SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion
Framework for Financial Trading Systems
- URL: http://arxiv.org/abs/2011.04364v1
- Date: Mon, 9 Nov 2020 11:58:12 GMT
- Title: SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion
Framework for Financial Trading Systems
- Authors: Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
- Abstract summary: This work proposes a supervised multi-channel time-series learning framework for financial stock trading.
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.
Numerical experiments confirm that the proposed model yields considerably better results than state-of-the-art deep learning techniques for real-world problem of stock trading.
- Score: 29.411173536818477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 -
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 real-world problem of stock
trading.
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