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- W3216326868 abstract "Return on equity reflects the ability of self-owned capital to obtain net income. As we all know, return on equity is an important indicator to measure the profitability of listed companies, which reflects the profitability of enterprise owners. However, some people slightly expand the role of return on equity when they choose investment products. When they choose, they should consider comprehensively, recognize the defects of return on equity, and refer to other indicators to choose investment products. Whether for companies or individual investors, how to predict the return on equity and analyze the sensitive indicators that affect the return on equity is an important issue. This paper proposes a method of forecasting return on equity and index sensitivity analysis based on deep neural network. It analyzes a large number of data samples based on deep neural network algorithm, and investigates the relationship between typical input and output factors in a wide variable space; Finally, combined with directional derivative and genetic algorithm, the sensitivity of neural network is mined to solve the sensitive points, which provides a reference for the financial analysis of the company. This paper selects the financial data of 41 companies in recent five years for analysis, a total of 204. Experiments show the accuracy of the method and the effectiveness of sensitivity analysis." @default.
- W3216326868 created "2021-12-06" @default.
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- W3216326868 date "2021-08-27" @default.
- W3216326868 modified "2023-09-24" @default.
- W3216326868 title "Prediction and Sensitivity Analysis of Companies’ Return on Equity Based on Deep Neural Network" @default.
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- W3216326868 doi "https://doi.org/10.1145/3485190.3485211" @default.
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