The impact of disruptive technology on banking under switching volatility regimes
Abstract
This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios.
Keyword : banking, disruptive technology, volatility, Factor model, Markov heteroscedastic regime switching, volatility clustering, asset pricing
How to Cite
Arenas, L., Gil-Lafuente, A. M., & Boria Reverter, J. (2023). The impact of disruptive technology on banking under switching volatility regimes. Technological and Economic Development of Economy, 29(4), 1264–1290. https://doi.org/10.3846/tede.2023.18976
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdymomunov, A., & Morley, J. (2011). Time variation of CAPM betas across market volatility regimes. Applied Financial Economics, 21(19), 1463–1478. https://doi.org/10.1080/09603107.2011.577010
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the 2nd international symposium on information, bn petrow, f. Czaki, Akademiai Kiado. Budapest.
Andersson, K., & Styf, A. (2020). Blockchain technology & volatility of stock returns: a quantitative study that examines blockchain technology’s impact on volatility in Swedish stock (Master Thesis). Umeå University, Umeå, Sweden. https://www.diva-portal.org/smash/record.jsf?dswid=9035&pid=diva2%3A1445361
Ang, A., & Bekaert, G. (2004). How regimes affect asset al.ocation. Financial Analysts Journal, 60(2), 86–99. https://doi.org/10.2469/faj.v60.n2.2612
Ang, A., & Timmermann, A. (2012). Regime changes and financial markets. Annual Review of Financial Economics, 4(1), 313–337. https://doi.org/10.1146/annurev-financial-110311-101808
Anginer, D., Demirguc-Kunt, A., & Zhu, M. (2014). How does deposit insurance affect bank risk? Evidence from the recent crisis. Journal of Banking & Finance, 48, 312–321. https://doi.org/10.1016/j.jbankfin.2013.09.013
Arenas, L., & Gil Lafuente, A. M. (2021). Impact of emerging technologies in banking and finance in Europe: A volatility spillover and contagion approach. Journal of Intelligent and Fuzzy Systems, 40(2), 1903–1919. https://doi.org/10.3233/JIFS-189195
Arghyrou, M. G., & Kontonikas, A. (2012). The EMU sovereign-debt crisis: Fundamentals, expectations and contagion. Journal of International Financial Markets, Institutions and Money, 22(4), 658–677. https://doi.org/10.1016/j.intfin.2012.03.003
Agrawal, D., Bharath, S., & Viswanathan, S. (2004). Technological change and stock return volatility: Evidence from eCommerce adoptions. SSRN. https://doi.org/10.2139/ssrn.387543
Akyildirim, E., Corbet, S., Sensoy, A., & Yarovaya, L. (2020). The impact of blockchain related name changes on corporate performance. Journal of Corporate Finance, 65, 101759. https://doi.org/10.1016/j.jcorpfin.2020.101759
Asmarani, S. & Wijaya, C. (2020). Effects of fintech on stock return: Evidence from retail banks listed in Indonesia stock exchange. The Journal of Asian Finance, Economics, and Business, 7(7), 95–104. https://doi.org/10.13106/jafeb.2020.vol7.no7.095
Ba, S., Lisic, L. L., Liu, Q., & Stallaert, J. (2013). Stock market reaction to green vehicle innovation. Production and Operations Management, 22(4), 976-990. https://doi.org/10.1111/j.1937-5956.2012.01387.x
Begley, T. A., Purnanandam, A., & Zheng, K. (2017). The strategic underreporting of bank risk. The Review of Financial Studies, 30(10), 3376–3415. https://doi.org/10.1093/rfs/hhx036
Bennett, R. L., Güntay, L., & Unal, H. (2015). Inside debt, bank default risk, and performance during the crisis. Journal of Financial Intermediation, 24(4), 487–513. https://doi.org/10.1016/j.jfi.2014.11.006
Blanco-Oliver, A. (2021). Banking reforms and bank efficiency: Evidence for the collapse of Spanish savings banks. International Review of Economics & Finance, 74, 334–347. https://doi.org/10.1016/j.iref.2021.03.015
Bloomberg L.P. (2021). Bloomberg professional. Retrieved December 30, 2021, from Bloomberg Terminal.
BME Market Data. (2021). Retrieved January 27, 2021, from https://www.bmemarketdata.es/esp/
Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: catching the wave. Harward Business Review, 73(1), 43–53.
Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235. https://doi.org/10.1080/07474939608800353
Brown, J. R., Martinsson, G., & Petersen, B. C. (2017). Stock markets, credit markets, and technology-led growth. Journal of Financial Intermediation, 32, 45–59. https://doi.org/10.1016/j.jfi.2016.07.002
Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological), 37(2), 149–163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x
Brynjolfsson, E., Rock, D., & Syverson, C. (2019). 1. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics (pp. 23–60). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0001
Buchak, G., Matvos, G., Piskorski, T., & Seru, A. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453–483. https://doi.org/10.1016/j.jfineco.2018.03.011
Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56(1), 1–43. https://doi.org/10.1111/0022-1082.00318
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
Chen, J., & Kawaguchi, Y. (2018). Multi-factor asset-pricing models under markov regime switches: Evidence from the Chinese stock market. International Journal of Financial Studies, 6(2), 54. https://doi.org/10.1080/09603107.2011.577010
Chen, B., Yang, X., & Ma, Z. (2022). Fintech and financial risks of systemically important commercial banks in China: An inverted u-shaped relationship. Sustainability, 14(10), 5912. https://doi.org/10.3390/su14105912
Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1–15. https://doi.org/10.1002/jae.2585
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057–1072. https://doi.org/10.2307/1912517
Dou, P. Y., Gallagher, D. R., Schneider, D., & Walter, T. S. (2014). Cross‐region and cross‐sector asset allocation with regimes. Accounting & Finance, 54(3), 809–846. https://doi.org/10.1111/acfi.12017
European Central Bank. (2021). Statistical Data Warehouse. Retrieved December 30, 2021, from https://sdw.ecb.europa.eu/
Fama, E., & French, K. (1992). The cross-section of expected stock returns. The Journal of Finance, 47, 427–465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. https://doi.org/10.1016/0304-405X(93)90023-5
Fama, E., & French, K. (1996). Multifactor explanations of asset pricing anomalies. The Journal of Finance, 51, 55–84. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010
Federal Reserve Bank of St. Louis (n.d.). Retrieved August 15, 2021, from https://fred.stlouisfed.org/series/TB3MS
Gharbi, S., Sahut, J.-M., & Teulon, F. (2014). R&D investments and high-tech firms’ stock return volatility. Technological Forecasting and Social Change, 88, 306–312. https://doi.org/10.1016/j.techfore.2013.10.006
Goldfeld, S. M., & Quandt, R. E. (1973). A Markov model for switching regressions. Journal of Econometrics, 1(1), 3–15. https://doi.org/10.1016/0304-4076(73)90002-X
Greenwood, J., & Jovanovic, B. (1999). The information-technology revolution and the stock market. American Economic Review, 89(2), 116–122. https://doi.org/10.1257/aer.89.2.116
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the econometric society, 57(2), 357–384. https://doi.org/10.2307/1912559
Hannan, E. J. (1980). The estimation of the order of an ARMA process. The Annals of Statistics, 8(5), 1071–1081. https://doi.org/10.1214/aos/1176345144
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 190–195. https://doi.org/10.1111/j.2517-6161.1979.tb01072.x
Hassani, H., Huang, X., & Silva, E. (2018). Banking with blockchain-ed big data. Journal of Management Analytics, 5(4), 256–275. https://doi.org/10.1080/23270012.2018.1528900
Ho, K. Y., Shi, Y., & Zhang, Z. (2020). News and return volatility of Chinese bank stocks. International Review of Economics & Finance, 69, 1095–1105. https://doi.org/10.1016/j.iref.2018.12.003
Hobjin, B., & Jovanovic, B. (2001). The information-technology revolution and the stock market: Evidence. American Economic Review, 91(5), 1203–1220. https://doi.org/10.1257/aer.91.5.1203
Huang, H. C. (2000). Tests of regimes-switching CAPM. Applied Financial Economics, 10(5), 573–578. https://doi.org/10.1080/096031000416451
Investing. (n.d.). Retrieved September 16, 2022, from https://es.investing.com/indices/msci-world-historical-data
Iraola, M. A., & Santos, M. (2007). Technological waves in the stock market. https://www.cemfi.es/ftp/pdf/papers/Seminar/ManuelSantos1805.pdf
Jovanovic, B., & Rosseau, P. L. (2001). Vintage organization capital. National Bureau of Economic Research. https://doi.org/10.3386/w8166
Kearney, C., & Potì, V. (2008). Have European stocks become more volatile? An empirical investigation of idiosyncratic and market risk in the Euro area. European Financial Management, 14(3), 419–444. https://doi.org/10.1111/j.1468-036X.2007.00395.x
Kim, C.-J., & Nelson, C. R. (2017). StateSpace Models with Regimes Switching: Classical and Gibbs-Sampling Approaches with Applications (1 ed.). MIT Press. https://doi.org/10.7551/mitpress/6444.001.0001
Klöckner, M., Schmidt, C. G., & Wagner, S. M. (2022). When blockchain creates shareholder value: empirical evidence from international firm announcements. Production and Operations Management, 31(1), 46–64. https://doi.org/10.1111/poms.13609
Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin.
KPMG. (2020). Transición digital y transformación del negocio bancario en España impulsado por la COVID-19. Retrieved December 30, 2021, from https://home.kpmg/es
Kritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22–39. https://doi.org/10.2469/faj.v68.n3.3
Kydland, F. E., & Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica: Journal of the Econometric Society, 50(6), 1345–1370. https://doi.org/10.2307/1913386
Laitner, J., & Stolyarov, D. (2003). Technological change and the stock market. American Economic Review, 93(4), 1240–1267. https://doi.org/10.1257/000282803769206287
Laitner, J., & Stolyarov, D. (2019). Asset pricing implications of disruptive technological change (working paper). University of Michigan.
Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. The Journal of Finance, 45(1), 221–229. https://doi.org/10.1111/j.1540-6261.1990.tb05088.x
Lane, P. R. (2012). The European sovereign debt crisis. Journal of Economic Perspectives, 26(3), 49–68. https://doi.org/10.1257/jep.26.3.49
Li, Y., Spigt, R., & Swinkels, L. (2017). The impact of FinTech start-ups on incumbent retail banks’ share prices. Financial Innovation, 3(1), 1–16. https://doi.org/10.1186/s40854-017-0076-7
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615. https://doi.org/10.1111/j.1540-6261.1965.tb02930.x
Low, C., & Wong, M. (2020). The effect of FinTech on the financial institution in Six ASEAN countries: Fama-French five-factor asset pricing model approach. In Ninth International Conference on Entrepreneurship and Business Management (ICEBM 2020). Atlantis Press. https://doi.org/10.2991/aebmr.k.210507.034
Lui, A., Lee, M., & Ngai, E. (2021). Impact of artificial intelligence investment on firm value. Annals of Operations Research, 308, 373–388. https://doi.org/10.1007/s10479-020-03862-8
Majid, S., Sultana, N., & Abid, G. (2021). The impact of corporate innovation on abnormal stock returns: The moderating role of investor sentiment. Academy of Strategic Management Journal, 20, 1–16.
Manuelli, R. E. (2000). Technological change, the labor market and the stock market (Working Paper 8022). National Bureau of Economic Research. https://doi.org/10.3386/w8022
Mazzucato, M. (2002). The PC industry: New economy or early life-cycle? Review of Economic Dynamics, 5(2), 318–345. https://doi.org/10.1006/redy.2002.0164
Mazzucato, M. (2006). Innovation and stock prices: a review of some recent work. Revue de l’OFCE, (5), 159–179. https://doi.org/10.3917/reof.073.0159
Mazzucato, M., & Tancioni, M. (2008). Innovation and idiosyncratic risk: An industry-and firm-level analysis. Industrial and Corporate Change, 17(4), 779–811. https://doi.org/10.1093/icc/dtn024
Mishkin, F. S. (2016). The economics of money, banking, and financial markets. Pearson.
Mizrach, B. (1996). Learning and conditional heteroscedasticity in asset returns (Departmental Working Papers 199526). Department of Economics, Rutgers University, New Brunswick, NJ, USA.
MSCI. (n.d.-a). MSCI ACWI IMI Disruptive Technology ESG Filtered Index (EUR). Retrieved September 16, 2022a, from https://www.msci.com/documents/10199/f1e29c8e-8600-508d-6e8f-155c7c429a88
MSCI. (n.d.-b). MSCI World Index (USD). Retrieved September 16, 2022b, from https://www.msci.com/documents/10199/178e6643-6ae6-47b9-82be-e1fc565ededb
Navaretti, G., Calzolari, G., & Mansilla-Fernandez, J. (2018). FinTech and banking. Friends or foes?. Friends or Foes. European Economy Banks Regulation, and the Real Sector 2017, 2, 9–30. https://doi.org/10.2139/ssrn.3099337
Neuberger, J. A. (1991). Risk and return in banking: Evidence from bank stock returns. Economic Review-Federal Reserve Bank of San Francisco, (4), 18.
OFX (n.d.). Retrieved August 15, 2022, from www.ofx.com
Pástor, L., & Veronesi, P. (2006). Was there a Nasdaq bubble in the late 1990s? Journal of Financial Economics, 81(1), 61–100. https://doi.org/10.1016/j.jfineco.2005.05.009
Pástor, Ľ., & Veronesi, P. (2009). Technological revolutions and stock prices. American Economic Review, 99(4), 1451–1483. https://doi.org/10.1257/aer.99.4.1451
Peralta-Alva, A. (2007). The information technology revolution and the puzzling trends in Tobin’s average q. International Economic Review, 48(3), 929–951. https://doi.org/10.1111/j.1468-2354.2007.00450.x
Pérez, C. (2003). Technological revolutions and financial capital. Edward Elgar Publishing. https://doi.org/10.4337/9781781005323
Pérez, C. (2012). Financial bubbles, crises and the role of government in unleashing golden ages. UK Open University, FINNOV Milton Keynes.
Phan, D. H. B., Narayan, P. K., Rahman, R. E., & Hutabarat, A. R. (2020). Do financial technology firms influence bank performance?. Pacific-Basin Finance Journal, 62, 101210. https://doi.org/10.1016/j.pacfin.2019.101210
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Quandt, R. E. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American statistical Association, 55(290), 324–330. https://doi.org/10.1080/01621459.1960.10482067
Rodríguez-Ruiz, Ó., Rodríguez-Duarte, A., & Gómez-Martínez, L. (2016). Does a balanced gender ratio improve performance? The case of Spanish banks (1999–2010). Personnel Review, 45(1), 103–120. https://doi.org/10.1108/PR-07-2014-0143
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341–360. https://doi.org/10.1016/0022-0531(76)90046-6
Sawada, M. (2013). How does the stock market value bank diversification? Empirical evidence from Japanese banks. Pacific-Basin Finance Journal, 25, 40–61. https://doi.org/10.1016/j.pacfin.2013.08.001
Schmidt, W. C., & González, A. (2020). Fintech and tokenization: A legislative study in Argentina and Spain about the application of Blockchain in the field of properties. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(1), 51.
Schumpeter, J. A. (1912). Theorie der wirschaftlichen Entwicklung. Duncker & Humblot.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Schwert, G. W. (2002). Stock volatility in the new millennium: how wacky is Nasdaq? Journal of Monetary Economics, 49(1), 3–26. https://doi.org/10.1016/S0304-3932(01)00099-X
Setiawan, R., Cavaliere, L., Koti, K., Ogunmola, G., Jalil, N. A., Chakravarthi, M. K., Rajest, S. S., Regin, R., & Singh, S. (2021). The artificial intelligence and inventory effect on banking industrial performance. Turkish Online Journal of Qualitative Inquiry (TOJQI), 12(6), 8100–8125.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Shiller, R. C. (2000). Irrational exuberance. Philosophy and Public Policy Quarterly, 20(1), 18–23.
Statista. (2021). Number of employees in the banking sector in Spain from 2010 to 2021. Retrieved December 31, 2021, from https://www.statista.com/statistics/765375/employment-in-the-banking-sector-spain/
Statista. (2022). Fintech-Spain. Retrieved September 15, 2022, from https://www.statista.com/outlook/dmo/fintech/spain#transaction-value
Stiroh, K. J. (2006). A portfolio view of banking with interest and noninterest activities. Journal of Money, Credit and Banking, 38(5), 1351–1361. https://doi.org/10.1353/mcb.2006.0075
Stock, J. H. (1988). Estimating continuous-time processes subject to time deformation: An application to postwar US GNP. Journal of the American Statistical Association, 83(401), 77–85. https://doi.org/10.1080/01621459.1988.10478567
Turner, C. M., Startz, R., & Nelson, C. (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1), 3–22. https://doi.org/10.1016/0304-405X(89)90094-9
Tushman, M., & O’Reilly III, C. A. (1996). Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8–29. https://doi.org/10.2307/41165852
Vendrame, V., Guermat, C., & Tucker, J. (2018). A conditional regime switching CAPM. International Review of Financial Analysis, 56, 1–11. https://doi.org/10.1016/j.irfa.2017.12.001
Visconti-Caparrós, J., & Campos-Blázquez, J. (2021). The development of alternate payment methods and their impact on customer behavior: The Bizum case in Spain. Technological Forecasting and Social Change, 175, 121330. https://doi.org/10.1016/j.techfore.2021.121330
Vives, X. (2019). Competition and stability in modern banking: A post-crisis perspective. International Journal of Industrial Organization, 64, 55–69. https://doi.org/10.1016/j.ijindorg.2018.08.011
Ying, W., Jia, S., & Du, W. (2018). Digital enablement of blockchain: Evidence from HNA group. International Journal of Information Management, 39, 1–4. https://doi.org/10.1016/j.ijinfomgt.2017.10.004
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the 2nd international symposium on information, bn petrow, f. Czaki, Akademiai Kiado. Budapest.
Andersson, K., & Styf, A. (2020). Blockchain technology & volatility of stock returns: a quantitative study that examines blockchain technology’s impact on volatility in Swedish stock (Master Thesis). Umeå University, Umeå, Sweden. https://www.diva-portal.org/smash/record.jsf?dswid=9035&pid=diva2%3A1445361
Ang, A., & Bekaert, G. (2004). How regimes affect asset al.ocation. Financial Analysts Journal, 60(2), 86–99. https://doi.org/10.2469/faj.v60.n2.2612
Ang, A., & Timmermann, A. (2012). Regime changes and financial markets. Annual Review of Financial Economics, 4(1), 313–337. https://doi.org/10.1146/annurev-financial-110311-101808
Anginer, D., Demirguc-Kunt, A., & Zhu, M. (2014). How does deposit insurance affect bank risk? Evidence from the recent crisis. Journal of Banking & Finance, 48, 312–321. https://doi.org/10.1016/j.jbankfin.2013.09.013
Arenas, L., & Gil Lafuente, A. M. (2021). Impact of emerging technologies in banking and finance in Europe: A volatility spillover and contagion approach. Journal of Intelligent and Fuzzy Systems, 40(2), 1903–1919. https://doi.org/10.3233/JIFS-189195
Arghyrou, M. G., & Kontonikas, A. (2012). The EMU sovereign-debt crisis: Fundamentals, expectations and contagion. Journal of International Financial Markets, Institutions and Money, 22(4), 658–677. https://doi.org/10.1016/j.intfin.2012.03.003
Agrawal, D., Bharath, S., & Viswanathan, S. (2004). Technological change and stock return volatility: Evidence from eCommerce adoptions. SSRN. https://doi.org/10.2139/ssrn.387543
Akyildirim, E., Corbet, S., Sensoy, A., & Yarovaya, L. (2020). The impact of blockchain related name changes on corporate performance. Journal of Corporate Finance, 65, 101759. https://doi.org/10.1016/j.jcorpfin.2020.101759
Asmarani, S. & Wijaya, C. (2020). Effects of fintech on stock return: Evidence from retail banks listed in Indonesia stock exchange. The Journal of Asian Finance, Economics, and Business, 7(7), 95–104. https://doi.org/10.13106/jafeb.2020.vol7.no7.095
Ba, S., Lisic, L. L., Liu, Q., & Stallaert, J. (2013). Stock market reaction to green vehicle innovation. Production and Operations Management, 22(4), 976-990. https://doi.org/10.1111/j.1937-5956.2012.01387.x
Begley, T. A., Purnanandam, A., & Zheng, K. (2017). The strategic underreporting of bank risk. The Review of Financial Studies, 30(10), 3376–3415. https://doi.org/10.1093/rfs/hhx036
Bennett, R. L., Güntay, L., & Unal, H. (2015). Inside debt, bank default risk, and performance during the crisis. Journal of Financial Intermediation, 24(4), 487–513. https://doi.org/10.1016/j.jfi.2014.11.006
Blanco-Oliver, A. (2021). Banking reforms and bank efficiency: Evidence for the collapse of Spanish savings banks. International Review of Economics & Finance, 74, 334–347. https://doi.org/10.1016/j.iref.2021.03.015
Bloomberg L.P. (2021). Bloomberg professional. Retrieved December 30, 2021, from Bloomberg Terminal.
BME Market Data. (2021). Retrieved January 27, 2021, from https://www.bmemarketdata.es/esp/
Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: catching the wave. Harward Business Review, 73(1), 43–53.
Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235. https://doi.org/10.1080/07474939608800353
Brown, J. R., Martinsson, G., & Petersen, B. C. (2017). Stock markets, credit markets, and technology-led growth. Journal of Financial Intermediation, 32, 45–59. https://doi.org/10.1016/j.jfi.2016.07.002
Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological), 37(2), 149–163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x
Brynjolfsson, E., Rock, D., & Syverson, C. (2019). 1. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics (pp. 23–60). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0001
Buchak, G., Matvos, G., Piskorski, T., & Seru, A. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453–483. https://doi.org/10.1016/j.jfineco.2018.03.011
Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56(1), 1–43. https://doi.org/10.1111/0022-1082.00318
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
Chen, J., & Kawaguchi, Y. (2018). Multi-factor asset-pricing models under markov regime switches: Evidence from the Chinese stock market. International Journal of Financial Studies, 6(2), 54. https://doi.org/10.1080/09603107.2011.577010
Chen, B., Yang, X., & Ma, Z. (2022). Fintech and financial risks of systemically important commercial banks in China: An inverted u-shaped relationship. Sustainability, 14(10), 5912. https://doi.org/10.3390/su14105912
Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1–15. https://doi.org/10.1002/jae.2585
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057–1072. https://doi.org/10.2307/1912517
Dou, P. Y., Gallagher, D. R., Schneider, D., & Walter, T. S. (2014). Cross‐region and cross‐sector asset allocation with regimes. Accounting & Finance, 54(3), 809–846. https://doi.org/10.1111/acfi.12017
European Central Bank. (2021). Statistical Data Warehouse. Retrieved December 30, 2021, from https://sdw.ecb.europa.eu/
Fama, E., & French, K. (1992). The cross-section of expected stock returns. The Journal of Finance, 47, 427–465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. https://doi.org/10.1016/0304-405X(93)90023-5
Fama, E., & French, K. (1996). Multifactor explanations of asset pricing anomalies. The Journal of Finance, 51, 55–84. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010
Federal Reserve Bank of St. Louis (n.d.). Retrieved August 15, 2021, from https://fred.stlouisfed.org/series/TB3MS
Gharbi, S., Sahut, J.-M., & Teulon, F. (2014). R&D investments and high-tech firms’ stock return volatility. Technological Forecasting and Social Change, 88, 306–312. https://doi.org/10.1016/j.techfore.2013.10.006
Goldfeld, S. M., & Quandt, R. E. (1973). A Markov model for switching regressions. Journal of Econometrics, 1(1), 3–15. https://doi.org/10.1016/0304-4076(73)90002-X
Greenwood, J., & Jovanovic, B. (1999). The information-technology revolution and the stock market. American Economic Review, 89(2), 116–122. https://doi.org/10.1257/aer.89.2.116
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the econometric society, 57(2), 357–384. https://doi.org/10.2307/1912559
Hannan, E. J. (1980). The estimation of the order of an ARMA process. The Annals of Statistics, 8(5), 1071–1081. https://doi.org/10.1214/aos/1176345144
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 190–195. https://doi.org/10.1111/j.2517-6161.1979.tb01072.x
Hassani, H., Huang, X., & Silva, E. (2018). Banking with blockchain-ed big data. Journal of Management Analytics, 5(4), 256–275. https://doi.org/10.1080/23270012.2018.1528900
Ho, K. Y., Shi, Y., & Zhang, Z. (2020). News and return volatility of Chinese bank stocks. International Review of Economics & Finance, 69, 1095–1105. https://doi.org/10.1016/j.iref.2018.12.003
Hobjin, B., & Jovanovic, B. (2001). The information-technology revolution and the stock market: Evidence. American Economic Review, 91(5), 1203–1220. https://doi.org/10.1257/aer.91.5.1203
Huang, H. C. (2000). Tests of regimes-switching CAPM. Applied Financial Economics, 10(5), 573–578. https://doi.org/10.1080/096031000416451
Investing. (n.d.). Retrieved September 16, 2022, from https://es.investing.com/indices/msci-world-historical-data
Iraola, M. A., & Santos, M. (2007). Technological waves in the stock market. https://www.cemfi.es/ftp/pdf/papers/Seminar/ManuelSantos1805.pdf
Jovanovic, B., & Rosseau, P. L. (2001). Vintage organization capital. National Bureau of Economic Research. https://doi.org/10.3386/w8166
Kearney, C., & Potì, V. (2008). Have European stocks become more volatile? An empirical investigation of idiosyncratic and market risk in the Euro area. European Financial Management, 14(3), 419–444. https://doi.org/10.1111/j.1468-036X.2007.00395.x
Kim, C.-J., & Nelson, C. R. (2017). StateSpace Models with Regimes Switching: Classical and Gibbs-Sampling Approaches with Applications (1 ed.). MIT Press. https://doi.org/10.7551/mitpress/6444.001.0001
Klöckner, M., Schmidt, C. G., & Wagner, S. M. (2022). When blockchain creates shareholder value: empirical evidence from international firm announcements. Production and Operations Management, 31(1), 46–64. https://doi.org/10.1111/poms.13609
Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin.
KPMG. (2020). Transición digital y transformación del negocio bancario en España impulsado por la COVID-19. Retrieved December 30, 2021, from https://home.kpmg/es
Kritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22–39. https://doi.org/10.2469/faj.v68.n3.3
Kydland, F. E., & Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica: Journal of the Econometric Society, 50(6), 1345–1370. https://doi.org/10.2307/1913386
Laitner, J., & Stolyarov, D. (2003). Technological change and the stock market. American Economic Review, 93(4), 1240–1267. https://doi.org/10.1257/000282803769206287
Laitner, J., & Stolyarov, D. (2019). Asset pricing implications of disruptive technological change (working paper). University of Michigan.
Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. The Journal of Finance, 45(1), 221–229. https://doi.org/10.1111/j.1540-6261.1990.tb05088.x
Lane, P. R. (2012). The European sovereign debt crisis. Journal of Economic Perspectives, 26(3), 49–68. https://doi.org/10.1257/jep.26.3.49
Li, Y., Spigt, R., & Swinkels, L. (2017). The impact of FinTech start-ups on incumbent retail banks’ share prices. Financial Innovation, 3(1), 1–16. https://doi.org/10.1186/s40854-017-0076-7
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615. https://doi.org/10.1111/j.1540-6261.1965.tb02930.x
Low, C., & Wong, M. (2020). The effect of FinTech on the financial institution in Six ASEAN countries: Fama-French five-factor asset pricing model approach. In Ninth International Conference on Entrepreneurship and Business Management (ICEBM 2020). Atlantis Press. https://doi.org/10.2991/aebmr.k.210507.034
Lui, A., Lee, M., & Ngai, E. (2021). Impact of artificial intelligence investment on firm value. Annals of Operations Research, 308, 373–388. https://doi.org/10.1007/s10479-020-03862-8
Majid, S., Sultana, N., & Abid, G. (2021). The impact of corporate innovation on abnormal stock returns: The moderating role of investor sentiment. Academy of Strategic Management Journal, 20, 1–16.
Manuelli, R. E. (2000). Technological change, the labor market and the stock market (Working Paper 8022). National Bureau of Economic Research. https://doi.org/10.3386/w8022
Mazzucato, M. (2002). The PC industry: New economy or early life-cycle? Review of Economic Dynamics, 5(2), 318–345. https://doi.org/10.1006/redy.2002.0164
Mazzucato, M. (2006). Innovation and stock prices: a review of some recent work. Revue de l’OFCE, (5), 159–179. https://doi.org/10.3917/reof.073.0159
Mazzucato, M., & Tancioni, M. (2008). Innovation and idiosyncratic risk: An industry-and firm-level analysis. Industrial and Corporate Change, 17(4), 779–811. https://doi.org/10.1093/icc/dtn024
Mishkin, F. S. (2016). The economics of money, banking, and financial markets. Pearson.
Mizrach, B. (1996). Learning and conditional heteroscedasticity in asset returns (Departmental Working Papers 199526). Department of Economics, Rutgers University, New Brunswick, NJ, USA.
MSCI. (n.d.-a). MSCI ACWI IMI Disruptive Technology ESG Filtered Index (EUR). Retrieved September 16, 2022a, from https://www.msci.com/documents/10199/f1e29c8e-8600-508d-6e8f-155c7c429a88
MSCI. (n.d.-b). MSCI World Index (USD). Retrieved September 16, 2022b, from https://www.msci.com/documents/10199/178e6643-6ae6-47b9-82be-e1fc565ededb
Navaretti, G., Calzolari, G., & Mansilla-Fernandez, J. (2018). FinTech and banking. Friends or foes?. Friends or Foes. European Economy Banks Regulation, and the Real Sector 2017, 2, 9–30. https://doi.org/10.2139/ssrn.3099337
Neuberger, J. A. (1991). Risk and return in banking: Evidence from bank stock returns. Economic Review-Federal Reserve Bank of San Francisco, (4), 18.
OFX (n.d.). Retrieved August 15, 2022, from www.ofx.com
Pástor, L., & Veronesi, P. (2006). Was there a Nasdaq bubble in the late 1990s? Journal of Financial Economics, 81(1), 61–100. https://doi.org/10.1016/j.jfineco.2005.05.009
Pástor, Ľ., & Veronesi, P. (2009). Technological revolutions and stock prices. American Economic Review, 99(4), 1451–1483. https://doi.org/10.1257/aer.99.4.1451
Peralta-Alva, A. (2007). The information technology revolution and the puzzling trends in Tobin’s average q. International Economic Review, 48(3), 929–951. https://doi.org/10.1111/j.1468-2354.2007.00450.x
Pérez, C. (2003). Technological revolutions and financial capital. Edward Elgar Publishing. https://doi.org/10.4337/9781781005323
Pérez, C. (2012). Financial bubbles, crises and the role of government in unleashing golden ages. UK Open University, FINNOV Milton Keynes.
Phan, D. H. B., Narayan, P. K., Rahman, R. E., & Hutabarat, A. R. (2020). Do financial technology firms influence bank performance?. Pacific-Basin Finance Journal, 62, 101210. https://doi.org/10.1016/j.pacfin.2019.101210
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Quandt, R. E. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American statistical Association, 55(290), 324–330. https://doi.org/10.1080/01621459.1960.10482067
Rodríguez-Ruiz, Ó., Rodríguez-Duarte, A., & Gómez-Martínez, L. (2016). Does a balanced gender ratio improve performance? The case of Spanish banks (1999–2010). Personnel Review, 45(1), 103–120. https://doi.org/10.1108/PR-07-2014-0143
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341–360. https://doi.org/10.1016/0022-0531(76)90046-6
Sawada, M. (2013). How does the stock market value bank diversification? Empirical evidence from Japanese banks. Pacific-Basin Finance Journal, 25, 40–61. https://doi.org/10.1016/j.pacfin.2013.08.001
Schmidt, W. C., & González, A. (2020). Fintech and tokenization: A legislative study in Argentina and Spain about the application of Blockchain in the field of properties. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(1), 51.
Schumpeter, J. A. (1912). Theorie der wirschaftlichen Entwicklung. Duncker & Humblot.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Schwert, G. W. (2002). Stock volatility in the new millennium: how wacky is Nasdaq? Journal of Monetary Economics, 49(1), 3–26. https://doi.org/10.1016/S0304-3932(01)00099-X
Setiawan, R., Cavaliere, L., Koti, K., Ogunmola, G., Jalil, N. A., Chakravarthi, M. K., Rajest, S. S., Regin, R., & Singh, S. (2021). The artificial intelligence and inventory effect on banking industrial performance. Turkish Online Journal of Qualitative Inquiry (TOJQI), 12(6), 8100–8125.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Shiller, R. C. (2000). Irrational exuberance. Philosophy and Public Policy Quarterly, 20(1), 18–23.
Statista. (2021). Number of employees in the banking sector in Spain from 2010 to 2021. Retrieved December 31, 2021, from https://www.statista.com/statistics/765375/employment-in-the-banking-sector-spain/
Statista. (2022). Fintech-Spain. Retrieved September 15, 2022, from https://www.statista.com/outlook/dmo/fintech/spain#transaction-value
Stiroh, K. J. (2006). A portfolio view of banking with interest and noninterest activities. Journal of Money, Credit and Banking, 38(5), 1351–1361. https://doi.org/10.1353/mcb.2006.0075
Stock, J. H. (1988). Estimating continuous-time processes subject to time deformation: An application to postwar US GNP. Journal of the American Statistical Association, 83(401), 77–85. https://doi.org/10.1080/01621459.1988.10478567
Turner, C. M., Startz, R., & Nelson, C. (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1), 3–22. https://doi.org/10.1016/0304-405X(89)90094-9
Tushman, M., & O’Reilly III, C. A. (1996). Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8–29. https://doi.org/10.2307/41165852
Vendrame, V., Guermat, C., & Tucker, J. (2018). A conditional regime switching CAPM. International Review of Financial Analysis, 56, 1–11. https://doi.org/10.1016/j.irfa.2017.12.001
Visconti-Caparrós, J., & Campos-Blázquez, J. (2021). The development of alternate payment methods and their impact on customer behavior: The Bizum case in Spain. Technological Forecasting and Social Change, 175, 121330. https://doi.org/10.1016/j.techfore.2021.121330
Vives, X. (2019). Competition and stability in modern banking: A post-crisis perspective. International Journal of Industrial Organization, 64, 55–69. https://doi.org/10.1016/j.ijindorg.2018.08.011
Ying, W., Jia, S., & Du, W. (2018). Digital enablement of blockchain: Evidence from HNA group. International Journal of Information Management, 39, 1–4. https://doi.org/10.1016/j.ijinfomgt.2017.10.004