IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v6y2022i2id19428.html

Mitigating Risk in Financial Industry by Analyzing Social-Media with Machine Learning Technology

Author

Listed:
  • Issayas M. Haile
  • Yanzhen Qu

Abstract

A large amount of data is available on Twitter that can be used to manage different types of risks in financial institutions. This paper shows how machine learning algorithms can be applied to analyze large unstructured data and train a model to make a future prediction on tweets to categorize them by risk type and use sentiment analysis to understand the risk type. This model reads each tweet and categorizes them by risk using a specified dictionary and adds sentiment analysis to show the risk type seen in each tweet. Logistic regression used in this research helped to formulate the prediction model. Twitter data from 2019 was used to train and test a supervised machine learning algorithm and once the model started predicting tweets efficiently, it was used to predict twitter data from 2022 in our experimental research. Our experiment confirmed that Twitter data can be used to manage risk with the right type of modeling using machine learning techniques.

Suggested Citation

  • Issayas M. Haile & Yanzhen Qu, 2022. "Mitigating Risk in Financial Industry by Analyzing Social-Media with Machine Learning Technology," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 6(2), pages 33-37, March.
    • Handle: RePEc:epw:ejece0:v:6:y:2022:i:2:id:19428
    DOI: 10.24018/ejece.2022.6.2.428
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19428
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/19428/11232
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2022.6.2.428?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Syed Shahan Ali & Muhammad Mubeen & Irfan Lal & Adnan Hussain, 2018. "Prediction of stock performance by using logistic regression model: evidence from Pakistan stock exchange (PSX)," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 8(7), pages 247-258, July.
    2. Krishna, Dilip, 2016. "Big Data in risk management," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 9(1), pages 46-52, January.
    3. Ali, Syed Shahan & Mubeen, Muhammad & Lal, Irfan & Hussain, Adnan, 2018. "Prediction of Stock Performance by Using Logistic Regression Model: Evidence from Pakistan Stock Exchange (PSX)," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 8(7), pages 247-258.
    Full references (including those not matched with items on IDEAS)

    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. Federico Mecchia & Marcellino Gaudenzi, 2022. "The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network," Papers 2206.09899, arXiv.org.
    2. Gaurang Sonkavde & Deepak Sudhakar Dharrao & Anupkumar M. Bongale & Sarika T. Deokate & Deepak Doreswamy & Subraya Krishna Bhat, 2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications," IJFS, MDPI, vol. 11(3), pages 1-22, July.
    3. Zübeyir Akturk & Erdal Kılıç & Ömer Algorabi & Mesut Ulu & Yusuf Sait Türkan & Ersin Namlı, 2026. "Real-Time Forecast of BIST100 Index Under Market Volatility and Uncertainty," Computational Economics, Springer;Society for Computational Economics, vol. 67(4), pages 3249-3274, April.
    4. Yushen Kong & Micheal Owusu-Akomeah & Henry Asante Antwi & Xuhua Hu & Patrick Acheampong, 2019. "Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:epw:ejece0:v:6:y:2022:i:2:id:19428. 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: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    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.