U-CNNpred: A Universal CNN-based Predictor for Stock Markets
This work addresses the challenge of improving prediction accuracy for financial markets by leveraging common patterns across markets, offering a more efficient approach than market-specific models.
The paper tackled the problem of stock market prediction by extracting general features from multiple financial markets, showing that U-CNNpred outperforms baseline algorithms in predicting directional movements and can be fine-tuned for new markets with better performance than state-of-the-art methods.
The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S\&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch.