Short-Term Stock Price-Trend Prediction Using Meta-Learning
This addresses stock price prediction for investors under data-limited conditions, but it is incremental as it adapts existing meta-learning and neural network methods to a specific financial domain.
The study tackled short-term stock price-trend prediction by proposing a meta-learning framework with convolutional neural networks and slope-detection labeling, applied to the S&P500, resulting in significant improvements in prediction accuracy and profitability.
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.