YATIRIMCI MUTLULUĞU VE KRİPTO PARA GETİRİLERİ ARASINDAKİ İLİŞKİ: EN BÜYÜK İLK BEŞ KRİPTO PARA BİRİMİNDEN KANITLAR

Çalışma, yatırımcı mutluluğu ile kripto para getirileri arasındaki nedensellik ilişkisini ortaya çıkarmayı amaçlamaktadır. Bu amaç doğrultusunda piyasa değeri bakımından ilk sırada yer alan Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP) ve Cardano (ADA)’ya odaklanılmıştır. Yatırımcı mutluluğunu ölçmek için ise Twitter tabanlı Mutluluk Endeksi kullanılmıştır. Çalışmanın örneklemi 1 Ocak 2019 ile 2 Ekim 2021 arasındaki dönemi kapsamaktadır. Çalışmada ortak değişkenlerin durağanlığını tespit etmek için Zivot-Andrews testinden faydalanılmıştır. Tüm değişkenlerin seviyelerde durağan olduğundan emin olduktan sonra, mutluluk endeksi ile kripto para getirileri arasındaki ilişkiyi anlamak için Granger nedensellik testi uygulanmıştır. Ayrıca etki-tepki analizi ile kripto para getirileri ve mutluluk endeksinde meydana gelecek şokların etkileri analiz edilmiştir. Bulgular, BTC'den Mutluluk Endeksi'ne ve Mutluluk Endeksi'nden ETH'ye tek yönlü bir ilişki olduğunu göstermektedir. Kripto para getirileri ile yatırımcı mutluluğu arasındaki nedensellik ilişkisinin kripto para birimleri arasında farklılık gösterdiği düşünüldüğünde, yatırımcıların mutluluk endeksini yakından takip etmeleri ve yatırımcı mutluluğundaki değişimlere karşılık portföylerinde ayarlamalar yapmaları gerektiği düşünülmektedir.

Anahtar Kelimeler:

Yatırımcı mutluluğu, Kriptopara birimi, Granger nedensellik, Etki-tepki analizi

INVESTOR HAPPINESS AND CRYPTOCURRENCY RETURNS: FRESH EVIDENCE FROM TOP FIVE CRYPTOCURRENCIES

The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.

Keywords:

Investor happiness, Cryptocurrency, Granger causality, Impulse-response analysis,

PDF

___

Abakah, E. J. A., Gil-Alana, L. A., Madigu, G., & Romero-Rojo, F. (2020). Volatility persistence in cryptocurrency markets under structural breaks. International Review of Economics & Finance, 69, 680-691.
Abraham, M. (2020). Studying the patterns and long‐run dynamics in cryptocurrency prices. Journal of Corporate Accounting & Finance, 31(3), 98-113.
Akyildirim, E., Aysan, A. F., Cepni, O., & Darendeli, S. P. C. (2021). Do investor sentiments drive cryptocurrency prices?. Economics Letters, 206, 109980.
Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570.
Anamika, Chakraborty, M., & Subramaniam, S. (2021). Does sentiment impact cryptocurrency?. Journal of Behavioral Finance, 1-17.
Apergis, N., Koutmos, D., & Payne, J. E. (2021). Convergence in cryptocurrency prices? The role of market microstructure. Finance Research Letters, 40, 101685.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151.
Balcilar, M., Bonato, M., Demirer, R., & Gupta, R. (2017). The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach. Resources Policy, 51, 77-84.
Ballis, A., & Drakos, K. (2020). Testing for herding in the cryptocurrency market. Finance Research Letters, 33, 101210.
Banerjee, A. K., Akhtaruzzaman, M., Dionisio, A., Almeida, D., & Sensoy, A. (2022). Nonlinear nexus between cryptocurrency returns and COVID–19 COVID-19 news sentiment. Journal of Behavioral and Experimental Finance, 36, 100747.
Basher, S. A., & Sadorsky, P. (2022). Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?. Machine Learning with Applications, 9, 100355.
Bouri, E., Gabauer, D., Gupta, R., & Tiwari, A. K. (2021). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Behavioral and Experimental Finance, 30, 100463.
Bouri, E., Shahzad, S. J. H., & Roubaud, D. (2019). Co-explosivity in the cryptocurrency market. Finance Research Letters, 29, 178-183.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Buckman, S. R., Shapiro, A. H., Sudhof, M., & Wilson, D. J. (2020). News sentiment in the time of COVID-19. FRBSF Economic Letter, 8(1), 1-5.
Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32.
Dateportal (2022). Digital 2022: Global overview report. Retrieved from: https://datareportal.com/reports/digital-2022-global-overview-report
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.
Dunbar, K., & Owusu-Amoako, J. (2022). Cryptocurrency returns under empirical asset pricing. International Review of Financial Analysis, 82, 102216.
Elliot, G., Rothenberg T. J. & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64, 813 836.
Flori, A. (2019). News and subjective beliefs: A Bayesian approach to Bitcoin investments. Research in International Business and Finance, 50, 336-356.
Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross Spectral Methods. Econometrica, 37(3), 424-438.
Gregoriou, A. (2019). Cryptocurrencies and asset pricing. Applied Economics Letters, 26(12), 995-998.
Gronwald, M. (2021). How explosive are cryptocurrency prices?. Finance Research Letters, 38, 101603.
Guler, D. (2021). The Impact of investor sentiment on bitcoin returns and conditional volatilities during the era of Covid-19. Journal of Behavioral Finance, 1-14.
Haykir, O., & Yagli, I. (2022). Speculative bubbles and herding in cryptocurrencies. Financial Innovation, 8(1), 78.
Hedonometer (2022). Retrieved from: http://www.hedonometer.org/about.html
Jo, H., Park, H., & Shefrin, H. (2020). Bitcoin and sentiment. Journal of Futures Markets, 40(12), 1861-1879.
Keilbar, G., & Zhang, Y. (2021). On cointegration and cryptocurrency dynamics. Digital Finance, 3, 1-23.
Koutmos, D. (2023). Investor sentiment and bitcoin prices. Review of Quantitative Finance and Accounting, 60(1), 1-29.
Kraaijeveld, O., & De Smedt, J. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65, 101188.
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992). Testing The Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That The Economic Time Series Have a Unit Root?. Journal of Econometrics, 54, 159 178.
Kyriazis, N., Papadamou, S., & Corbet, S. (2020). A systematic review of the bubble dynamics of cryptocurrency prices. Research in International Business and Finance, 54, 101254.
Laborda, R., & Olmo, J. (2014). Investor sentiment and bond risk premia. Journal of Financial Markets, 18, 206-233.
Lee, K., & Jeong, D. (2023). Too much is too bad: The effect of media coverage on the price volatility of cryptocurrencies. Journal of International Money and Finance, 133, 102823.
Li, R., Li, S., Yuan, D., & Zhu, H. (2021). Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis. Research in International Business and Finance, 56, 101389.
Luo, J., Demirer, R., Gupta, R., & Ji, Q. (2022). Forecasting oil and gold volatilities with sentiment indicators under structural breaks. Energy Economics, 105, 105751.
Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS one, 8(5), e64417.
Phillips, P. C. B., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335-346.
Shahzad, S. J. H., Bouri, E., Ahmad, T., Naeem, M. A., & Vo, X. V. (2021). The pricing of bad contagion in cryptocurrencies: a four-factor pricing model. Finance Research Letters, 41, 101797.
Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict Bitcoin?. Economics Letters, 174, 118-122.
Shen, D., Urquhart, A., & Wang, P. (2020). A three-factor pricing model for cryptocurrencies. Finance Research Letters, 34, 101248.
Shen, D., Urquhart, A., & Wang, P. (2020). A three-factor pricing model for cryptocurrencies. Finance Research Letters, 34, 101248.
Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of Economics and Financial Analysis, 2(2), 1-27.
Teker, D., Teker, S., & Ozyesil, M. (2020). Macroeconomic determinants of cryptocurrency volatility: Time series analysis. Journal of Business & Economic Policy, 7(1), 65-71.
Wang, J., Ma, F., Bouri, E., & Guo, Y. (2022). Which factors drive bitcoin volatility: Macroeconomic, technical, or both?. Journal of Forecasting, 1-19.
Wang, Q., & Chong, T. T. L. (2021). Factor pricing of cryptocurrencies. The North American Journal of Economics and Finance, 57, 101348.
Waters, G. A., & Bui, T. (2022). An empirical test for bubbles in cryptocurrency markets. Journal of Economics and Finance, 1-13.
Zhang, J., & Zhang, C. (2022). Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter. Research in International Business and Finance, 61, 101656.
Zivot E. & Andrews D.W.K. (1992). Further Evidence on the Great Crash, the Oil Price Shock, and the Unit-Root Hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.