| dc.description.abstract | This study is intended to explores whether investor sentiment would affect bitcoin trading behavior, and what factors would affect bitcoin daily return and daily return volatility. Therefore, this study cites two sentiment factors used by Antweiler and Frank (2004) and Cookson and Niessner (2020): Bullishness Signal (BS) and Agreement Index (AI). The factors construction process uses the VADER package in Python as a text analysis tool, and uses the Long Short-Term Memory(LSTM) model to construct a total of four sets of sentiment factors. The sample data is a total of 53,415 Bitcointalk forum articles from October 2013 to December 2020. According to the literature, we included 28 variables of blockchain information, macro economic development, global currency exchange rates, and sentiment factors to construct a regression model.
The empirical results found that out of 28 variables including blockchain information, macro economic development, global currency exchange rates, and sentiment factors, a total of 5 variables are important variables for Bitcoin daily return prediction, including BitcoinHashRate. , Golden Daily Return (Gold), VIX, British Pound Exchange Rate (GBP), RMB Exchange Rate (CNY). A total of 9 variables are important variables for Bitcoin daily return volatility prediction, including the Shanghai Stock Exchange Composite Index return (SSE) and four sets of sentiment factors.
In terms of constructing sentiment factors, this study uses machine learning LSTM model. The accuracy of the training model reached 95.19%. The empirical results found that the sentiment fators constructed in this study through Bitcointalk forum articles can effectively predict the daily return volatility of Bitcoin. | en_US |