A Stochastic Time Series Model for Predicting Financial Trends using NLP

TL;DR

This paper introduces ST-GAN, a novel deep learning model combining GANs with sentiment analysis and technical indicators to improve stock trend predictions by analyzing financial news and numerical data.

Contribution

The paper presents a new stochastic time-series GAN model that integrates textual sentiment analysis with numerical data for enhanced financial trend forecasting.

Findings

Significant improvement over existing models in stock prediction accuracy

Effective integration of textual and numerical data using GANs

Demonstrated robustness across multiple financial datasets

Abstract

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.