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

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  • 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.

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  • 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
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    References listed on IDEAS

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    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).
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    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.

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