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Resumen
Este artículo analiza el impacto del contenido audiovisual
en el precio y el volumen de transacciones de Bitcoin,
un área poco explorada en la literatura financiera. A través
un análisis correlacional y econométrico, se estudian datos de audiencia de películas, series y documentales sobre Bitcoin, junto con métricas de interés público en YouTube y Twitch.
Los resultados muestran correlaciones débiles (<0,2) entre los niveles de búsqueda de la mayoría de los títulos y las variables financieras de Bitcoin. Sin embargo, en YouTube, el aumento de suscriptores en canales relacionados con criptomonedas tiene un efecto positivo y significativo en el precio y el volumen de transacciones de Bitcoin. Estos hallazgos resaltan la influencia de los creadores de contenido en la adopción e inversión en criptomonedas, lo que proporciona un marco para investigaciones futuras sobre el impacto de los medios audiovisuales en los mercados financieros.
Citas
Aalborg, H., Molnár, P., & De Vries, J. (2019). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29, 255–265. https://doi.org/10.1016/j.frl.2018.08.010
Abraham, J., Higdon, D., Nelson, J., & Ibarra, J. (2018). Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis. SMU Data Science Review, 1(3), 1. https://scholar.smu.edu/datasciencereview/vol1/iss3/1
Akdag, M., & Emsen, H. (2021). Effect of monetary growth in USA on selected assets: S&P500, Gold & Bitcoin estimates with Facebook Prophet. In Administrative and Economic Science Research Theory (pp. 219–234). Livre de Lyon. https://www.researchgate.net/publication/357340269_EFFECT_OF_MONETARY_GROWTH_IN_USA_ON_SELECTED_ASSETS_SP500_GOLD_BITCOIN_ESTIMATES_WITH_FACEBOOK_PROPHET
Ante, L. (2021). How Elon Musk’s Twitter Activity Moves Cryptocurrency Markets. SSRN. https://doi.org/10.2139/ssrn.3778844
Aslanidis, N., Bariviera, A., & López, O. (2022). The link between cryptocurrencies and Google Trends attention. Finance Research Letters, 47(Part A), 102654. https://doi.org/10.1016/j.frl.2021.102654
Bartos, J. (2015). Does Bitcoin follow the hypothesis of efficient market? International Journal of Economics Sciences, 4(2), 10–23. https://doi.org/10.20472/ES.2015.4.2.002
Baur, D., & Dimppfl, T. (2017). Excess Volatility as an Impediment for a Digital Currency. SSRN. https://doi.org/10.2139/ssrn.2949754
Bouoitour, J., & Selmi, R. (2016). Bitcoin: A beginning of a new phase? Economics Bulletin, 36(3), 1430–1440. https://www.researchgate.net/publication/305810738_Bitcoin_A_beginning_of_a_new_phase
Bouri, E., Gupta, R., Tiwari, A., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87–95. https://doi.org/10.1016/j.frl.2017.02.009
Brown, J., L’Engle, K., Pardun, C., Guo, G., Kenneavy, K., & Jackson, C. (2006). Sexy Media Matter: Exposure to Sexual Content in Music, Movies, Television, and Magazines Predicts Black and White Adolescents’ Sexual Behavior. Pediatrics, 117(4), 1018–1027. https://doi.org/10.1542/peds.2005-1406
Buhbut, O., Ben Assayag, H., Aharoni-Bar, S., Epstein, M., Tsumi, E., Regev, T., Bunin, A., Honig, A., Kotaro B. O., Ben Arie, G., & Horev, A. (2024). Disability and Patient-Reported Satisfaction in Women with Idiopathic Intracranial Hypertension: A Comparative Study of Venous Sinus Stenting and Medical Management. Diagnostics, 14(22), 2572. https://doi.org/10.3390/diagnostics14222572
Burnie, A., & Yilmaz, E. (2019). Social media and bitcoin metrics: which words matter. Royal Society open science, 6(10), 191068. https://doi.org/10.1098/rsos.191068
Cermak, V. (2017). Can Bitcoin Become a Viable Alternative to Fiat Currencies? An Empirical Analysis of Bitcoin’s Volatility Based on a GARCH Model. SSRN. https://doi.org/10.2139/ssrn.2961405
Charlesworth, A., & Glantz, S. (2005). Smoking in the Movies Increases Adolescent Smoking: A Review. Pediatrics, 116(6), 1516–1528. https://doi.org/10.1542/peds.2005-0141
Chinazzo, C., & Jeleskovic, V. (2024). Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches. Quantitative Finance. https://doi.org/10.48550/arXiv.2401.02049
Colianni, S., Rosales, S., & Signorotti, M. (2022, August 2). Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis. Stanford.edu. http://cs229.stanford.edu/proj2015/029_report.pdf
Conlon, T., Corbet, S., & McGee, R. (2023). The Bitcoin Volume-volatility Relationship: a High Frequency Analysis of Futures and Spot Exchanges. SSRN. https://dx.doi.org/10.2139/ssrn.4615028
Dyhrberg, A., Foley, S., & Svec, J. (2018). How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets. Economics Letters, 171, 140–143. https://doi.org/10.1016/j.econlet.2018.07.032
Elliot, S. (2024, March 11). Bitcoin Alcanza Los $72.000 Superando a la Plata Por Primera Vez en la Historia. Decrypt. https://decrypt.co/es/221182/bitcoin-alcanza-los-72-000-superando-a-la-plata-por-primera-vez-en-la-historia
Feng, X., Cao, X., Li, L., Li, Z., Zhang, Q., Sun, W., Houd, B., Liu, C., & Shi, Z. (2024). Study on Performance and Engineering Application of Novel Expansive Superfine Cement Slurry. Materials, 17(22), 5597. https://doi.org/10.3390/ma17225597}
Fosso Wamba, S., Kala Kamdjoug, J., Epie Bawack, R., & Keogh, J. (2019). Bitcoin, Blockchain and Fintech: a systematic review and case studies in the supply chain. Production Planning & Control, 31(2-3), 115–142. https://doi.org/10.1080/09537287.2019.1631460
Gandal, N., Hamrick, J., Moore, T., & Vasek, M. (2021). The rise and fall of cryptocurrency coins and tokens. Decisions in Economics and Finance, 44(2), 981–1014. https://doi.org/10.1007/s10203-021-00329-8
Gao, X., Huang, W., & Wang, H. (2021). Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility. Virtual Economics, 4(1), 7–18. https://doi.org/10.34021/ve.2021.04.01(1)
Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. (2015). Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices. SSRN. https://doi.org/10.2139/ssrn.2607167
Glenski, M., Saldanha, E., & Volkova, S. (2019). Characterizing Speed and Scale of Cryptocurrency Discussion Spread on Reddit. In The World Wide Web Conference (WWW '19) (pp. 560–570). Association for Computing Machinery. https://doi.org/10.1145/3308558.3313702
Guizani, S., & Nafti, I. (2019). The Determinants of Bitcoin Price Volatility: An Investigation With ARDL Model. Procedia Computer Science, 164, 233–238. https://doi.org/10.1016/j.procs.2019.12.177
Hileman, G., & Rauchs, M. (2017). 2017 Global Cryptocurrency Benchmarking Study. SSRN. https://doi.org/10.2139/ssrn.2965436
Hu, J. (2019). Risk of Bitcoin Market: Volatility, Jumps, and Forecasts. SSRN. https://doi.org/10.2139/ssrn.3658078
Huynh, T. (2021). Does Bitcoin React to Trump’s Tweets? Journal of Behavioral and Experimental Finance, 31. https://doi.org/10.1016/j.jbef.2021.100546
Jana, R., Ghosh, I., & Wallin, M. (2022). Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network. Technological Forecasting and Social Change, 178. https://doi.org/10.1016/j.techfore.2022.121584
Jerdack, N., Dauletbek, A., Divine, M., Hult, M., & Carvalho, A. (2019). Understanding What Drives Bitcoin Trading Activities. arXiv: Computers and Society. https://arxiv.org/abs/1901.00720
Johnson, J., Cohen, P., Smailes, E., Kasen, S., & Brook, J. (2002). Television Viewing and Aggressive Behavior During Adolescence and Adulthood. Science, 295(5564), 2468–2471. https://doi.org/10.1126/science.1062929
Junmde, A., & Cho, B. (2020). Can cryptocurrencies overtake the fiat money. International Journal of Business Performance Management, 21(1/2), 6–20. https://doi.org/10.1504/ijbpm.2020.106107
Karalevicius, V., Degrande, N., & De Weerdt, J. (2018). Using sentiment analysis to predict interday Bitcoin price movements. The Journal of Risk Finance, 19(1), 56–75. https://doi.org/10.1108/JRF-06-2017-0092
Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415. https://doi.org/10.1038/srep03415
Kristoufek, L. (2015). What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923
Li, W., & He, M. (2020). Comparative Analysis of Bitcoin, Ethereum, and Libra. In 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS) (pp. 545–550). IEEE. https://doi.org/10.1109/ICSESS49938.2020.9237710
Lin, Z. (2021). Investor attention and cryptocurrency performance. Finance Research Letters, 40. https://doi.org/10.1016/j.frl.2020.101702
McCormick, S., Rimal, R., Harrington, C., Shafer, M., & Shaikh, H. (2019). Communicating climate change through documentary film: imagery, emotion, and efficacy. Climatic Change, 154(1-2), 1–18. https://doi.org/10.1007/s10584-019-02408-7
Milutinovic, M. (2018). Cryptocurrency. Ekonomika, Journal for Economic Theory and Practice and social Issues, 64(1), https://doi.org/10.5937/ekonomika1801105M
Mittal, A., Dhiman, V., Singh, A., & Prakash, C. (2019). Short-Term Bitcoin Price Fluctuation Prediction Using Social Media and Web Search Data. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (pp. 1–6). IEEE. https://doi.org/10.1109/IC3.2019.8844899
Pant, D., Neupane, P., Poudel, A., Pokhrel, A., & Lama, B. (2018). Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis. In 2018 IEEE 3rd International Conference on Computing, Communication And Security (ICCCS) (pp. 128–132). IEEE. https://
doi.org/10.1109/cccs.2018.8586824
Partida, P. (2019). Bitcoin Bubbles: Epidemic-Diffusion Analyses and Models [Master’s thesis, Eidgenossische Technische Hochschule Zurich - Swiss Federal Institute of Technology Zurich - Entrepreneurial Risk]. https://ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/Master-project_Partida_March2019.pdf
Peterson, T. (2019). Bitcoin Spreads Like a Virus. SSRN, https://doi.org/10.2139/ssrn.3356098
Phillips, R., & Gorse, D. (2017). Predicting cryptocurrency price bubbles using social media data and epidemic modelling. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. https://doi.org/10.1109/ssci.2017.8280809
Phillips, R., & Gorse, D. (2018). Cryptocurrency price drivers: Wavelet coherence analysis revisited. PLoS ONE, 13(4), e0195200. https://doi.org/10.1371/journal.pone.0195200
Polasik, M., Piotrwska, A., Wisniewski, T., Kotkowski, R., & Lighfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20(1), 9–49. https://doi.org/10.1080/10864415.2016.1061413
Prajapati, P. (2020). Predictive analysis of Bitcoin price considering social sentiments. arXiv.Org. https://arxiv.org/abs/2001.10343
Sattarov, O., Jeon, H., Oh, R., & Lee, J. (2020). Forecasting Bitcoin Price Fluctuation by Twitter Sentiment Analysis. In 2020 International Conference on Information Science and Communications Technologies (ICISCT). IEEE. https://doi.org/10.1109/icisct50599.2020.9351527
Senarathne, C. (2019). Possible impact of Facebook’s Libra on Volatility of Bitcoin: Evidence from Initial Coin Offer Funding Data. Management of Organizations; Systematic Research, 81(1), 87–100. https://doi.org/10.1515/mosr-2019-0006
Shanaev, S., Sharma, S., Ghimire, B., & Shuraeva, A. (2020). Taming the blockchain beast? Regulatory implications for the cryptocurrency Market. Research in International Business and Finance, 51, 101080. https://doi.org/10.1016/j.ribaf.2019.101080
Shen, D., Urquhart, A., & Wang, P. (2019). Does Twitter predict Bitcoin? Economics Letters, 174, 118–122. https://doi.org/10.1016/j.econlet.2018.11.007
Smith, S., & Castonguay, J. (2020). Blockchain and Accounting Governance: Emerging Issues and Considerations for Accounting and Assurance Professionals. Journal of Emerging Technologies in Accounting, 17(1), 119–131. https://doi.org/10.2308/jeta-52686
Urquhart, A. (2018). What causes the attention of Bitcoin? Economics Letters, 166, 40–44. https://doi.org/10.1016/j.econlet.2018.02.017
Vinkóczi, T., Idziak, E., Tamás, B., & Kurucz, A. (2024). Tech titans: Generation Zs role in the FinTech evolution. Journal of Infrastructure, Policy and Development, 8(10), 8201. https://doi.org/10.24294/jipd.v8i10.8201
Wooley, S., Edmonds, A., Bagavathi, A., & Krishnan, S. (2019). Extracting Cryptocurrency Price Movements from the Reddit Network Sentiment. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 500–505). IEEE. https://doi.org/10.1109/ICMLA.2019.00093
Yao, W., Xu, K., & Li, Q. (2019). Exploring the Influence of News Articles on Bitcoin Price with Machine Learning. In IEEE Symposium on Computers and Communications (ISCC) (pp. 1–6). IEEE. https://doi.org/10.1109/iscc47284.2019.8969596
Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters, 22(13), 1030–1036. https://doi.org/10.1080/13504851.2014.995359
Zhang, W., & Wang, P. (2020). Investor attention and the pricing of cryptocurrency market. Evolutionary and Institutional Economics Review, 17(2), 445–468. https://doi.org/10.1007/s40844-020-00182-1
Zhou, F., Wang, X., Wang, Y., Li, G., & Dong, C. (2024). Study on the Mechanical Properties and Basic Elastic Constants of Yunnan Dendrocalamus sinicus Chia et J. L. Sun. Forests, 15(11), 2017. https://doi.org/10.3390/f15112017
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