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Comprehensive review of text-mining applications in finance

Author

Listed:
  • Aaryan Gupta

    (Nirma University)

  • Vinya Dengre

    (Nirma University)

  • Hamza Abubakar Kheruwala

    (Nirma University)

  • Manan Shah

    (Pandit Deendayal Petroleum University)

Abstract

Text-mining technologies have substantially affected financial industries. As the data in every sector of finance have grown immensely, text mining has emerged as an important field of research in the domain of finance. Therefore, reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research. This paper focuses on the text-mining literature related to financial forecasting, banking, and corporate finance. It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies. Finally, the paper briefly discusses various text-mining methods being applied in the financial domain, the challenges faced in these applications, and the future scope of text mining in finance.

Suggested Citation

  • Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    • Handle: RePEc:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00205-1
    DOI: 10.1186/s40854-020-00205-1
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    References listed on IDEAS

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    Cited by:

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    2. Dongyoung Kim & Sungwon Jung & Yongwook Jeong, 2021. "Theft Prediction Model Based on Spatial Clustering to Reflect Spatial Characteristics of Adjacent Lands," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    3. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Wenlu Zhao & Guanghu Jin & Chenyue Huang & Jinji Zhang, 2023. "Attention and Sentiment of the Chinese Public toward a 3D Greening System Based on Sina Weibo," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    5. Christopher Gerling & Stefan Lessmann, 2023. "Multimodal Document Analytics for Banking Process Automation," Papers 2307.11845, arXiv.org, revised Nov 2023.
    6. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org.
    7. Kentaka Aruga & Md. Monirul Islam & Yoshihiro Zenno & Arifa Jannat, 2022. "Developing Novel Technique for Investigating Guidelines and Frameworks: A Text Mining Comparison between International and Japanese Green Bonds," JRFM, MDPI, vol. 15(9), pages 1-17, August.
    8. Mazzotta, Stefano, 2022. "Immigration narrative sentiment from TV news and the stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    9. Poojan Thakkar & Manan Shah, 2021. "An Assessment of Football Through the Lens of Data Science," Annals of Data Science, Springer, vol. 8(4), pages 823-836, December.
    10. Yuanying Chi & Mingjian Yan & Yuexia Pang & Hongbo Lei, 2022. "Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    11. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.

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