Improving prediction of stock market indices by analyzing the psychological states of twitter users
AbstractIn our paper, we analyze the possibility of improving the prediction of stock market indicators by conducting a sentiment analysis of Twitter posts. We use a dictionary-based approach for sentiment analysis, which allows us to distinguish eight basic emotions in the tweets of users. We compare the results of applying the Support Vector Machine algorithm trained on three sets of data: historical data, historical and “Worry”, “Fear”, “Hope” words count data, historical data and data on the present eight categories of emotions. Our results suggest that the Twitter sentiment analysis data provides additional information and improves prediction as compared to a model based solely on information on previous shifts in stock indicators.
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Bibliographic InfoPaper provided by National Research University Higher School of Economics in its series HSE Working papers with number WP BRP 22/FE/2013.
Length: 25 pages
Date of creation: 2013
Date of revision:
Publication status: Published in WP BRP Series: Financial Economics / FE, December 2013, pages 1-25
stock market; forecast; Twitter; mood; psychological states; Support Vectors Machine; machine learning/;
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
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