Emotional Reactions, Sentiment Disagreement, and Bitcoin Trading
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.
Abstract
Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.
참고문헌 (35)
- Aalborg, H. A., "What can explain the price, volatility and trading volume of Bitcoin?", Finance Research Letters, 29, pp.255~265 (2019)
- Ahn, Y., "Sentiment disagreement and bitcoin price fluctuations : a psycholinguistic approach", Applied Economics Letters, 27(5), pp.412~416 (2020)
- Ahn, Y., "Emotional trading in the cryptocurrency market", Finance Research Letters, 42, pp.101912~ (2021)
- Ahn, Y., "Visceral emotions and Bitcoin trading", Finance Research Letters, 51, pp.103458~ (2023)
- Andersen, T. G., "Return volatility and trading volume : An information flow interpretation of stochastic volatility", The Journal of Finance, 51(1), pp.169~204 (1996)
- Bollen, J., "Twitter mood predicts the stock market", Journal of Computational Science, 2(1), pp.1~8 (2011)
- Broad, C. D., "Emotion and sentiment", The Journal of Aesthetics and Art Criticism, 13(2), pp.203~214 (1954)
- Broadstock, D. C., "Social-media and intraday stock returns : The pricing power of sentiment", Finance Research Letters, 30, pp.116~123 (2019)
- Cen, L., "Investor sentiment, disagreement, and the breadth–return relationship", Management Science, 59(5), pp.1076~1091 (2013)
- Chen, H., "Wisdom of crowds : The value of stock opinions transmitted through social media", The Review of Financial Studies, 27(5), pp.1367~1403 (2014)
- An index of global economic policy uncertainty, "Davis, S. J.", National Bureau of Economic Research, 1~10, pp.Working Paper 22740 (2016)
- Ekman, P., "An argument for basic emotions", Cognition & Emotion, 6(3-4), pp.169~200 (1992)
- Gordon, S. L., "Social Psychology", pp.562~592 (2017), Routledge
- Grobys, K., "Cryptocurrencies and momentum", Economics Letters, 180, pp.6~10 (2019)
- Hirshleifer, D., "Limited investor attention and stock market misreactions to accounting information", The Review of Asset Pricing Studies, 1(1), pp.35~73 (2011)
- Hong, H., "Disagreement and the stock market", Journal of Economic Perspectives, 21(2), pp.109~128 (2007)
- Jiang, Y., "Time-varying long-term memory in Bitcoin market", Finance Research Letters, 25, pp.280~284 (2018)
- Kratzwald, B., "Deep learning for affective computing : Text-based emotion recognition in decision support", Decision Support Systems, 115, pp.24~35 (2018)
- Li, Y., "Does size matter in the cryptocurrency market?", Applied Economics Letters, 27(14), pp.1141~1149 (2020)
- Loewenstein, G., "Emotions in economic theory and economic behavior", American Economic Review, 90(2), pp.426~432 (2000)
- Long, H., "Seasonality in the cross-section of cryptocurrency returns", Finance Research Letters, 35, pp.101566~ (2020)
- Loughran, T., "When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks", The Journal of Finance, 66(1), pp.35~65 (2011)
- Luo, X., "Social media and firm equity value", Information Systems Research, 24(1), pp.146~163 (2013)
- Mai, F., "How does social media impact Bitcoin value? A test of the silent majority hypothesis", Journal of Management Information Systems, 35(1), pp.19~52 (2018)
- Mauss, I. B., "Measures of emotion : A review", Cognition & Emotion, 23(2), pp.209~237 (2009)
- Morris, S., "Trade with heterogeneous prior beliefs and asymmetric information", Econometrica, 132, pp.7~1347 (1994)
- Nandwani, P., "A review on sentiment analysis and emotion detection from text", Social Network Analysis and Mining, 11(1), pp.1~19 (2021)
- Shi, Y., "Better to Hear All Parties : Understanding the Impact of Homophily in Online Financial Discussion", Electronic Commerce Research and Applications, 54, pp.101159~ (2022)
- Stets, J. E., "Handbook of Social Psychology", pp.309~335 (2006), Springer
- Toufaily, E., "A framework of blockchain technology adoption : An investigation of challenges and expected value", Information & Management, 58(3), pp.103444~ (2021)
- Tumarkin, R., "News or noise? Internet postings and stock prices", Financial Analysts Journal, 57(3), pp.41~51 (2001)
- Xie, P., "Signal or noise in social media discussions : the role of network cohesion in predicting the Bitcoin market", Journal of Management Information Systems, 37(4), pp.933~956 (2020)
- Ye, Q., "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches", Expert Systems with Applications, 36(3), pp.6527~6535 (2009)
- Yu, Y., "The impact of social and conventional media on firm equity value : A sentiment analysis approach", Decision Support Systems, 55(4), pp.919~926 (2013)
- Zhang, W., "Is idiosyncratic volatility priced in cryptocurrency markets?", Research in International Business and Finance, 54, pp.101252~ (2020)