Abstract
This paper aims to develop deep learning models for forecasting stock’s mid-price movements based on the high-frequency limit order book (LOB) data. We acquire a relatively large (\(\sim \)15GB) dataset from the well-known Wharton Research Data Services (WRDS), which contains Millisecond Trade and Quote, consolidated from “Daily Product” in WRDS. Stock prices in millisecond are carefully aggregated to stock prices in seconds so that stock price trends remains relatively the same after the aggregation. To predict the stock price, we apply popular machine learning models: ResNet50, LSTM, and two of their hybrid forms. Our tested results are comparable with other recent studies regarding accuracy and F1-score.
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References
Abadi, M., et al.: Tensorflow: Large-scale machine learning on heterogeneous systems (2015)
Chollet, F., et al.: Keras (2015). https://keras.io
Doering, J., Fairbank, M., Markose, S.: Convolutional neural networks applied to high-frequency market microstructure forecasting. 2017 9th Computer Science and Electronic Engineering (CEEC), pp. 31–36. IEEE (2017)
Fend, V.: An overview of resnet and its variant
Gandhmal, D.P., Kumar, K.: Systematic analysis and review of stock market prediction techniques. Comput. Sci. Rev. 34, 100190 (2019)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning, vol. 1. MIT Press Cambridge (2016)
Grandini, M., Bagli, E., Visani, G.: Metrics for multi-class classification: an overview, arXiv preprint arXiv:2008.05756 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016-December (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. ACM International Conference Proceeding Series, pp. 145–151 (2012)
Ozbayoglu, A.M., Gudelek, M.U., Sezer, O.B.: Deep learning for financial applications: A survey. Appli. Soft Comput. J. 93, 106384 (2020)
Sirignano, J.A.: Deep learning for limit order books. Quant. Finance 19, 549–570 (2019)
Tsantekidis, A., et al.: Using deep learning to detect price change indications in financial markets. In: 25th European Signal Processing Conference (EUSIPCO), pp. 2511–251 (2017)
Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., Iosifidis, A.:Forecasting stock prices from the limit order book using convolutional neural networks. In: Proceedings - 2017 IEEE 19th Conference on Business Informatics, CBI 2017, vol. 1, pp. 7–12 (2017)
Zavadskaya, A., et al.: Artificial intelligence in finance: Forecasting stock market returns using artificial neural networks (2017)
Zhang, Z., Zohren, S., Roberts, S.: DeepLOB: Deep convolutional neural networks for limit order books. IEEE Trans. Signal Process. 67, 3001–3012 (2019)
Acknowledgment
We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study.
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Nguyen, DP. et al. (2022). Deep Hybrid Models for Forecasting Stock Midprices from the High-Frequency Limit Order Book. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_26
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DOI: https://doi.org/10.1007/978-981-19-8069-5_26
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