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Internet publishing as a forecasting tool in the crypto market

Vol. 30, Iss. 1, JANUARY 2024

Received: 19 October 2023

Received in revised form: 2 November 2023

Accepted: 16 November 2023

Available online: 30 January 2024

Subject Heading: Securities market

JEL Classification: G14, G17, G39

Pages: 72–102

https://doi.org/10.24891/fc.30.1.72

Elena A. FEDOROVA Financial University under Government of Russian Federation, Moscow, Russian Federation
ecolena@mail.ru

https://orcid.org/0000-0002-3381-6116

Natal'ya A. ANDREEVA Financial University under Government of Russian Federation, Moscow, Russian Federation
218015@edu.fa.ru

https://orcid.org/0009-0007-3416-6112

Irena I. TARBA Financial University under Government of Russian Federation, Moscow, Russian Federation
218939@edu.fa.ru

https://orcid.org/0009-0006-1977-0058

Daniil D. ANDREEV National Research University – Higher School of Economics (NRU – HSE) Moscow, Russian Federation
andr.daniil@gmail.com

https://orcid.org/0000-0001-8365-7101

Subject. This article examines the relationship between the sentiment caused by the news on the CoinTelegragh professional forum and the changes in Bitcoin, Litecoin and Ethereum cryptocurrencies.
Objectives. The article aims to assess the impact of the sentiment of various Internet publications on the volatility of cryptocurrencies, as well as the predictive power of Google Trends and the VIX Index for cryptocurrencies.
Methods. For the study, we used the cross-quantilogram method and the VADER sentiment analysis model.
Results. The article finds that the Google Trends Index in a short period of one to three days can be used to predict the closing prices of Bitcoin, Litecoin, and Ethereum, while the VIX Index (Stock Market Uncertainty) has no relationship with the cryptocurrency market. This means that cryptocurrencies can be used as a safe-haven asset when the background market is highly volatile.
Conclusions. The crypto market has a complex sentiment component, with its prices and trading activity determined by popularity, emotion, and sentiment. The findings confirm previous studies, which claim that during the period of prevalence of negative news and publications, the crypto market gets narrowed, the trading volume drops off, and the interest of Internet users gets low to a minimum. The euphoria in the market, on the contrary, attracts new unqualified investors, and this is confirmed by the number of views of basic information about cryptocurrencies on Wikipedia.

Keywords: volatility, cryptocurrency volatility index, market sentiment, Bitcoin

References:

  1. Zhao J., Zhang T. Exploring the Time-Varying Dependence Between Bitcoin and the Global Stock Market: Evidence from a TVP-VAR Approach. Finance Research Letters, 2023, vol. 58, part A. URL: Link
  2. Nekhili R., Sultan J., Bouri E. Liquidity Spillovers Between Cryptocurrency and Foreign Exchange Markets. The North American Journal of Economics and Finance, 2023, vol. 68. URL: Link
  3. Dastgir S., Demir E., Downing G. et al. The Causal Relationship Between Bitcoin Attention and Bitcoin Returns: Evidence from the Copula-based Granger Causality Test. Finance Research Letters, 2019, vol. 28, pp. 160–164. URL: Link
  4. Shahzad S.J.H., Bouri E., Roubaud D. et al. Is Bitcoin a Better Safe-Haven Investment Than Gold and Commodities? International Review of Financial Analysis, 2019, vol. 63, pp. 322–330. URL: Link
  5. Liu Yu., Tsyvinski A. Risks and Returns of Cryptocurrency. Review of Financial Studies, 2021, vol. 34, iss. 6, pp. 2689–2727. URL: Link
  6. Mnif E., Salhi B., Trabelsi L., Jarboui A. Efficiency and Herding Analysis in Gold-backed Cryptocurrencies. Heliyon, 2022, vol. 8, iss. 12. URL: Link
  7. Naeem M.A., Mbarki I., Shahzad S.J.H. Predictive Role of Online Investor Sentiment for Cryptocurrency Market: Evidence from Happiness and Fears. International Review of Economics & Finance, 2021, vol. 73, pp. 496–514. URL: Link
  8. Bouri E., Gupta R., Roubaud D. Herding Behaviour in Cryptocurrencies. Finance Research Letters, 2019, vol. 29, pp. 216–221. URL: Link
  9. Menkhoff L., Taylor M.P. The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis. Journal of Economic Literature, 2007, vol. 45, no. 4, pp. 936–972. URL: Link
  10. Greenwood R., Nagel S. Inexperienced Investors and Bubbles. Journal of Financial Economics, 2009, vol. 93, iss. 2, pp. 239–258. URL: Link
  11. Anamika, Chakraborty M., Subramaniam S. Does Sentiment Impact Cryptocurrency? Journal of Behavioral Finance, 2023, vol. 24, iss. 2, pp. 202–218. URL: Link
  12. Ángeles López-Cabarcos M., Vázquez-Rodríguez P., Quiñoá-Piñeiro L.M. An Approach to Employees' Job Performance Through Work Environmental Variables and Leadership Behaviours. Journal of Business Research, 2022, vol. 140, pp. 361–369. URL: Link
  13. Kumar P., Islam M.A., Pillai R., Sharif T. Analysing the Behavioural, Psychological, and Demographic Determinants of Financial Decision Making of Household Investors. Heliyon, 2023, vol. 9, iss. 2. URL: Link
  14. Laurini M.P., Furlani L.G.C., Portugal M.S. Empirical Market Microstructure: An Analysis of the BRL/US$ Exchange Rate Market. Emerging Markets Review, 2008, vol. 9, iss. 4, pp. 247–265. URL: Link
  15. Chang C.Y., Shie F.S. The Relation Between Relative Order Imbalance and Intraday Futures Returns: An Application of the Quantile Regression Model to Taiwan. Emerging Markets Finance & Trade, 2011, vol. 47, no. 3, pp. 69–87.
  16. Ha L.T., Nham N.T.H. An Application of a TVP-VAR Extended Joint Connected Approach to Explore Connectedness Between WTI Crude Oil, Gold, Stock and Cryptocurrencies During the COVID-19 Health Crisis. Technological Forecasting and Social Change, 2022, vol. 183. URL: Link
  17. Georgoula I., Pournarakis D., Bilanakos C. et al. Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices. 2015. URL: Link
  18. Anastasiou D., Ballis A., Drakos K. Constructing a Positive Sentiment Index for COVID-19: Evidence from G20 Stock Markets. International Review of Financial Analysis, 2022, vol. 81. URL: Link
  19. Cheng X. The Impact of Economic Policy Uncertainty on the Efficiency of Corporate Working Capital Management – The Evidence from China. Modern Economy, 2019, vol. 10, no. 3, pp. 811–827. URL: Link
  20. Green D.M., Swets J.A. Signal Detection Theory and Psychophysics. New York, Wiley, 1966.
  21. Fama E.F. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 1970, vol. 25, no. 2, pp. 383–417. URL: Link
  22. Gregoriou A. Cryptocurrencies and Asset Pricing. Applied Economics Letters, 2019, vol. 26, iss. 12, pp. 995–998. URL: Link
  23. Connelly B.L., Certo S.T., Ireland R.D., Reutze C.R. Signaling Theory: A Review and Assessment. Journal of Management, 2011, vol. 37, iss. 1, pp. 39–67. URL: Link
  24. Romer D. Rational Asset-Price Movements Without News. The American Economic Review, 1993, vol. 83, no. 5, pp. 1112–1130. URL: Link
  25. Tetlock P.C. All the News That's Fit to Reprint: Do Investors React to Stale Information? The Review of Financial Studies, 2011, vol. 24, iss. 5, pp. 1481–1512. URL: Link
  26. Shen D., Urquhart A., Wang P. Does Twitter Predict Bitcoin? Economics Letters, 2019, vol. 174, pp. 118–122. URL: Link
  27. Karalevicius V., Degrande N., De Weerdt J. Using Sentiment Analysis to Predict Interday Bitcoin Price Movements. Journal of Risk Finance, 2018, vol. 19, no. 1, pp. 56–75. URL: Link
  28. Shiller R.J. Irrational Exuberance. Princeton, Princeton University Press, 2000, 296 p.
  29. Black F. Noise. The Journal of Finance, 1986, vol. 41, iss. 3, pp. 528–543. URL: Link
  30. Tetlock P.C., Saar-Tsechansky M., Macskassy S. More Than Words: Quantifying Language to Measure Firms' Fundamentals. The Journal of Finance, 2008, vol. 63, no. 3, pp. 1437–1467. URL: Link
  31. Caferra R. Good Vibes Only: The Crypto-Optimistic Behavior. Journal of Behavioral and Experimental Finance, 2020, vol. 28. URL: Link
  32. Philippas D., Rjiba H., Guesmi K., Goutte S. Media Attention and Bitcoin Prices. Finance Research Letters, 2019, vol. 30, pp. 37–43. URL: Link
  33. Bouri E., Gupta R., Tiwari A.K., Roubaud D. Does Bitcoin Hedge Global Uncertainty? Evidence from Wavelet-Based Quantile-in-Quantile Regressions. Finance Research Letters, 2017, vol. 23, pp. 87–95. URL: Link
  34. Mnif E., Jarboui A., Mouakhar K. How the Cryptocurrency Market Has Performed During COVID-19? A Multifractal Analysis. Finance Research Letters, 2020, vol. 36. URL: Link
  35. Umar Z., Gubareva M. A Time–Frequency Analysis of the Impact of the COVID-19 Induced Panic on the Volatility of Currency and Cryptocurrency Markets. Journal of Behavioral and Experimental Finance, 2020, vol. 28. URL: Link
  36. Vidal-Tomás D. Transitions in the Cryptocurrency Market During the COVID-19 Pandemic: A Network Analysis. Finance Research Letters, 2021, vol. 43. URL: Link
  37. Han H., Linton O., Oka T., Whang Y.-J. The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability Between Time Series. Journal of Econometrics, 2016, vol. 193, iss. 1, pp. 251–270. URL: Link
  38. Borg A., Boldt M. Using VADER Sentiment and SVM for Predicting Customer Response Sentiment. Expert Systems with Applications, 2020, vol. 162. URL: Link
  39. Krumholz M.R., Forbes J.C. VADER: A Flexible, Robust, Open-Source Code for Simulating Viscous Thin Accretion Disks. Astronomy and Computing, 2015, vol. 11, part A, pp. 1–17. URL: Link
  40. Gaies B., Nakhli M.S., Sahut J.M., Guesmi K. Is Bitcoin Rooted in Confidence? – Unraveling the Determinants of Globalized Digital Currencies. Technological Forecasting and Social Change, 2021, vol. 172. URL: Link
  41. Jo H., Park H., Shefrin H. Bitcoin and Sentiment. The Journal of Futures Markets, 2020, vol. 40, iss. 12, pp. 1861–1879. URL: Link
  42. Bianchi D., Babiak M., Dickerson A. Trading Volume and Liquidity Provision in Cryptocurrency Markets. Journal of Banking & Finance, 2022, vol. 142. URL: Link
  43. Kristoufek L. BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific Reports, 2013, vol. 3. URL: Link
  44. Partida A., Gerassis S., Criado R. et al. The Chaotic, Self-Similar and Hierarchical Patterns in Bitcoin and Ethereum Price Series. Chaos, Solitons & Fractals, 2022, vol. 165, part 2. URL: Link
  45. Tversky A., Kahneman D. Judgment under Uncertainty: Heuristics and Biases: Biases in Judgments Reveal Some Heuristics of Thinking under Uncertainty. Science, 1974, vol. 185, no. 4157, pp. 1124–1131. URL: Link
  46. Loughran T., McDonald B. IPO First-Day Returns, Offer Price Revisions, Volatility, and Form S-1 Language. Journal of Financial Economics, 2013, vol. 109, iss. 2, pp. 307–326. URL: Link
  47. Lin Z.-Y. Investor Attention and Cryptocurrency Performance. Finance Research Letters, 2021, vol. 40. URL: Link
  48. Al Guindy M. Cryptocurrency Price Volatility and Investor Attention. International Review of Economics & Finance, 2021, vol. 76, pp. 556–570. URL: Link

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