Is Image Encoding Beneficial for Deep Learning in Finance?
| Autor: | Tianrui Wang, Ionuţ Florescu, Dan Wang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Finance
Artificial neural network Computer Networks and Communications business.industry Computer science Deep learning Financial ratio Convolutional neural network Hedge fund Computer Science Applications Hardware and Architecture Encoding (memory) Signal Processing Portfolio Electronic data Artificial intelligence business Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 9:5617-5628 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2020.3030492 |
| Popis: | 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 investigatesthe 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. |
| Databáze: | OpenAIRE |
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