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Predictive Analysis of Tweets Data: A Stock Market Application

Student: afanasev egor

Supervisor:

Faculty: Faculty of Economic Sciences

Educational Programme: Joint HSE-NES Undergraduate Program in Economics (Bachelor)

Year of Graduation: 2022

More and more researchers use Twitter as a source of information about public sentiment. In this work, I investigate the noisiness of Twitter data. I show that in the tasks of predicting the market performance of a company, it is optimal to use not the entire available volume of tweets, but only thematic subsamples. By reducing the amount of data used by 75%-99%, I achieve better quality in tasks of price direction prediction, performance relative to the market prediction, and trading volume prediction.

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