IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.05783.html
   My bibliography  Save this paper

Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets

Author

Listed:
  • Narayana Darapaneni
  • Anwesh Reddy Paduri
  • Himank Sharma
  • Milind Manjrekar
  • Nutan Hindlekar
  • Pranali Bhagat
  • Usha Aiyer
  • Yogesh Agarwal

Abstract

Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.

Suggested Citation

  • Narayana Darapaneni & Anwesh Reddy Paduri & Himank Sharma & Milind Manjrekar & Nutan Hindlekar & Pranali Bhagat & Usha Aiyer & Yogesh Agarwal, 2022. "Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets," Papers 2204.05783, arXiv.org.
    • Handle: RePEc:arx:papers:2204.05783
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.05783
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liam A. Gallagher & Mark P. Taylor, 2002. "Permanent and Temporary Components of Stock Prices: Evidence from Assessing Macroeconomic Shocks," Southern Economic Journal, John Wiley & Sons, vol. 69(2), pages 345-362, October.
    2. Manolis Kavussanos & Everton Dockery, 2001. "A multivariate test for stock market efficiency: the case of ASE," Applied Financial Economics, Taylor & Francis Journals, vol. 11(5), pages 573-579.
    3. Butler, Kirt C. & Malaikah, S. J., 1992. "Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 197-210, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saber Talazadeh & Dragan Perakovic, 2024. "SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest," Papers 2410.07143, arXiv.org.
    2. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Qian & Khaled Rasheed, 2010. "Foreign exchange market prediction with multiple classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 271-284.
    2. Mamdouh Abdulaziz Saleh Al-Faryan & Everton Dockery, 2021. "Testing for efficiency in the Saudi stock market: does corporate governance change matter?," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 61-90, July.
    3. Mufhumudzi Muthivhi & Terence L. van Zyl, 2022. "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers 2203.05673, arXiv.org.
    4. Altuğ Tanaltay & Amirreza Safari Langroudi & Raha Akhavan-Tabatabaei & Nihat Kasap, 2021. "Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter," SAGE Open, , vol. 11(2), pages 21582440211, April.
    5. Dimitrios D. Thomakos & Michail S. Koubouros, 2011. "The Role of Realised Volatility in the Athens Stock Exchange," Multinational Finance Journal, Multinational Finance Journal, vol. 15(1-2), pages 87-124, March - J.
    6. Ratner, Mitchell, 1996. "Investigating the behavior and characteristics of the Madrid Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 20(1), pages 135-149, January.
    7. Qingshuo Song & Qing Zhang, 2013. "An Optimal Pairs-Trading Rule," Papers 1302.6120, arXiv.org.
    8. Md Zobaer Hasan & Anton Abdulbasah Kamil & Adli Mustafa & Md Azizul Baten, 2012. "Stochastic Frontier Model Approach for Measuring Stock Market Efficiency with Different Distributions," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    9. Abdmoulah, Walid, 2010. "Testing the evolving efficiency of Arab stock markets," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 25-34, January.
    10. Addi, Abdelhamid & Bouoiyour, Jamal, 2023. "Interconnectedness and extreme risk: Evidence from dual banking systems," Economic Modelling, Elsevier, vol. 120(C).
    11. Al-Suhaibani, Mohammad & Kryzanowski, Lawrence, 2000. "An exploratory analysis of the order book, and order flow and execution on the Saudi stock market," Journal of Banking & Finance, Elsevier, vol. 24(8), pages 1323-1357, August.
    12. Kanellos Toudas & Athanasios Bellas, 2014. "Corporate Governance and its Effect on Firm Value and Stock Returns of Listed Companies on the Athens Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 58-80.
    13. Hammoudeh, Shawkat & Choi, Kyongwook, 2007. "Characteristics of permanent and transitory returns in oil-sensitive emerging stock markets: The case of GCC countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(3), pages 231-245, July.
    14. Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    15. Jorge Belaire-Franch & Stanley McGreal & Kwaku K. Opong & James R. Webb, 2007. "A Nonparametric Variance-Ratio Test of the Behavior of U.K. Real Estate and Construction Indices," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 94-112.
    16. Furdui Călin & Șfabu Dorina Teodora, 2023. "The European Banks Under the Shock of the Russian Invasion of 2022: An Event Study Approach," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 68(1), pages 62-77, April.
    17. Appiah-Kusi, Joe & Menyah, Kojo, 2003. "Return predictability in African stock markets," Review of Financial Economics, Elsevier, vol. 12(3), pages 247-270.
    18. Rompotis, Gerasimos G., 2011. "Testing weak-form efficiency of exchange traded funds market," MPRA Paper 36020, University Library of Munich, Germany.
    19. Jay Squalli, 2006. "A non-parametric assessment of weak-form efficiency in the UAE financial markets," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1365-1373.
    20. Alam, Md. Mahmudul & Akbar, Chowdhury Shahed, 2019. "Rationality of the Capital Market: Capitalistic System vs. Islamic System," SocArXiv 83ekv, Center for Open Science.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2204.05783. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.