Top

Journal of Intelligent Information Systems

Published in:

23-07-2023 | Research

Stock market prediction with time series data and news headlines: a stacking ensemble approach

Authors: Roberto Corizzo, Jacob Rosen

Published in: Journal of Intelligent Information Systems | Issue 1/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Time series forecasting models are gaining traction in many real-world domains as valuable decision support tools. Stock market analysis is a challenging domain, characterized by a complex multi-variate and time-evolving nature, with high volatility, and multiple correlations with exogenous factors. Autoregressive, machine learning, and deep learning models for temporal data have been adopted thus far to solve this task. However, they are usually limited to the analysis of a single data source or modality, and do not collectively deal with all the inherent challenges and complexities presented by stock market data. In this paper, inspired by the promising learning capabilities of hybrid ensemble methods, we propose a novel stacking ensemble approach for stock market prediction that jointly considers news headlines, multi-variate time series data, and multiple base models as predictors. By taking multiple factors into consideration, our model is able to learn historical patterns leveraging multiple data sources and models. Our experiments showcase the ability of our model to outperform popular baselines on next-day stock market trend prediction. A portfolio analysis reveals that our method is also able to yield potential gains or capital preservation capabilities when its predictions are exploited for trading decisions.

previous article Top-N music recommendation framework for precision and novelty under diversity group size and similarity

next article Deep latent representation enhancement method for social recommendation

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

https://pypi.org/project/yahooquery/

https://pypi.org/project/TA-Lib/

https://eodhistoricaldata.com/

The dataset used in our work and the implementation of the proposed approach are publicly available at: https://github.com/rcorizzo/stock-market-stacking/

https://pypi.org/project/pmdarima/

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html

All results are conveniently accessible at: http://www.rcorizzo.com/stock-market/stacking/

Additional high-resolution portfolio analysis plots considering other stacking models are conveniently accessible at: http://www.rcorizzo.com/stock-market/stacking/

Akter, M. S., Shahriar, H., Chowdhury, R., & et al. (2022). Forecasting the risk factor of frontier markets: A novel stacking ensemble of neural network approach. Future Internet, 14(9), 252. https://doi.org/10.3390/fi14090252

Al-Shiab, M. (2006). The predictability of the amman stock exchange using the univariate autoregressive integrated moving average (arima) model. Journal of Economic and Administrative Sciences, 22(2), 17–35. https://doi.org/10.1108/10264116200600006CrossRef

Althelaya, K.A., El-Alfy, E.S.M., & Mohammed, S. (2018). Evaluation of bidirectional lstm for short-and long-term stock market prediction. In: 2018 9th International Conference on Information and Communication Systems (ICICS) 151–156. IEEE. https://doi.org/10.1109/IACS.2018.8355458

Ariyo, A.A., Adewumi, A.O., & Ayo, C.K. (2014). Stock price prediction using the arima model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation 106–112. IEEE. https://doi.org/10.1109/UKSim.2014.67

Arsov, M., Zdravevski, E., Lameski, P., & et al. (2021). Multi-horizon air pollution forecasting with deep neural networks. Sensors 21(4). https://doi.org/10.3390/s21041235

Banik, S., Sharma, N., Mangla, M., & et al. (2022). Lstm based decision support system for swing trading in stock market. Knowledge-Based Systems, 239, 107994. https://doi.org/10.1016/j.knosys.2021.107994

Barbaglia, L., Consoli, S., & Manzan, S. (2021). Exploring the predictive power of news and neural machine learning models for economic forecasting. In: Mining Data for Financial Applications: 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers 5 135–149. Springer https://doi.org/10.1007/978-3-030-66981-2_11

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166. https://doi.org/10.1109/72.279181CrossRefPubMed

Bhandari, H. N., Rimal, B., Pokhrel, N. R., & et al. (2022). Predicting stock market index using lstm. Machine Learning with Applications, 9, 100320. https://doi.org/10.1016/j.mlwa.2022.100320

Borovkova, S., & Tsiamas, I. (2019). An ensemble of lstm neural networks for high-frequency stock market classification. Journal of Forecasting, 38(6), 600–619. https://doi.org/10.1002/for.2585MathSciNetCrossRef

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324CrossRef

Ceci, M., Corizzo, R., Malerba, D., & et al. (2019). Spatial autocorrelation and entropy for renewable energy forecasting. Data Mining and Knowledge Discovery, 33(3), 698–729. https://doi.org/10.1007/s10618-018-0605-7

Chen, T. (2014). Introduction to boosted trees. University of Washington Computer Science, 22(115), 14–40.

Corizzo, R., Ceci, M., Fanaee, -T., H., & et al. (2021). Multi-aspect renewable energy forecasting. Information Sciences, 546, 701–722. https://doi.org/10.1016/j.ins.2020.08.003

Corizzo, R., Yepez-Lopez, R., Gilbert, S., & et al. (2022). Lstm-based pulmonary air leak forecasting for chest tube management. In: 2022 IEEE International Conference on Big Data (Big Data) 5217–5222. IEEE. https://doi.org/10.1109/BigData55660.2022.10020874

Dong, X., Yu, Z., Cao, W., & et al. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241–258. https://doi.org/10.1007/s11704-019-8208-z

Gao, S., Huang, Y., Zhang, S., & et al. (2020). Short-term runoff prediction with gru and lstm networks without requiring time step optimization during sample generation. Journal of Hydrology, 589, 125188. https://doi.org/10.1016/j.jhydrol.2020.125188

Greco, M., Spagnoletta, M., Appice, A., & et al. (2021). Applying machine learning to predict closing prices in stock market: A case study. In: Mining Data for Financial Applications: 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers 5 32–39. Springer. https://doi.org/10.1007/978-3-030-66981-2_3

Hochreiter, S., Bengio, Y., Frasconi, P., & et al. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A Field Guide to Dynamical Recurrent Neural Networks, 237–244. https://doi.org/10.1109/9780470544037.ch14

Kumar, U., & Jain, V. (2010). Arima forecasting of ambient air pollutants (o 3, no, no 2 and co). Stochastic Environmental Research and Risk Assessment, 24(5), 751–760. https://doi.org/10.1007/s00477-009-0361-8CrossRef

Lana, I., Del Ser, J., Velez, M., & et al. (2018). Road traffic forecasting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine, 10(2), 93–109. https://doi.org/10.1109/MITS.2018.2806634

Laurinec, P., Lóderer, M., Lucká, M., & et al. (2019). Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption. Journal of Intelligent Information Systems, 53, 219–239. https://doi.org/10.1007/s10844-019-00550-3

Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1–11. https://doi.org/10.1007/s41060-021-00279-9

Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (arima) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13. https://doi.org/10.5121/ijcsea.2014.4202CrossRef

Olorunnimbe, K., & Viktor, H. (2023). Deep learning in the stock market-a systematic survey of practice, backtesting, and applications. Artificial Intelligence Review, 56(3), 2057–2109. https://doi.org/10.1007/s10462-022-10226-0CrossRefPubMed

Pandey, R., & Singh, J. P. (2023). Bert-lstm model for sarcasm detection in code-mixed social media post. Journal of Intelligent Information Systems, 60(1), 235–254. https://doi.org/10.1007/s10844-022-00755-zCrossRef

Pankratz, A. (2009). Forecasting with univariate Box-Jenkins models: Concepts and cases. John Wiley & Sons. https://doi.org/10.1002/9780470316566

Pasquadibisceglie, V., Appice, A., Castellano, G., & et al. (2023). Darwin: An online deep learning approach to handle concept drifts in predictive process monitoring. Engineering Applications of Artificial Intelligence, 123,. https://doi.org/10.1016/j.engappai.2023.106461

Qian, C., Yu, Y., & Zhou, Z.H. (2015). Pareto ensemble pruning. In: Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.5555/2888116.2888125

Ramaswamy, S. L., & Chinnappan, J. (2022). Recognet-lstm+ cnn: a hybrid network with attention mechanism for aspect categorization and sentiment classification. Journal of Intelligent Information Systems, 58(2), 379–404. https://doi.org/10.1007/s10844-021-00692-3CrossRef

Rocha, C. N., & Rodrigues, F. (2021). Forecasting emergency department admissions. Journal of Intelligent Information Systems, 56(3), 509–528. https://doi.org/10.1007/s10844-021-00638-9CrossRef

Salisu, A. A., Gupta, R., & Ogbonna, A. E. (2022). A moving average heterogeneous autoregressive model for forecasting the realized volatility of the us stock market: Evidence from over a century of data. International Journal of Finance & Economics, 27(1), 384–400. https://doi.org/10.1002/ijfe.2158CrossRef

Sesmero, M. P., Ledezma, A. I., & Sanchis, A. (2015). Generating ensembles of heterogeneous classifiers using stacked generalization. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(1), 21–34. https://doi.org/10.1002/widm.1143CrossRef

Shah, D., Campbell, W., & Zulkernine, F.H. (2018). A comparative study of lstm and dnn for stock market forecasting. In: 2018 IEEE International Conference on Big Data (Big Data) 4148–4155. IEEE. https://doi.org/10.1109/BigData.2018.8622462

Song, H., & Choi, H. (2023). Forecasting stock market indices using the recurrent neural network based hybrid models: Cnn-lstm, gru-cnn, and ensemble models. Applied Sciences, 13(7), 4644. https://doi.org/10.3390/app13074644CrossRef

Srijiranon, K., Lertratanakham, Y., & Tanantong, T. (2022). A hybrid framework using pca, emd and lstm methods for stock market price prediction with sentiment analysis. Applied Sciences, 12(21), 10823. https://doi.org/10.3390/app122110823CrossRef

Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives, 15(4), 101–115. https://doi.org/10.1257/jep.15.4.101CrossRef

Swathi, T., Kasiviswanath, N., & Rao, A. A. (2022). An optimal deep learning-based lstm for stock price prediction using twitter sentiment analysis. Applied Intelligence, 52(12), 13675–13688. https://doi.org/10.1007/s10489-022-03175-2CrossRef

Tan, Z., Yan, Z., & Zhu, G. (2019). Stock selection with random forest: An exploitation of excess return in the chinese stock market. Heliyon, 5(8), e02310. https://doi.org/10.1016/j.heliyon.2019.e02310CrossRefPubMedCentralPubMed

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080MathSciNetCrossRef

Wang, J., Cui, Q., Sun, X., & et al. (2022). Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based lstm model. Engineering Applications of Artificial Intelligence, 113, 104908. https://doi.org/10.1016/j.engappai.2022.104908

Wang, S., Zhao, J., Shao, C., & et al. (2020). Truck traffic flow prediction based on lstm and gru methods with sampled gps data. IEEE Access, 8, 208158–208169. https://doi.org/10.1109/ACCESS.2020.3038788

Weng, B., Lu, L., Wang, X., & et al. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258–273. https://doi.org/10.1016/j.eswa.2018.06.016

Wyner, A. J., Olson, M., Bleich, J., & et al. (2017). Explaining the success of adaboost and random forests as interpolating classifiers. The Journal of Machine Learning Research, 18(1), 1558–1590. https://doi.org/10.5555/3122009.3153004

Yu, Y., Si, X., Hu, C., & et al. (2019). A review of recurrent neural networks: Lstm cells and network architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199

Zhang, D., Chen, S., Zhou, Z.H., & et al. (2008). Constraint projections for ensemble learning. In: AAAI Conference on Artificial Intelligence 758–763

Zhang, Y., Shirakawa, M., & Hara, T. (2022). Predicting temporary deal success with social media timing signals. Journal of Intelligent Information Systems, 1–19. https://doi.org/10.1007/s10844-021-00681-6

Zhao, Y., Ye, L., Pinson, P., & et al. (2018). Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting. IEEE Transactions on Power Systems, 33(5), 5029–5040. https://doi.org/10.1109/TPWRS.2018.2794450

Title: Stock market prediction with time series data and news headlines: a stacking ensemble approach
Authors: Roberto Corizzo
Jacob Rosen
Publication date: 23-07-2023
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 1/2024
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-023-00804-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2024

OIE4PA: open information extraction for the public administration

Improving graph collaborative filtering with multimodal-side-information-enriched contrastive learning

Transformer based multilingual joint learning framework for code-mixed and english sentiment analysis

Semi-supervised and ensemble learning to predict work-related stress

Global-mirror graph network for session-based recommendation

Self-supervised opinion summarization with multi-modal knowledge graph

Premium Partner