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HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model

Author

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  • Ishu Gupta
  • Tarun Kumar Madan
  • Sukhman Singh
  • Ashutosh Kumar Singh

Abstract

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately.

Suggested Citation

  • Ishu Gupta & Tarun Kumar Madan & Sukhman Singh & Ashutosh Kumar Singh, 2022. "HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model," Papers 2203.08143, arXiv.org.
    • Handle: RePEc:arx:papers:2203.08143
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    References listed on IDEAS

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    1. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
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    Cited by:

    1. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.

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