A hybrid convolutional neural networks with sparse coding for intelligent future trading strategy design

• Main Authors: Chen, Jou-Fan, 陳柔帆
• Other Authors: Chen, An-Pin
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/542jp8

碩士 === 國立交通大學 === 資訊管理研究所 === 105 === This paper proposed a series of hybrid deep learning methods which combine convolutional neural networks (CNN) and convolutional sparse coding to enhance the performance of intelligent financial data analysis. The idea of this hybrid model is to utilize three meta-results of dictionary learning into deep learning framework. It aims to achieve better accuracy of financial data analysis for future trading strategy design. We proposed three novel hybrid models in this thesis as our major contributions. The first one is named as learned filter CNN model (LFCNN), which substitute a feature dictionary for the random filters in CNN framework. A set of initial filters is constructed by the unsupervised learning approach of sparse coding. The concept of the second model is to replace convolution layer with sparse matrix. This model aims to get better learning efficiency and accuracy based on sparse features. The third model uses the reconstructed data from sparse representation as input, and the purpose is to remove noise and make the decision making more robust. This research expects to enhance the predictability of investment behavior through the proposed hybrid deep learning models. The experimental results show that the proposed models can improve the accuracy and profit performance dramatically than the traditional rule-based strategies. The trading strategy derived from LFCNN model is relatively stable and prominent than the other two method proposed and examined in this thesis. In summary, the hybrid deep learning models are proven to have superior performance on improving the trading strategy and developing an intelligent trading strategy.