Master of Arts (M.A.)
Degree Granting Department
Mathematics and Statistics
Kandethody M. Ramachandran, Ph.D.
Manish Agrawal, Ph.D.
Lu Lu, Ph.D.
Long short-term memory, word embedding, neural networks
The goal of this research is to build a model to predict trend of financial asset price using sentiment from news headlines and financial indicators of the asset. Objective of the model is to conclude good results but also to minimize the difference between predicted values and actual values. Unlike previous approaches where the sentiments are usually calculated into score, we focus on combination of word embedding of news and financial indicators due to nonavailability of sentiment lexicon.
One idea is that the sentiment of news headline should have impact on financial asset val- ues. In other words, it would be crucial how we extract information from news headlines. An- other idea is that price data through time series analysis is also useful to predict trend of financial asset prices. Hence, improvement should be made with combination of sentiment analysis of news headlines and time series analysis.
Compared to time series models and word embedding models, our combined model shows smaller or similar small MAPE, MAE, and RMSE with time series models, and reduces lag in graphs.
Scholar Commons Citation
Chou, Hsiao-Chuan, "Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting" (2021). USF Tampa Graduate Theses and Dissertations.