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Stock Market Prediction using Natural Language Processing -- A Survey

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  • Om Mane
  • Saravanakumar kandasamy

Abstract

The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage profit opportunities. This attractiveness of finding a solution has prompted researchers to find a way past problems like volatility, seasonality, and dependence on time. This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.

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  • Om Mane & Saravanakumar kandasamy, 2022. "Stock Market Prediction using Natural Language Processing -- A Survey," Papers 2208.13564, arXiv.org.
    • Handle: RePEc:arx:papers:2208.13564
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    References listed on IDEAS

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    1. Junran Wu & Ke Xu & Xueyuan Chen & Shangzhe Li & Jichang Zhao, 2021. "Price graphs: Utilizing the structural information of financial time series for stock prediction," Papers 2106.02522, arXiv.org, revised Nov 2021.
    2. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
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