Abstract:
With the coming intelligent era and increasing demand for financial data analysis, deep learning has become the foreland in financial filed, especially in the prediction of financial market movements, text information processing and trading strategies improvement. Deep learning contains deep neural networks, deep belief networks and other structures. They extract deep features through the layered structure, strengthen important factors and filter the noise, which is of great significance to improve forecast accuracy. The applications and the variable optimization technologies of deep learning improve the forecast and analysis method in financial filed, make empirical research paradigm shift from linear to nonlinear and the concern from significance of parameters to the structure and dynamic characteristics of models, and also contribute to enriching financial and economic theories. It is important and difficult to build models with suitable structure and steady effect to capture the effective characteristics of financial data and interpret economic meaning with deep learning. The future research about deep learning in financial filed can be from these aspects, such as digging deep economic significance, refining the general analysis framework and exploring the applicability for heterogeneous information.
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