  Stock price forecasting is a highly complex and vitally important field of
research. Recent advancements in deep neural network technology allow
researchers to develop highly accurate models to predict financial trends. We
propose a novel deep learning model called ST-GAN, or Stochastic Time-series
Generative Adversarial Network, that analyzes both financial news texts and
financial numerical data to predict stock trends. We utilize cutting-edge
technology like the Generative Adversarial Network (GAN) to learn the
correlations among textual and numerical data over time. We develop a new
method of training a time-series GAN directly using the learned representations
of Naive Bayes' sentiment analysis on financial text data alongside technical
indicators from numerical data. Our experimental results show significant
improvement over various existing models and prior research on deep neural
networks for stock price forecasting.
