Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network
arXiv:2010.01197
Abstract
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks, while the temporal convolutional layers are used for automatically capturing effective temporal patterns both within and across series. Evaluated on S&P 500, our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Twitter mood predicts the stock market
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Convolutional Sequence to Sequence Learning
- Entity Embeddings of Categorical Variables
- A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem