Augmenting Transferred Representations for Stock Classification

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 timeseries 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.

Predicting the State of Synchronization of Financial Time Series Using Cross Recurrence Plots

“…Indeed, it has extensively been shown that, in financial applications, deep learning (DL) models are often capable of outperforming traditional approaches due to their ability to learn complex data representations based on end-to-end data-driven training, see, e.g., [19][20][21][22][23][24]. DL models have been adopted for a variety of problems ranging from price prediction [25][26][27][28][29], limitorder book-based mid-price prediction [20,21,23,30,31], and volatility prediction [32][33][34].…”