Title | ||
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SuperDeConFuse: A supervised deep convolutional transform based fusion framework for financial trading systems |
Abstract | ||
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This work proposes a supervised multi-channel time-series learning framework for financial stock trading. Although many deep learning models have recently been proposed in this domain, most of them treat the stock trading time-series data as 2-D image data, whereas its true nature is 1-D time-series data. Since the stock trading systems are multi-channel data, many existing techniques treating them as 1-D time-series data are not suggestive of any technique to effectively fusion the information carried by the multiple channels. To contribute towards both of these shortcomings, we propose an end-to-end supervised learning framework inspired by the previously established (unsupervised) convolution transform learning framework. Our approach consists of processing the data channels through separate 1-D convolution layers, then fusing the outputs with a series of fully-connected layers, and finally applying a softmax classification layer. The peculiarity of our framework, that we call SuperDeConFuse (SDCF), is that we remove the nonlinear activation located between the multi-channel convolution layers and the fully-connected layers, as well as the one located between the latter and the output layer. We compensate for this removal by introducing a suitable regularization on the aforementioned layer outputs and filters during the training phase. Specifically, we apply a logarithm determinant regularization on the layer filters to break symmetry and force diversity in the learnt transforms, whereas we enforce the non-negativity constraint on the layer outputs to mitigate the issue of dead neurons. This results in the effective learning of a richer set of features and filters with respect to a standard convolutional neural network. Numerical experiments confirm that the proposed model yields considerably better results than state-of-the-art deep learning techniques for the real-world problem of stock trading. |
Year | DOI | Venue |
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2021 | 10.1016/j.eswa.2020.114206 | Expert Systems with Applications |
Keywords | DocType | Volume |
Information fusion,Deep learning,Convolution,Transform learning,Stock trading | Journal | 169 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Pooja Gupta | 1 | 0 | 0.34 |
A. Majumdar | 2 | 644 | 75.83 |
Emilie Chouzenoux | 3 | 202 | 26.37 |
Giovanni Chierchia | 4 | 176 | 14.74 |