Using Twitter Attribute Information to Predict Stock Prices

TL;DR

This paper develops a neural network model incorporating Twitter social media attributes and technical indicators to improve stock price prediction accuracy, demonstrating modest but meaningful performance gains.

Contribution

It introduces a novel approach combining Twitter attribute data with technical analysis in a neural network for stock forecasting, showing improved accuracy.

Findings

Adding Twitter attributes reduces MSE by 3%.

Including technical indicators decreases MSE from 0.1617 to 0.1437.

Twitter data can enhance stock price prediction models.

Abstract

Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of LSTM and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved, extracted and calculated from posts on the social media platform Twitter. These attributes are sentiment score, favourites, followers, retweets and if an account is verified. To collect data from Twitter, Twitter's API is used. Sentiment analysis is conducted with VADER. The results show that by adding more Twitter attributes, the MSE between the predicted prices and the actual prices improved by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to 0.1437, which is an improvement of around 11%. The restrictions of this study include that the selected stock has to be publicly listed on the stock market and popular on Twitter and among individual investors. Besides, the stock markets' opening hours differ from Twitter, which constantly available. It may therefore introduce noises in the model.