From Code to Capital: A Study of How Emerging Technologies Shape Stock Markets

Abstract

[eng] Recent white papers have described how emerging technologies will shape the future (World Economic Forum, 2017) and how institutions are responding to the increasingly salient trend (EU Commission, 2021). From an investment perspective, emerging technology offers major potential for growth, but also entails considerable uncertainty and risk, as these technologies are just beginning to exist, grow and develop (Cambridge, 2023). By nature, these can be associated to uncertainty since the final stages of their development, market acceptation and impact, indeed, are uncertain.

Mishkin (2016) posits that stock prices reflect optimal expected forecasts based on available information. Expectations regarding future profits from emerging technology are thus embedded in current stock prices. Evidence shows that disruptive technology firms exhibit unjustifiably high stock returns and volatility (Pástor and Veronesi, 2006; Gharbi, Sahut, and Teulon, 2014; Schwert, 2002), suggesting bubble-like patterns driven by market irrationality (Shiller, 2000; Pérez, 2003).

This thesis delves into the intersection of technology and finance, focusing on how emerging technologies shape the landscape of financial assets risks and returns dynamics and to encourage investors and analysts to use emerging technologies strategically to engage with market return and volatility.

This thesis is a self-contained scientific document composed of six chapters, and each one entails certain particularities regarding the research approach and methodology.

The thesis begins with an Introduction.

Chapter 2, entitled “The Rubik’s Cube of Emerging Technologies and Stock Volatility” presents a systematic review of the literature on the constellation of emerging technologies and asset return volatility, documenting several potential explanations for how emerging technologies drive stock volatility. Several specific features of emerging technologies are identified across the literature review, which are described as diffusive, persistent, heterogeneous, and momentum-oriented. The main conclusion of this chapter is that emerging technologies systemically contributes to an increased stock return volatility driven by their inherent uncertain nature, the greater complexity to calculate fundamental values, over-enthusiastic and novice investors, and their idiosyncratic properties. The review of recent empirical evidence contributes to the technological innovation, economic and finance literature by providing a state of the art of the relationship between emerging technologies and asset returns and asset return volatility.

Chapter 3, entitled “Regime Switching in High-Tech ETFs: Idiosyncratic Volatility and Return” shows how investors’ expectations regarding emerging technologies are reflected across Exchange Traded Funds (ETFs), as a particular type of financial security. A Markov regime-switching (MRS) modeling involving time series analysis has been used for this study. The main finding contributes to the idiosyncratic risk literature by showcasing a significant relationship between idiosyncratic risk and return and suggest that idiosyncratic volatility matters in high-tech ETF pricing. The evidence demonstrate that idiosyncratic risk is priced negatively or positively depending on volatility regimes. The main contribution is toward diversification strategy for investments in the high-tech sector, idiosyncratic risk can play an important role in terms of managing idiosyncratic volatility and return.

Chapter 4, entitled “Impact of emerging technologies in banking and finance in Europe: A volatility spillover and contagion approach”, investigates the time series properties of the correlations, volatility cluster, spillover, and persistence for asset returns and emerging technology-related assets across the Spanish Banking sector, the Spanish Market, and the finance industry in the European Union. The main findings show that developments in emerging technology are a relevant factor for capturing the level of risk and volatility and that emerging technology-related assets are highly integrated. The findings shed light on the importance of considering sector, industry, and market specific features that need to be contemplated and can result in heterogeneous insights into the relationship between emerging technology and assets risk and returns. The contribution of this study is a more in-depth analysis of opportunities and challenges related to FinTech and the banking industry in the past, present, and future.

Chapter 5, “The impact of disruptive technologies on Spanish banking under different volatility regimes”, contributes to the innovation and finance literature and explores whether and how disruptive technology impacts banking stock returns under high volatility and low volatility regimes. A classical CAPM was adapted into a two-factor model with heteroscedastic Markov switching regimes. Using the Spanish banking sector the results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent. Additionally, we found that intensity depends also on the existing market circumstances, having a more significant influence under unfavorable market conditions and less influence under stable ones. 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.

Chapter 6, “Banking FinTech and stock market volatility? The BIZUM case”, reviews whether and how the adoption of FinTech by incumbent banks affects their stock price volatility. Using the case of BIZUM, a FinTech solution, the effect on Spanish incumbent banks has been analyzed by applying a GARCH-M GED methodology. The results indicates that investors are not indifferent to the adoption of a disruptive technology, driving to a reduction in the incumbent banks stock volatility. One might suspect investors to have anchored the benefits and competitive advantages that FinTech might offer for the incumbents, being in line with the theoretical argument proposed by Jun and Yeo (2016) that FinTech will complement incumbent banks and lead to positive impact.

To sum up, this thesis provides a number of contributions to the fields of financial economics and innovation. First, it investigates and presents evidence on a significant relationship between emerging technologies and stock market dynamics. Second, it provides evidence that the impact of emerging technologies on the stock market varies depending on the stock market conditions. Third, it shows that the intensity of the impact also depends on the market circumstances reflected through volatility regimes. Moreover, and fourth, we found that emerging technologies are providing new market opportunities, which entail novel volatility patterns. However, the results also highlight that the impact of emerging technology on market dynamics varies across security types, industries, sectors, and country levels. Therefore, a case-by-case approach is paramount. Further studies are necessary to formulate a generalized statement.

In summary, emerging technologies reshape stock markets, impacting volatility levels. Researchers and practitioners must navigate this dynamic landscape to make informed decisions.