Abstract
Predicting the trend of stock prices is a central topic in financial engineering. Given the complexity and nonlinearity of the underlying processes we consider the use of neural networks in general and sentiment analysis in particular for the analysis of financial time series. As one of the biggest social media platforms with a user base across the world, Twitter offers a huge potential for such sentiment analysis. In fact, stocks themselves are a popular topic in Twitter discussions. Due to the real-time nature of the collective information quasi contemporaneous information can be harvested for the prediction of financial trends. In this study, we give an introduction in financial feature engineering as well as in the architecture of a Long Short-Term Memory (LSTM) to tackle the highly nonlinear problem of forecasting stock prices. This paper presents a guide for collecting past tweets, processing for sentiment analysis and combining them with technical financial indicatorsto forecast the stock prices of Apple 30m and 60m ahead. A LSTM with lagged close price is used as a baseline model. We are able to show that a combination of financial and Twitter features can outperform the baseline in all settings. The code to fully replicate our forecasting approach is available in the Appendix.
Highlights
“The most valuable commodity [...] is information.” This famous quote by Gordon Gekko in The Wall Street in 1987 implies that in the framework of the stock market profits can be generated based on superior knowledge
We focus on publications that use Twitter data for stock price predictions
This paper provides a practical guide for stock price predictions with Twitter data by using a combination of a LSTM and sentiment classification
Summary
“The most valuable commodity [...] is information.” This famous quote by Gordon Gekko in The Wall Street in 1987 implies that in the framework of the stock market profits can be generated based on superior knowledge. “The most valuable commodity [...] is information.” This famous quote by Gordon Gekko in The Wall Street in 1987 implies that in the framework of the stock market profits can be generated based on superior knowledge. This stands in contradiction to the Efficient Market Hypothesis (EMH) which “states that market price mirrors the assimilation of all the information available. There are many different possibilities to extract Twitter data and to create variables based on tweets. This research contributes to the literature with a practical guide to forecast shortterm stock prices with a LSTM including TextBlob-Twitter variables. The code to fully replicate our forecasting approach is available in the Appendix
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