  Although conventional machine learning algorithms have been widely adopted
for stock-price predictions in recent years, the massive volume of specific
labeled data required are not always available. In contrast, meta-learning
technology uses relatively small amounts of training data, called fast
learners. Such methods are beneficial under conditions of limited data
availability, which often obtain for trend prediction based on time-series data
limited by sparse information. In this study, we consider short-term stock
price prediction using a meta-learning framework with several convolutional
neural networks, including the temporal convolution network, fully
convolutional network, and residual neural network. We propose a sliding time
horizon to label stocks according to their predicted price trends, referred to
as called slope-detection labeling, using prediction labels including "rise
plus," "rise," "fall," and "fall plus". The effectiveness of the proposed
meta-learning framework was evaluated by application to the S&P500. The
experimental results show that the inclusion of the proposed meta-learning
framework significantly improved both regular and balanced prediction accuracy
and profitability.
