Desenvolvimento de modelo para predi\c{c}\~ao de cota\c{c}\~oes de a\c{c}\~ao baseada em an\'alise de sentimentos de tweets

Mario Mitsuo Akita and Everton Josue da Silva

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Abstract: Training machine learning models for predicting stock market share prices is an active area of research since the automatization of trading such papers was available in real time. While most of the work in this field of research is done by training Neural networks based on past prices of stock shares, in this work, we use iFeel 2.0 platform to extract 19 sentiment features from posts obtained from microblog platform Twitter that mention the company Petrobras. Then, we used those features to train XBoot models to predict future stock prices for the referred company. Later, we simulated the trading of Petrobras' shares based on the model's outputs and determined the gain of R$88,82 (net) in a 250-day period when compared to a 100 random models' average performance.

Date: 2023-09
New Economics Papers: this item is included in nep-big and nep-ger
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Published in Anais do 1o Semin\'ario de Ci\^encia de Dados do IFSP. Campinas: 2023. p. 51 - 58

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2309.06538

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