A Stochastic Time Series Model for Predicting Financial Trends using NLP
This addresses stock price prediction for financial analysts, but appears incremental as it builds on existing GAN and sentiment analysis methods.
The authors tackled stock price forecasting by proposing ST-GAN, a model that combines financial news texts and numerical data using a time-series GAN, resulting in significant improvements over existing models.
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.