Criptoactivos en el nuevo contexto financierotipos de interés, precio y adopción

  1. Santiago CARBÓ VALVERDE 1
  2. Pedro J. CUADROS-SOLAS 2
  3. Francisco RODRÍGUEZ FERNÁNDEZ 3
  1. 1 Universitat de València
    info
    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

    Geographic location of the organization Universitat de València
  2. 2 CUNEF Universidad
  3. 3 Universidad de Granada
    info
    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

    Geographic location of the organization Universidad de Granada
Show affiliations +
Journal:
Papeles de economía española

ISSN: 0210-9107

Year of publication: 2023

Issue Title: El regreso de los tipos de interés y sus efectos

Issue: 178

Pages: 118-129

Type: Article

DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Papeles de economía española

Sustainable development goals

info

SDG classification obtained using Aurora SDG artificial intelligence model.

Abstract

The evolution of cryptomarkets has revealed the existence of interconnections between cryptoassets, financial markets and the economic cycle. In the context of an increase in the inflationary and interest rate normalization, this article examines the relationship between interest rates, the price of cryptoassets and their adoption. A negative correlation is shown. High rates correspond to lower valuations of cryptoassets and a lower adoption. Furthermore, the article empirically demonstrates that the socioeconomic characteristics of individuals contribute the most to predicting the adoption of cryptoassets.

Bibliographic References

  • ALONSO-ROBISCO, A. y CARBÓ, J. M. (2022). Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio. International Review of Financial Analysis, 84 (July). https://doi.org/10.1016/j.irfa.2022.102372
  • AUER, R., CORNELLI, G., DOERR, S., FROST, J. y GAMBACORTA, L. (2022). Crypto trading and bitcoin price: evidence from a new database of retail adoption. BIS Working Papers, n.º 1049. Bank for International Settlements.
  • BAJARI, P., NEKIPELOV, D., RYAN, S. P. y Yang, M. (2015). Machine Learning Methods for Demand Estimation. American Economic Review, 105(5), pp. 481-485.
  • BALUTEL, D., FELT, M., NICHOLLS, G. y VOIA, M. (2022a). Bitcoin Awareness, Ownership and Use: 2016 – 20. Bank of Canada Staff Discussion Paper, n.º 2022-10.
  • BALUTEL, D., HENRY, C., VÁSQUEZ, J. y VOIA, M. (2022b). Bitcoin adoption and beliefs in Canada. Canadian Journal of Economics, 55 (November 2022), pp. 1729-1761. https://doi.org/10.1111/caje.12620
  • BARBOZA, F., KIMURA, H. y ALTMAN, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, pp. 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
  • BHIMANI, A., HAUSKEN, K. y ARIF, S. (2022). Do national development factors affect cryptocurrency adoption? Technological Forecasting & Social Change, 181 (August 2021), 121739. https://doi.org/10.1016/j.techfore.2022.121739
  • BÖYÜKASLAN, A. y ECER, F. (2021). Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework. Technology in Society, 67 (September). https://doi.org/10.1016/j.techsoc.2021.101745
  • CARBÓ-VALVERDE, S., CUADROS-SOLAS, P. y RODRÍGUEZ-FERNÁNDEZ, F. (2020). A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests. In PLoS ONE (Vol. 15, Issue 10 October). https://doi.org/10.1371/journal.pone.0240362
  • CARBÓ-VALVERDE, S., CUADROS-SOLAS, P. J. y RODRÍGUEZ-FERNÁNDEZ, F. (2023). Cryptocurrency ownership and biases in perceived financial literacy (July 14, 2023). Available at SSRN: https://ssrn.com/abstract=4509931 or http://dx.doi.org/10.2139/ ssrn.4509931
  • CARMONA, P., CLIMENT, F. y MOMPARLER, A. (2019). Predicting failure in the U.S. banking sector: An extreme gradient boosting approach. International Review of Economics and Finance, 61 (September 2017), pp. 304-323. https://doi.org/10.1016/j.iref.2018.03.008
  • CHEN, M. A., WU, Q. y YANG, B. (2019). How Valuable Is FinTech Innovation? Review of Financial Studies, 32(5), pp. 2062-2106. https://doi.org/10.1093/rfs/hhy130
  • CNMV (2022). Estudio sobre las criptomonedas y la efectividad de las medidas impulsadas por la CNMV.
  • ECB (2022). Financial Stability Review. May 2022, pp. 1-125.
  • FERNÁNDEZ-DELGADO, M., CERNADAS, E. y BARRO, S. (2014). Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? Journal of Machine Learning Research, 15, pp. 3133-3181. https://doi.org/10.1080/13216597.1999.9751892
  • FUJIKI, H. (2020). Who adopts crypto assets in Japan? Evidence from the 2019 financial literacy survey. Journal of The Japanese and International Economies, 58 (July), 101107. https://doi.org/10.1016/j.jjie.2020.101107
  • FUJIKI, H. (2021). Crypto asset ownership, financial literacy, and investment experience. Applied Economics, 53(39), pp. 4560-4581. https://doi.org/10.1080/00036846.2021.1904125
  • GLASER, F., ZIMMERMANN, K., HAFERKORN, M., WEBER, M. C. y SIERING, M. (2014). Bitcoin - Asset or Currency? Revealing Users’ Hidden Intentions (April 15, 2014). ECIS 2014 (Tel Aviv), Available at SSRN: https://ssrn.com/abstract=2425247
  • HACKETHAL, A., HANSPAL, T., LAMMER, D. M. y RINK, K. (2022). The Characteristics and Portfolio Behavior of bitcoin Investors: Evidence from Indirect Cryptocurrency Investments. Review of Finance, December 2021, pp. 855-898.
  • HASSO, T., PELSTER, M. y BREITMAYER, B. (2019). Who trades cryptocurrencies, how do they trade it, and how do they perform? Evidence from brokerage accounts. Journal of Behavioral and Experimental Finance, 23, pp. 64-74. https://doi.org/10.1016/j.jbef.2019.04.009
  • HENRY, C. S., HUYNH, K. P., NICHOLLS, G. y NICHOLSON, M. W. (2019). 2018 bitcoin Omnibus Survey: Awareness and Usage. Bank of Canada Staff Discussion Paper, n.º 2019-10.
  • PANOS, G. A., KARKKAINEN, T. y ATKINSON, A. (2020). Financial Literacy and Attitudes to Cryptocurrencies. Working Papers in Responsible Banking & Finance WP, n.º 20-002.
  • STIX, H. (2021). Ownership and purchase intention of crypto – assets: survey results. In Empirica (Vol. 48, Issue 1). Springer US. https://doi.org/10.1007/s10663020-09499-x
  • TANAKA, K., KINKYO, T. y HAMORI, S. (2016). Random forests-based early warning system for bank failures. Economics Letters, 148, pp. 118-121. https://doi.org/10.1016/j.econlet.2016.09.024
  • TIFFIN, A. (2019). Machine Learning and Causality: The Impact of Financial Crises on Growth. IMF Working Paper, 19(228).
  • ZHAO, H. y ZHANG, L. (2021). Financial literacy or investment experience: which is more influential in cryptocurrency investment? International Journal of Bank Marketing, 39(7), pp. 1208-1226. https://doi.org/10.1108/IJBM-112020-0552
Data source: Dialnet