Abstract
Enterprise finance has become an indispensable financial channel for people to invest in their lives, and business management can provide a better economic environment for the development of enterprise finance. The structure of enterprises is gradually becoming more and more complex, and business administration shoulders considerable responsibilities and obligations in the organization and supervision of today’s social management structure. How can China play its functions under the new situation after the world economic exchanges are more frequent is an important link to promote the stable development of financial markets. In view of the problems of economic activity behavior and certainty of financial index system under the background of existing business administration, this paper puts forward the deep learning model to make risk analysis, income analysis, profit and loss analysis, and so on. The formula of deep learning model is used to calculate the data graph of financial economy, and finally, various data are compared to get the research of several business management methods on the development of enterprise financial economy. Among them, the model of current management mode belongs to two modes: e-commerce and EPR management. They not only have very unique management characteristics but also greatly promote the development of modern management, and their roles also well interpret the characteristics of modern management. The experiment also analyzes the financial data under the four algorithms for uncertainty comparison, profit and loss comparison, discreteness comparison, volatility comparison, and possibility analysis. Finally, after the source of uncertainty, the risk prediction and risk management are carried out by constructing decision trees, and these structural models are used to bring comprehensive analysis to the financial economy of enterprises and to build the impact of good trends and development prospects.
Highlights
Using Google domestic trend to represent public sentiment factor and macroeconomic development factor and using longterm and short-term memory model (LSTM), this paper studies the influence of these factors on the volatility of S&P 500 from October 19, 2004, to July 24, 2015
Through the collation and research of uncertainty index and related deep learning models for neural network model methods, in this paper, we will use the conventional uncertainty measurement methods, select higher-dimensional and multidimensional economic and financial data for research, and simulate to build the uncertainty index of China’s financial and economic market. en, in order to construct a model to predict the future expectations of a large number of financial variables as well as possible, this paper will further explore the prediction function of deep learning (Seq2Seq) model in financial markets based on LSTM neural network
China’s macroeconomic variable data and financial variable data come from wind database, wide quantification platform, and Tushare API; there are 629 kinds of economic and financial variables used in the construction of an uncertain index of the financial market, including 346 kinds of domestic macroeconomic variables and 242 kinds of financial variables
Summary
With the gradual improvement and deepening of China’s economic system reform, the structure of enterprises has gradually become complicated. In order to understand the impact of different deep learning models and business management methods on the financial economy of enterprises, in this paper, various methods are studied, and data were analyzed [8]. In this way, it is a good suggestion and choice for the future development trend of enterprises. Ird, it is difficult to summarize the uncertainty in the complicated economic and financial operation by using a single or a small number of economic and financial indicators [21] For this reason, it is inevitable to explore uncertain factors from more comprehensive data in today’s society. Scotti uses the extent of the range selected by the public in the process of publishing relatively extensive data as the independent variable index of the uncertainty factor index instead of the data
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