Intelligent FinTech Data Mining by Advanced Deep Learning Approaches

Abstract

With the progress of financial technology (FinTech), real-time information from FinTech is huge and complicated. For various fields of research, identifying intrinsic features of complex data is important, not limited to financial big data. Reviewing previous studies, there are no suitable methods to deal with complex financial data. General methods are traditionally developed from statistics and machine learning. They are usually in some shallow model forms, which cannot fully represent complex, compositional, and hierarchical financial data features. Due to above drawbacks, this study tries to address the problem by advanced deep learning (DL) methods. In DL more layers will increase the power for abstract data representation. Recently, DL has achieved state-of-the-art performance in a wide range of tasks including speech, image, and vision. DL is effective in learning increasingly more abstract representations in a layer-wise manner. That just meets the characteristic of financial data. This study applies newly developed DL networks, the deep canonically correlation analysis and deep canonically correlated autoencoders to perform FinTech data mining. To test the proposed model, this study employed financial statement data regarding many listed high technology companies in Taiwan stock markets. The computation of deep learning is leveraged by multiple graphics processing unit. Our systems and traditional methods are compared by the same data. Empirical results showed that our systems outperform traditional techniques from statistics and machine learning.