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Modeling Financial Time Series using LSTM with Trainable Initial Hidden States

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  • Jungsik Hwang

Abstract

Extracting previously unknown patterns and information in time series is central to many real-world applications. In this study, we introduce a novel approach to modeling financial time series using a deep learning model. We use a Long Short-Term Memory (LSTM) network equipped with the trainable initial hidden states. By learning to reconstruct time series, the proposed model can represent high-dimensional time series data with its parameters. An experiment with the Korean stock market data showed that the model was able to capture the relative similarity between a large number of stock prices in its latent space. Besides, the model was also able to predict the future stock trends from the latent space. The proposed method can help to identify relationships among many time series, and it could be applied to financial applications, such as optimizing the investment portfolios.

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  • Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
    • Handle: RePEc:arx:papers:2007.06848
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    References listed on IDEAS

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    1. Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    2. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Guosheng Hu & Yuxin Hu & Kai Yang & Zehao Yu & Flood Sung & Zhihong Zhang & Fei Xie & Jianguo Liu & Neil Robertson & Timothy Hospedales & Qiangwei Miemie, 2017. "Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions," Papers 1709.03803, arXiv.org, revised Feb 2018.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

    1. Om Mane & Saravanakumar kandasamy, 2022. "Stock Market Prediction using Natural Language Processing -- A Survey," Papers 2208.13564, arXiv.org.

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