Abstract
The stock trends prediction is the key interest area for the investors. If the successful stock trends prediction is achieved, then the investors can adopt a more appropriate trading strategy, and that can significantly reduce the risk of investment. But it is hard to predict the stock market due to the unpredictable fatal events called Black Swan events. In this work, we propose a deep learning framework to predict the daily stock market trends with the intent that our framework can predict the stock market even on the time periods of the Black Swan events. In this framework, the signals of various technical indicators along with the daily closing price of the stock market and other influencing stock markets are used as the input for more accurate predictions. The base module of this framework is 1D convolutional neural network (1D-CNN) and bidirectional gated recurrent unit (Bi-GRU) neural network. We conduct vast experiments on the real-world datasets from two different stock markets and show that our framework exhibit satisfactory prediction accuracy for the normal circumstances. It outperforms other existing similar works during the periods of Black Swan events.
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Bhanja, S., Das, A. (2021). A Deep Learning Framework to Forecast Stock Trends Based on Black Swan Events. In: Mandal, J.K., Mukhopadhyay, S., Unal, A., Sen, S.K. (eds) Proceedings of International Conference on Innovations in Software Architecture and Computational Systems. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-4301-9_17
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DOI: https://doi.org/10.1007/978-981-16-4301-9_17
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