Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2017 | OriginalPaper | Chapter

18. Dynamic Topic Modelling for Cryptocurrency Community Forums

Authors: M. Linton, E. G. S. Teo, E. Bommes, C. Y. Chen, Wolfgang Karl Härdle

Published in: Applied Quantitative Finance

Publisher: Springer Berlin Heidelberg

share
SHARE

Abstract

Cryptocurrencies are more and more used in official cash flows and exchange of goods. Bitcoin and the underlying blockchain technology have been looked at by big companies that are adopting and investing in this technology. The CRIX Index of cryptocurrencies http://​hu.​berlin/​CRIX indicates a wider acceptance of cryptos. One reason for its prosperity certainly being a security aspect, since the underlying network of cryptos is decentralized. It is also unregulated and highly volatile, making the risk assessment at any given moment difficult. In message boards one finds a huge source of information in the form of unstructured text written by e.g. Bitcoin developers and investors. We collect from a popular crypto currency message board texts, user information and associated time stamps. We then provide an indicator for fraudulent schemes. This indicator is constructed using dynamic topic modelling, text mining and unsupervised machine learning. We study how opinions and the evolution of topics are connected with big events in the cryptocurrency universe. Furthermore, the predictive power of these techniques are investigated, comparing the results to known events in the cryptocurrency space. We also test hypothesis of self-fulling prophecies and herding behaviour using the results.
Literature
go back to reference Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 1371–1391. CrossRef Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 1371–1391. CrossRef
go back to reference Blei, D., Ng, A. Y., Jordan, M. I., & Lafferty, J. (2003). Latent Dirichlet allocation; Journal of Machine Learning Research, 3, 993–1022. Blei, D., Ng, A. Y., Jordan, M. I., & Lafferty, J. (2003). Latent Dirichlet allocation; Journal of Machine Learning Research, 3, 993–1022.
go back to reference Blei, D., & Lafferty, J. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (AMC). Blei, D., & Lafferty, J. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (AMC).
go back to reference Bommes, E., Chen, C. Y., Härdle, W. K. (2017). Textual sentiment and sector-specific reaction. Forthcoming. Bommes, E., Chen, C. Y., Härdle, W. K. (2017). Textual sentiment and sector-specific reaction. Forthcoming.
go back to reference Chang, J., Boyd-Graber, J. L., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems, 288–296. Chang, J., Boyd-Graber, J. L., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems, 288–296.
go back to reference Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32–36. MathSciNetCrossRefMATH Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32–36. MathSciNetCrossRefMATH
go back to reference Cheung, A., Roca, E., & Su, J. J. (2015). Crypto-currency bubbles: An application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices. Applied Economics, 47(23), 2348–2358. CrossRef Cheung, A., Roca, E., & Su, J. J. (2015). Crypto-currency bubbles: An application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices. Applied Economics, 47(23), 2348–2358. CrossRef
go back to reference Frigyik, B. A., Kapila, A., & Gupta, M. R. (2010). Introduction to the Dirichlet distribution and related processes. Technical Report, Department of Electrical Engineering, University of Washington. Frigyik, B. A., Kapila, A., & Gupta, M. R. (2010). Introduction to the Dirichlet distribution and related processes. Technical Report, Department of Electrical Engineering, University of Washington.
go back to reference Griffiths, T., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl1), 5228–5235. CrossRef Griffiths, T., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl1), 5228–5235. CrossRef
go back to reference Hall, D., Jurafsky, D., & Manning, C. (2008). Studying the history of ideas using topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 363–371. Hall, D., Jurafsky, D., & Manning, C. (2008). Studying the history of ideas using topic models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 363–371.
go back to reference Huang, K. W., & Li, Z. L. (2011). A multilable text classification algorithm for labeling risk factors in SEC form 10-K. ACM Transactions on Management Information Systems (TMIS), 2(3), 18. Huang, K. W., & Li, Z. L. (2011). A multilable text classification algorithm for labeling risk factors in SEC form 10-K. ACM Transactions on Management Information Systems (TMIS), 2(3), 18.
go back to reference Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415. CrossRef Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415. CrossRef
go back to reference Mai, F., Bai, Q., Shan, Z., Wang, X. S., & Chiang, R. H. (2015). The impacts of social media on Bitcoin performance. In Proceedings of the Thirty Sixth International Conference on Information Systems (ICIS 2015). Mai, F., Bai, Q., Shan, Z., Wang, X. S., & Chiang, R. H. (2015). The impacts of social media on Bitcoin performance. In Proceedings of the Thirty Sixth International Conference on Information Systems (ICIS 2015).
go back to reference Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin spread prediction using social and web search media. Proceedings of DeCAT. Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin spread prediction using social and web search media. Proceedings of DeCAT.
go back to reference Mimno, D., Wallach, H. M., Talley E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 262–272. Mimno, D., Wallach, H. M., Talley E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 262–272.
go back to reference Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013). Predictive sentiment analysis of tweets: A stock market application. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 77–88). Berlin: Springer. Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013). Predictive sentiment analysis of tweets: A stock market application. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 77–88). Berlin: Springer.
go back to reference Wallach, H. M., Jensen, S. T., Dicker, L. H., & Heller, K. A. (2010). An alternative prior process for nonparametric Bayesian clustering. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 9, 892–899. Wallach, H. M., Jensen, S. T., Dicker, L. H., & Heller, K. A. (2010). An alternative prior process for nonparametric Bayesian clustering. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 9, 892–899.
go back to reference Zhang, J. L., Härdle, W. K., Chen, C. Y., & Bommes, E. (2016). Distillation of news flow into analysis of stock reactions. Journal of Business and Economic Statistics, 34, 547–563. MathSciNetCrossRef Zhang, J. L., Härdle, W. K., Chen, C. Y., & Bommes, E. (2016). Distillation of news flow into analysis of stock reactions. Journal of Business and Economic Statistics, 34, 547–563. MathSciNetCrossRef
Metadata
Title
Dynamic Topic Modelling for Cryptocurrency Community Forums
Authors
M. Linton
E. G. S. Teo
E. Bommes
C. Y. Chen
Wolfgang Karl Härdle
Copyright Year
2017
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-54486-0_18