Федорова Е.А.доктор экономических наук, профессор департамента корпоративных финансов и корпоративного управления, Финансовый Университет при Правительстве РФ, Москва, Российская Федерация ecolena@mail.ru https://orcid.org/0000-0002-3381-6116 SPIN-код: 7520-2160
Предмет. Взаимосвязь между настроениями, вызванными новостями на профессиональном форуме CoinTelegragh и динамикой криптовалют биткоин, лайткоин и эфириум. Цели. Оценить влияние тональности различных интернет-публикаций на волатильность криптовалют, а также предсказательную силу Google Trends и индекса неопределенности VIX в отношении криптовалют. Методология. Использован метод кросс-квантилограмм, предобученная нейросеть VADER. Результаты. Обнаружено, что рынок криптовалют имеет сложный компонент настроений, его цены и торговая активность определяются популярностью, эмоциями и настроениями. При этом период COVID-19 и геополитических шоков усилил это влияние. Выводы. В период превалирования негативных новостей и публикаций рынок криптовалют сужается, объем торгов уменьшается, а интерес у интернет-пользователей снижается до минимума. Эйфория на рынке, напротив, привлекает новых неквалифицированных инвесторов, что подтверждается количеством просмотров базовой информации о криптовалютах в Википедии. Индекс Google Trends в краткосрочном периоде 1-3 дня можно использовать для прогнозирования цены закрытия биткоина, лайткоина и эфириума, в то время как индекс неопределенности фондового рынка VIX не имеет взаимосвязи с рынком криптовалют. Это означает, что криптовалюты могут быть использованы как актив-убежище при сильной волатильности фонового рынка.
Ключевые слова: индекс волатильности криптовалюты, волатильность, внимание инвесторов, биткоин
Список литературы:
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
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
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
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
Liu Yu., Tsyvinski A. Risks and Returns of Cryptocurrency. Review of Financial Studies, 2021, vol. 34, iss. 6, pp. 2689–2727. URL: Link
Mnif E., Salhi B., Trabelsi L., Jarboui A. Efficiency and Herding Analysis in Gold-backed Cryptocurrencies. Heliyon, 2022, vol. 8, iss. 12. URL: Link
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
Bouri E., Gupta R., Roubaud D. Herding Behaviour in Cryptocurrencies. Finance Research Letters, 2019, vol. 29, pp. 216–221. URL: Link
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
Greenwood R., Nagel S. Inexperienced Investors and Bubbles. Journal of Financial Economics, 2009, vol. 93, iss. 2, pp. 239–258. URL: Link
Anamika, Chakraborty M., Subramaniam S. Does Sentiment Impact Cryptocurrency? Journal of Behavioral Finance, 2023, vol. 24, iss. 2, pp. 202–218. URL: Link
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
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
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
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.
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
Georgoula I., Pournarakis D., Bilanakos C. et al. Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices. 2015. URL: Link
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
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
Green D.M., Swets J.A. Signal Detection Theory and Psychophysics. New York, Wiley, 1966.
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
Gregoriou A. Cryptocurrencies and Asset Pricing. Applied Economics Letters, 2019, vol. 26, iss. 12, pp. 995–998. URL: Link
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
Romer D. Rational Asset-Price Movements Without News. The American Economic Review, 1993, vol. 83, no. 5, pp. 1112–1130. URL: Link
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
Shen D., Urquhart A., Wang P. Does Twitter Predict Bitcoin? Economics Letters, 2019, vol. 174, pp. 118–122. URL: Link
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
Shiller R.J. Irrational Exuberance. Princeton, Princeton University Press, 2000, 296 p.
Black F. Noise. The Journal of Finance, 1986, vol. 41, iss. 3, pp. 528–543. URL: Link
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
Caferra R. Good Vibes Only: The Crypto-Optimistic Behavior. Journal of Behavioral and Experimental Finance, 2020, vol. 28. URL: Link
Philippas D., Rjiba H., Guesmi K., Goutte S. Media Attention and Bitcoin Prices. Finance Research Letters, 2019, vol. 30, pp. 37–43. URL: Link
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
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
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
Vidal-Tomás D. Transitions in the Cryptocurrency Market During the COVID-19 Pandemic: A Network Analysis. Finance Research Letters, 2021, vol. 43. URL: Link
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
Borg A., Boldt M. Using VADER Sentiment and SVM for Predicting Customer Response Sentiment. Expert Systems with Applications, 2020, vol. 162. URL: Link
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
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
Jo H., Park H., Shefrin H. Bitcoin and Sentiment. The Journal of Futures Markets, 2020, vol. 40, iss. 12, pp. 1861–1879. URL: Link
Bianchi D., Babiak M., Dickerson A. Trading Volume and Liquidity Provision in Cryptocurrency Markets. Journal of Banking & Finance, 2022, vol. 142. URL: Link
Kristoufek L. BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific Reports, 2013, vol. 3. URL: Link
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
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
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
Lin Z.-Y. Investor Attention and Cryptocurrency Performance. Finance Research Letters, 2021, vol. 40. URL: Link
Al Guindy M. Cryptocurrency Price Volatility and Investor Attention. International Review of Economics & Finance, 2021, vol. 76, pp. 556–570. URL: Link
