| Paper ID | MLSP-41.6 |
| Paper Title |
AUGMENTING TRANSFERRED REPRESENTATIONS FOR STOCK CLASSIFICATION |
| Authors |
Elizabeth Fons, University of Manchester, United Kingdom; Paula Dawson, AllianceBernstein, United Kingdom; Xiao-jun Zeng, John Keane, University of Manchester, United Kingdom; Alexandros Iosifidis, Aarhus University, Denmark |
| Session | MLSP-41: Deep Learning Optimization |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-TRL] Transfer learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S&P500 index and then transferring it to another model to directly learn a trading rule. Transferred models present more than double the risk-adjusted returns than their counterparts trained from zero. In addition, we propose the use of data augmentation on the feature space defined as the output of a pre-trained model (i.e. augmenting the aggregated time-series representation). We compare this augmentation approach with the standard one, i.e. augmenting the time-series in the input space. We show that augmentation methods on the feature space leads to 20% increase in risk-adjusted return compared to a model trained with transfer learning but without augmentation. |
