Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Interplay between Cryptocurrency Transactions and Online Financial Forums (2401.10238v1)

Published 27 Nov 2023 in q-fin.GN, cs.CY, and cs.LG

Abstract: Cryptocurrencies are a type of digital money meant to provide security and anonymity while using cryptography techniques. Although cryptocurrencies represent a breakthrough and provide some important benefits, their usage poses some risks that are a result of the lack of supervising institutions and transparency. Because disinformation and volatility is discouraging for personal investors, cryptocurrencies emerged hand-in-hand with the proliferation of online users' communities and forums as places to share information that can alleviate users' mistrust. This research focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values. In particular, the most popular cryptocurrency Bitcoin (BTC) and a related active discussion community, Bitcointalk, are analyzed. This study shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC, therefore analysis of this interaction would be a perfect base to support personal investments in a non-regulated market and, to confirm whether cryptocurrency forums show evidences to detect abnormal behaviors in BTC values as well as to predict or estimate these values. The experiment highlights that forum data can explain specific events in the financial field. It also underlines the relevance of quotes (regular mechanism to response a post) at periods: (1) when there is a high concentration of posts around certain topics; (2) when peaks in the BTC price are observed; and, (3) when the BTC price gradually shifts downwards and users intend to sell.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. Lansky, J. Possible State Approaches to Cryptocurrencies. J. Syst. Integr. 2018, 8. doi:10.20470/jsi.v9i1.335.
  2. Back to Basics: What Are Cryptocurrencies? Financ. Dev. Mag. 2018, 55. doi:10.5089/9781484357415.022.
  3. Cryptocurrencies and Blockchain. Legal Context and Implications for Financial Crime, Money Laundering and Tax Evasion; Policy Department for Economic, Scientific and Quality of Life Policies: Brussels, Belgium, 2018. doi:10.2861/263175.
  4. Virtual Currencies Key Definitions and Potential AML/CFT Risks; Financial Action Task Force (FATF); OECD: Paris, France, 2018.
  5. Cryptocurrencies and Blockchain; The World Bank: Washington, DC, USA, 2018.
  6. Virtual Currency Schemes: A Further Analysis; European Central Bank: Frankfurt, Germany, 2015.
  7. Digital Currencies; Bank for International Settlements: Basel, Switzerland, 2015.
  8. Dibrova, A. Virtual Currency: New Step in Monetary Development. Procedia Soc. Behav. Sci. 2016, 229, 42–49. doi:10.1016/j.sbspro.2016.07.112.
  9. A methodology for the resolution of cashtag collisions on Twitter—A natural language processing & data fusion approach. Expert Syst. Appl. 2019, 127, 353–369. doi:10.1016/j.eswa.2019.03.019.
  10. Big Data Fusion Model for Heterogeneous Financial Market Data (FinDf). Adv. Intell. Syst. Comput. 2018, 868. doi:10.1007/978-3-030-01054-6˙75.
  11. Twitter permeability to financial events: an experiment towards a model for sensing irregularities. Multim. Tools Appl. 2019, 78, 9217–9245. doi:10.1007/s11042-018-6388-4.
  12. Core Concepts, Challenges, and Future Directions in Blockchain: A Centralized Tutorial. ACM Comput. Surv. 2020, 53. doi:10.1145/3366370.
  13. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telemat. Informatics 2019, 36, 55–81. doi:10.1016/j.tele.2018.11.006.
  14. Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns. Decis. Sci. 2017, 48, 454–488. doi:10.1111/deci.12229.
  15. Sentiment analysis on stock social media for stock price movement prediction. Eng. Appl. Artif. Intell. 2019, 85, 569–578. doi:10.1016/j.engappai.2019.07.002.
  16. Forecasting cryptocurrencies under model and parameter instability. Int. J. Forecast. 2019, 35, 485–501. doi:10.1016/j.ijforecast.2018.09.005.
  17. Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models. J. Risk Financ. Manag. 2019, 12. doi:10.1016/j.ijforecast.2018.09.005.
  18. Bianchi, D. Cryptocurrencies As an Asset Class? An Empirical Assessment. J. Altern. Investments 2020, 23, 162–179. doi:10.3905/jai.2020.1.105.
  19. Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Appl. Soft Comput. 2019, 75, 596–606. doi:10.1016/j.asoc.2018.11.038.
  20. Analysing Social Media Forums to Discover Potential Causes of Phasic Shifts in Cryptocurrency Price Series. Front. Blockchain 2020, 3, 1. doi:10.3389/fbloc.2020.00001.
  21. A Probe Survey of Bitcoin Transactions Through Analysis of Advertising in an On-Line Discussion Forum. Acta Inform. Pragensia 2019, 2019, 112–131. doi:10.18267/j.aip.127.
  22. Predicting altcoin returns using social media. PLoS ONE 2018, 13, e0208119.
  23. The Irruption of Cryptocurrencies Into Twitter Cashtags: A Classifying Solution. IEEE Access 2020, 8, 32698–32713. doi:10.1109/ACCESS.2020.2973735.
  24. Cryptocurrency pump-and-dump schemes. Available at SSRN 3267041, 11 February 2021. doi:10.2139/ssrn.3267041.
  25. The Economics of Cryptocurrency Pump and Dump Schemes. SSRN Electron. J. 2018. doi:10.2139/ssrn.3303365.
  26. Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies. PLOS ONE 2016, 11, e0161197.
  27. Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects. Chaos Interdiscip. J. Nonlinear Sci. 2018, 28, 071101. doi:10.1063/1.5036517.
  28. Signatures of the Crypto-Currency Market Decoupling from the Forex. Future Internet 2019, 11, 071101. doi:10.3390/fi11070154.
  29. Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19. Entropy 2020, 22, 43. doi:110.3390/e22091043.
  30. Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities; Association for Computing Machinery: New York, NY, USA, 2019; p. 379–389. doi:10.1145/3311350.3347178.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
Citations (8)

Summary

We haven't generated a summary for this paper yet.