  Data augmentation methods in combination with deep neural networks have been
used extensively in computer vision on classification tasks, achieving great
success; however, their use in time series classification is still at an early
stage. This is even more so in the field of financial prediction, where data
tends to be small, noisy and non-stationary. In this paper we evaluate several
augmentation methods applied to stocks datasets using two state-of-the-art deep
learning models. The results show that several augmentation methods
significantly improve financial performance when used in combination with a
trading strategy. For a relatively small dataset ($\approx30K$ samples),
augmentation methods achieve up to $400\%$ improvement in risk adjusted return
performance; for a larger stock dataset ($\approx300K$ samples), results show
up to $40\%$ improvement.
