Abstract:

The financial and banking sector has experienced a great revolution in recent years with the appearance of FinTech, applying the concept of digital transformation to the financial services industry. The focus of this research paper is to analyze the stochastic properties of the banking revolution and financial technologies (FinTech) before and after COVID-19 episode. This study adds a new dimension to the literature because it is the first research paper that uses advances methodologies based on fractional integration and artificial intelligence to understand the behavior of the FinTech industry. The results exhibit a high degree of persistence in both cases. However, it is observed in the behavior of the subsamples that before the pandemic, the so-called "FinTech revolution" period, we find a behavior of mean reversion in the event of an external shock. After the pandemic episode, the series behaves like non mean reversion, where shocks are expected to be permanent, causing a change in trend. This last result is in line with the one obtained using machine learning techniques predicting the behavior of the new trend in the next 365 days.

Keywords:

FinTech; banking revolution; mean reversion; persistence; fractional integration; machine learning.

JEL Classification:

C22; C45; G20; O30.

How to Cite:

Berta Marcos Ceron and Manuel Monge. Financial Technologies (Fintech) Revolution and Covid-19. Time Trends and Persistence . [ref]: vol.21.2023. available at: https://refpress.org/ref-vol21-a93/