  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.
