 299. https://doi.org/10.3390/a14100299
Zhou, F., Zhou, H., Yang, Z., & Gu, L. (2021). IF2CNN: Towards non-stationary time series Feature Selection by integrating iterative filtering and convolutional neural networks. Expert Systems with Applications, 170, 114527. https://doi.org/10.1016/j.eswa.2020.114527
Zouaghia, Z., Aouina, Z. K., & Said, L. B. (2023, October). Hybrid Machine Learning Model for Predicting NASDAQ Composite Index. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–6). IEEE. https://doi.org/10.1109/ISNCC58260.2023.10323903
Abraham, A., Nath, B., & Mahanti, P. K. (2001). Hybrid intelligent systems for stock market analysis. In Computational Science-ICCS 2001: International Conference San Francisco, CA, USA, May 28—30, 2001 Proceedings, Part II 1 (pp. 337–345). Springer: Berlin, Heidelberg. https://doi.org/10.1007/3-540-45718-6_38
Agbloyor, E. K., Abor, J. Y., Adjasi, C. K. D., & Yawson, A. (2014). Private capital flows and economic growth in Africa: The role of domestic financial markets. Journal of International Financial Markets, Institutions and Money, 30, 137–152. https://doi.org/10.1016/j.intfin.2014.02.003
Aghapour, A., Arian, H. R., & Seco, L. A. (2023). Deep-Time Neural Networks: An Efficient Approach for Solving High-Dimensional PDEs. Available at SSRN. http://dx.doi.org/10.2139/ssrn.4554574
Carlsson, B. (2004). The Digital Economy: what is new and what is not? Structural change and economic dynamics, 15(3), 245–264. https://doi.org/10.1016/j.strueco.2004.02.001
Chen, X., Yan, D., & Chen, W. (2022). Can the digital economy promote FinTech development? Growth and Change, 53(1), 221–247. https://doi.org/10.1111/grow.12582
Dadhich, M., Pahwa, M. S., Jain, V., & Doshi, R. (2021). Predictive models for stock market index using stochastic time series ARIMA modeling in emerging economy. In Advances in Mechanical Engineering: Select Proceedings of CAMSE 2020 (pp. 281-290). Springer Singapore. https://doi.org/10.1007/978-981-16-0942-8_26
Dey, P., Hossain, E., Hossain, M. I., Chowdhury, M. A., Alam, M. S., Hossain, M. S., & Andersson, K. (2021). Comparative analysis of recurrent neural networks in stock price prediction for different frequency domains. Algorithms, 14(8), 251. https://doi.org/10.3390/a14080251
Elman, J. (1998). Generalization, simple recurrent networks, and the emergence of structure. In Proceedings of the twentieth annual conference of the Cognitive Science Society (p. 6). Mahwah, NJ: Lawrence Erlbaum Associates. Retrieved from https://langev.com/pdf/elman98generalizationSimple.pdf
Faes, L., Wagner, S. K., Fu, D. J., Liu, X., Korot, E., Ledsam, J. R., ... & Keane, P. A. (2019). Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health, 1(5), e232–e242. https://doi.org/10.1016/S2589-7500(19)30108-6
Fathali, Z., Kodia, Z., & Ben Said, L. (2022). Stock market prediction of Nifty 50 index applying machine learning techniques. Applied Artificial Intelligence, 36(1), 2111134. https://doi.org/10.1080/08839514.2022.2111134
Gao, Y., Wang, R., & Zhou, E. (2021). Stock prediction based on optimized LSTM and GRU models. Scientific Programming, 2021, 1–8. https://doi.org/10.1155/2021/4055281
Glushchenko, T. E., Khodarinova, N. V., Ishchenko, O. V., Shaposhnikov, V. L., & Aksenova, Z. A. (2020). The development of cooperation in the digital economy based on scientific research by av chayanov. In Frontier Information Technology and Systems Research in Cooperative Economics (pp. 43–52). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-57831-2_5
Goonatilake, R., & Herath, S. (2007). The volatility of the stock market and news. International Research Journal of Finance and Economics, 3(11), 53–65. Retrieved from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=567df765560dfb426c184bd34b47510eaaabb500
Grieger, M. (2003). Electronic marketplaces: A literature review and a call for supply chain management research. European Journal of Operational Research, 144(2), 280–294. https://doi.org/10.1016/S0377-2217(02)00394-6
Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273–285. https://doi.org/10.1016/j.eswa.2019.03.029
Jung, J. O., Crnovrsanin, N., Wirsik, N. M., Nienhüser, H., Peters, L., Popp, F., ... & Schmidt, T. (2023). Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer. Journal of Cancer Research and Clinical Oncology, 149(5), 1691–1702. https://doi.org/10.1007/s00432-022-04063-5
Kompella, S., & Chakravarthy Chilukuri, K. C. C. (2020). Stock market prediction using machine learning methods. International Journal of Computer Engineering and Technology, 10(3), 2019. Available at SSRN: https://ssrn.com/abstract=3554945
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097–1105. https://doi.org/10.1145/3065386 doi: 10.1145/3065386
Kumar, R., Kumar, P., & Kumar, Y. (2022). Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm. Neural Computing and Applications, 34(21), 18421–18437. https://doi.org/10.1007/s00521-022-07431-x
Kurle, R., Cseke, B., Klushyn, A., Van Der Smagt, P., & Günnemann, S. (2019). Continual learning with Bayesian neural networks for non-stationary data. In International Conference on Learning Representations (pp. 1–28). Retrieved from https://openreview.net/pdf?id=SJlsFpVtDB
Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba, 333(2006), 2013. Available from https://cmps-people.ok.ubc.ca/rlawrenc/research/Papers/nn.pdf
Leung, C. K. S., MacKinnon, R. K., & Wang, Y. (2014, July). A machine learning approach for stock price prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 274-277). https://doi.org/10.1145/2628194.2628211
Li, S., Gao, L., Han, C., Gupta, B., Alhalabi, W., & Almakdi, S. (2023). Exploring the effect of digital transformation on Firms’ innovation performance. Journal of Innovation & Knowledge, 8(1), 100317. https://doi.org/10.1016/j.jik.2023.100317
Liu, Q., Tao, Z., Tse, Y., & Wang, C. (2022). Stock market prediction with deep learning: The case of China. Finance Research Letters, 46, 102209. https://doi.org/10.1016/j.frl.2021.102209
Liu, Z., Xu, Z., Rajaa, S., Madadi, M., Junior, J. C. J., Escalera, S., ... & Guyon, I. (2020, 08–14 Dec). Towards automated deep learning: Analysis of the autodl challenge series 2019. In H. J. Escalante & R. Hadsell (Eds.), Proceedings of the neurips 2019 competition and demonstration track (Vol. 123, pp. 242–252). PMLR. Retrieved from https://proceedings.mlr.press/v123/liu20a.html
Lv, P., Wu, Q., Xu, J., & Shu, Y. (2022). Stock index prediction based on time series decomposition and hybrid model. Entropy, 24(2), 146. https://doi.org/10.3390/e24020146
Markova, M. (2022, April). Convolutional neural networks for forex time series forecasting. In AIP Conference Proceedings (Volume 2459, No. 1). AIP Publishing. https://doi.org/10.1063/5.0083533
Mgadmi, N., Moussa, W., Béjaoui, A., Sadraoui, T., & Afef, G. (2021). Revisiting the nexus between digital economy and economic prosperity: evidence from a comparative analysis. Journal of Telecommunications and the Digital Economy, 9(2), 69–91. https://doi.org/10.18080/jtde.v9n2.384
Mukherjee, S., Sadhukhan, B., Sarkar, N., Roy, D., & De, S. (2023). Stock market prediction using deep learning algorithms. CAAI Transactions on Intelligence Technology, 8(1), 82–94. https://doi.org/10.1049/cit2.12059
Murkute, A., & Sarode, T. (2015). Forecasting market price of stock using artificial neural network. International Journal of Computer Applications, 124(12), 11–15. https://doi.org/10.5120/ijca2015905681
Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
Reddy, C. V. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. http://dx.doi.org/10.2139/ssrn.3451677
Reid, D., Hussain, A. J., & Tawfik, H. (2014). Financial time series prediction using spiking neural networks. PloS one, 9(8), e103656. https://doi.org/10.1371/journal.pone.0103656
Romano, J. P., & Wolf, M. (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73(4), 1237–1282. https://doi.org/10.1111/j.1468-0262.2005.00615.x
Samarawickrama, A. J. P., & Fernando, T. G. I. (2017, December). A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICIINFS.2017.8300345
Shinde, P. P., & Shah, S. (2018, August). A review of machine learning and deep learning applications. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCUBEA.2018.8697857
Srivinay, Manujakshi, B. C., Kabadi, M. G., & Naik, N. (2022). A hybrid stock price prediction model based on pre and deep neural network. Data, 7(5), 51. https://doi.org/10.3390/data7050051
Strauss, H., Fawcett, T., & Schutte, D. (2020). Tax risk assessment and assurance reform in response to the digitalised economy. Journal of Telecommunications and the Digital Economy, 8(4), 96–126. https://doi.org/10.18080/jtde.v8n4.306
Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017, June). Deep learning for stock market prediction from financial news articles. In 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA) (pp. 60–65). IEEE. https://doi.org/10.1109/CIVEMSA.2017.7995302
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599–606. https://doi.org/10.1016/j.procs.2020.03.326
Wu, J. M. T., Li, Z., Srivastava, G., Tasi, M. H., & Lin, J. C. W. (2021). A graph‐based convolutional neural network stock price prediction with leading indicators. Software: Practice and Experience, 51(3), 628–644. https://doi.org/10.1002/spe.2915
Yi, H., & Bui, K. H. N. (2020). An automated hyperparameter search-based deep learning model for highway traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 22(9), 5486–5495. https://doi.org/10.1109/TITS.2020.2987614
Zheng, J., Wang, Y., Li, S., & Chen, H. (2021). The stock index prediction based on SVR model with bat optimization algorithm. Algorithms, 14(10), 299. https://doi.org/10.3390/a14100299
Zhou, F., Zhou, H., Yang, Z., & Gu, L. (2021). IF2CNN: Towards non-stationary time series Feature Selection by integrating iterative filtering and convolutional neural networks. Expert Systems with Applications, 170, 114527. https://doi.org/10.1016/j.eswa.2020.114527
Zouaghia, Z., Aouina, Z. K., & Said, L. B. (2023, October). Hybrid Machine Learning Model for Predicting NASDAQ Composite Index. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–6). IEEE. https://doi.org/10.1109/ISNCC58260.2023.10323903
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Mar 27, 2024
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Zouaghia, Z., Kodia, Z., & Ben Said, L. (2024). A Novel AutoCNN Model for Stock Market Index Prediction. Journal of Telecommunications and the Digital Economy, 12(1), 612–636. https://doi.org/10.18080/jtde.v12n1.843
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Special Issue: Emerging Technologies and Innovation for Digital Economy and Transformation
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