IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2010.08698.html
   My bibliography  Save this paper

Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance

Author

Listed:
  • Dan Wang
  • Tianrui Wang
  • Ionuc{t} Florescu

Abstract

In 2012, SEC mandated all corporate filings for any company doing business in US be entered into the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. In this work we are investigating ways to analyze the data available through EDGAR database. This may serve portfolio managers (pension funds, mutual funds, insurance, hedge funds) to get automated insights into companies they invest in, to better manage their portfolios. The analysis is based on Artificial Neural Networks applied to the data.} In particular, one of the most popular machine learning methods, the Convolutional Neural Network (CNN) architecture, originally developed to interpret and classify images, is now being used to interpret financial data. This work investigates the best way to input data collected from the SEC filings into a CNN architecture. We incorporate accounting principles and mathematical methods into the design of three image encoding methods. Specifically, two methods are derived from accounting principles (Sequential Arrangement, Category Chunk Arrangement) and one is using a purely mathematical technique (Hilbert Vector Arrangement). In this work we analyze fundamental financial data as well as financial ratio data and study companies from the financial, healthcare and IT sectors in the United States. We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1D input directly for fundamental data. We do not find the Hilbert Vector Arrangement technique to be significantly better than other imaging techniques.

Suggested Citation

  • Dan Wang & Tianrui Wang & Ionuc{t} Florescu, 2020. "Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance," Papers 2010.08698, arXiv.org.
    • Handle: RePEc:arx:papers:2010.08698
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2010.08698
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muthu Subash Kavitha & Takio Kurita & Soon-Yong Park & Sung-Il Chien & Jae-Sung Bae & Byeong-Cheol Ahn, 2017. "Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    2. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
    3. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    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. Shenghuan Yang & lonut Florescu & Md Tariqul Islam, 2020. "Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating," Papers 2011.09137, arXiv.org, revised Dec 2020.

    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. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    2. Shenghuan Yang & lonut Florescu & Md Tariqul Islam, 2020. "Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating," Papers 2011.09137, arXiv.org, revised Dec 2020.
    3. Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023. "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
    5. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    6. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Sep 2023.
    7. Koresh Galil & Ami Hauptman & Rosit Levy Rosenboim, 2023. "Prediction of Corporate Credit Ratings with Machine Learning: Simple Interpretative Models," Working Papers 2308, Ben-Gurion University of the Negev, Department of Economics.
    8. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    9. María Jesús Segovia‐Vargas & I. Marta Miranda‐García & Freddy Alejandro Oquendo‐Torres, 2023. "Sustainable finance: The role of savings and credit cooperatives in Ecuador," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 951-980, September.
    10. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    11. Kai Ren, 2023. "Study on Intelligent Forecasting of Credit Bond Default Risk," Papers 2305.12142, arXiv.org, revised Jun 2023.

    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:2010.08698. 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.