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delgadillo, josiel kinyua, johnson mutigwe, charles
Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs...
Choi, Ka Wai (Stanley) Ma, Wentao Ho, Shuk Ying Wu, Dickson
This paper investigates the association between retail investors’ online activity and the pricing of initial public offerings (IPOs). We utilize data from Google Trends and StockTwits to analyze price revision for 901 U.S. IPOs, and find that the online search count, social media post count, and post sentiment are positively associated with IPO pri...
Santi, Caterina
peer reviewed / We propose to measure investor climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. In financial markets, stocks of emission (carbon-intensive) firms underperform clean (low-emission) stocks when investor climate sentiment is more positive. We document investors overreaction to...
Ballinari, Daniele Behrendt, Simon
Published in
Digital finance
Given the increasing interest in and the growing number of publicly available methods to estimate investor sentiment from social media platforms, researchers and practitioners alike are facing one crucial question - which is best to gauge investor sentiment? We compare the performance of daily investor sentiment measures estimated from Twitter and ...
Renault, Thomas
We use a large dataset of one million messages sent on the microblogging platform StockTwits to evaluate the performance of a wide range of preprocessing methods and machine learning algorithms for sentiment analysis in finance. We find that adding bigrams and emojis significantly improve sentiment classification performance. However, more complex ...
