Diginomics WORKING PAPER - The impact of fintech startups on financial institutions' performance and default risk - Uni Bremen
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Diginomics WORKING PAPER The impact of fintech startups on financial institutions’ performance and default risk Christian Haddad and Lars Hornuf April 2021 No. 0004
Impressum: Diginomics Working Paper ISSN: 2701-6307 Published by: Universität Bremen, Diginomics Research Group, Max-von-Laue-Straße 1, 28359 Bremen, Germany Editor: Prof. Dr. Lars Hornuf Phone: +49(0)421 218 66820 E-mail: hornuf@uni-bremen.de https://www.uni-bremen.de/graduiertengruppe-diginomics/forschung/diginomics-working- paper
The impact of fintech startups on financial institutions’ performance and default risk Christian Haddad Excelia Business School 102 Rue de Coureilles - Les Minimes | 17024 La Rochelle, France Phone: +33-(0)5 16 19 6394 Email: haddadc@excelia-group.com Lars Hornuf University of Bremen, Faculty of Business Studies and Economics Max-von-Laue-Straße 1 | 28359 Bremen, Germany Phone: +49-421-218-66820 Email: hornuf@uni-bremen.de Max Planck Institute for Innovation and Competition Marstallplatz 1 | 80539 Munich, Germany Email: lars.hornuf@ip.mpg.de CESifo Poschingerstraße 5 | 81679 Munich, Germany 1
The impact of fintech startups on financial institutions’ performance and default risk Abstract We study the impact fintech startups have on the performance and the default risk of traditional financial institutions. We find a positive relationship between fintech startup formations and incumbent institutions’ performance for the period from 2005 to 2018 and a large sample of financial institutions from 87 countries. We further analyze the link between fintech startup formations and the default risk of traditional financial institutions. Fintech startup formations decreases stock return volatility of incumbent institutions and decreases the systemic risk exposure of financial institutions. Our findings indicate that the development of fintech startups should be monitored very closely by legislators and financial supervisory authorities, because fintechs not only have a positive effect on the financial sector’s performance, but can also improve financial stability relative to the status quo. This version: April 24, 2021 JEL Classification: K00, L26, O3 Keywords: Fintech, Bank Performance, Default Risk, Financial Stability 2
1. Introduction The rise of financial technology (fintech) has received considerable attention from academics, practitioners, and regulators. The recent hype about fintech results from the development and deployment of novel technologies such as artificial intelligence (AI), big data, cloud computing, machine learning, blockchain and other technologies that have the potential to revolutionize the financial sector, which was historically considered to be among the most traditional and conservative sectors in the economy. The Financial Stability Board of the Bank for International Settlements defines fintech as “(…) as technology-enabled innovation in financial services that could result in new business models, applications, processes or products with an associated material effect on the provision of financial services” (European Banking Authority, 2017, p. 7). Fintech innovations have emerged in many of the traditional value-adding areas of a universal bank, which include financing, asset management, payment services, and other activities (Dorfleitner et al., 2017). Fintech startups not only challenge traditional financial institutions by providing cheaper, faster, and easier access to financial services, but potentially also foster the transformation and innovation activities of incumbent institutions (An and Rau, 2019; Di, et al; 2020; Milian et al., 2019; Panos and Wilson, 2020). Until today, little is known about the way fintech startups affect the traditional financial sector. One of the core functions of financial institutions is the intermediation of financial resources (Merton, 1992). Yet, the financial crisis of 2007–2008 created a credit crunch (Cowling et al., 2012; Campello et al., 2010; Campello et al., 2011), which resulted in many small and medium size firms being financially constrained (Mc Cahery et al., 2015). Economic output declined sharply and unemployment rates increased worldwide (Bruno et al., 2014; Daly et al., 2012). Moreover, bank customers lost their confidence in many of the traditional financial institution, which in some 3
regions resulted in bank runs like in the case of British Northern Rock in 2008. This fragile environment provided the ground for novel products and services of fintech startups, which started with a clean slate and did not have to overcome a history of failure and excessive risk taking. As a result of consumers’ distrust in banks, marketplace lending has become more prevalent (Saiedi et al., 2020). In particular, banks’ misconduct has been shown to be related to the emergence of the United States (US) online lending market (Bertsch et al., 2020). Moreover, recent research indicate that fintech startups have the potential to better address information asymmetries (Ge et al., 2017; Lin et al., 2013; Xu et al., 2018), because they leverage additional information about borrowers form the Internet, thereby enabling them to receive credit for the first time or in some cases at cheaper rates (Jagtiani and Lemieux, 2019; Iyer et al., 2016; Serrano-Cinca et al., 2015). Services provided by fintech startups also include, for example, algorithm-based investment advice, mobile banking services, instant online and mobile payment infrastructure, innovative risk management systems, and cost-efficient foreign exchange services (Haddad und Hornuf, 2019). The increasing prevalence of fintech startups and the potential pressure they exercise on incumbent firms raise the question how fintech startups affect the performance and risk taking of traditional financial institutions. It has often been argued that fintech startups do not fully comply with financial regulation and engage in regulatory arbitrage using existing legal exemptions (Buchak et al. 2018; Cumming and Schwienbacher, 2018; Hornuf and Schwienbacher, 2017), which might consequently undermine financial stability (Vučinić, 2020; Fung et al, 2020; Li et al., 2020). Because financial stability has a direct impact on economic growth, scholars have investigated financial institutions’ default risk and under which conditions this risk led to subsequent financial turmoil (Acharya and Naqvi, 2012; Beck et al., 2006; Diamond and Rajan, 2012). Fintechs can, for example, affect systemic risk 4
through their increasing interconnectedness with traditional financial institutions and a lax supervision by the authorities (BIS, 2017). By contrast, new business models that are uncorrelated with traditional financial institutions can also reduce systemic risk in the financial industry. To the best of our knowledge, empirical research has not yet studied the systemic risk of traditional financial institutions resulting from fintech startup formations. In this article, we therefore raise the question: Does the emergence of fintech startups affect systemic risk of traditional financial institutions? The empirical literature on financial innovation in general and interaction between traditional financial institutions and fintechs in particular is still scarce. Lerner (2002) and Miller (1986) measure financial innovation by the filing of financial patents and evidence that it has been increasing from the late 1970s onwards. The quality of financial patents and financial innovations was, however, often perceived to be low (Lerner et al. 2016). Research by Scott et al. (2017) shows that the financial industry traditionally spent a large share of their expenses on IT, reaching around one-third of all expenses in the early 1990s. In particular, the financial industry employed computers early on. However, there have only been few financial innovations—e.g., the automated teller machine—that have led to considerable changes in financial institutions and their business models (Merton 1995). Whether fintechs affects incumbents’ ability to innovate and consequently performance is still an open question. A related article to ours focusing on bank–fintech alliances is that of Brandl and Hornuf (2020), who run a simply bank–fintech network analysis for Germany and find that bank–fintech relationships are often product-related. They argue that this form of alliance results from that fact that most fintechs develop an algorithm or software, the value of which can only be determined when the software has been adapted more thoroughly to customer needs. Hornuf et al. (2020) refine 5
these findings by analyzing bank characteristics that are associated with a bank–fintech alliance. They hand-collected data for the largest banks from Canada, France, Germany, and the United Kingdom, and provide detailed evidence that banks are more likely to form alliances with fintechs when they pursue a well-defined digital strategy and/or employ a chief digital officer. Furthermore, they find that banks more often invest in small fintechs but often build product-related collaborations with larger fintechs, which is in line with predictions from incomplete contract theory (Aghion and Bolton 1992; Grossman and Hart 1986). Phan et al. (2020) investigate a sample of 41 Indonesian banks. They find that fintech negatively predicts bank performance and argue that fintechs substitute traditional banks by providing less expensive and more efficient services In this article, we collected data for 8,092 financial institutions and 12,549 fintech startups from 87 countries to study the effect fintech startups have on the performance and default risk of traditional financial institutions. Our results indicate a positive and significant impact of fintech formations on financial institutions’ performance. An increase of fintech startup formations are associated with an increase of incumbent institutions’ performance. Our findings also suggest that fintech formations decrease stock return volatility and financial institutions’ exposure to systemic risk. These findings might be of interest to academics, practitioners, and regulators alike, especially as the fintech sector is steadily growing and becomes more and more integrated with the traditional economy and incumbent financial institutions (Li et al., 2020). The remainder of the article is organized as follows. Section 2 summarizes the literature and introduces our hypotheses. Section 3 describes the data and introduce the variables used in the quantitative analysis. Section 4 presents the descriptive and multivariate results. Section 5 provides a discussion and conclusion of our study. 6
2. Literature and Hypotheses An extensive literature has investigated the performance of financial institutions. During the last decade, research has studied the determinants of financial institutions performance analyzing how firms address corporate governance issues (Aebi et al., 2012; Peni and Vähämaa, 2012; Zheng and Das, 2018), master the diversification of their business activities (Berger et al., 2010; Brahmana et al., 2018; Chen, et al., 2018; Kim et al., 2020), deal with external regulation (Naceur and Omran, 2011, Psillaki and Mamatzakis, 2017), react to monetary policies (Mamatzakis and Bermpei, 2016; Gambacorta and Shin, 2018), deal with the legal and institutional framework (Kalyvas and Mamatzakis, 2017; Bitar and Tarazi, 2019; El Ghoul et al., 2020), generate intellectual capital (Adesina, 2021; Nawaz, 2019; Talavera, et al., 2018), and engage in shadow banking activities (Tan, 2017; Lin et al., 2018). Given the all-embracing and massive development of the fintech sector during the last decade, it appears worthwile to also investigate how fintech startup formations affect financial institutions performance. Consumer theory stipulates that a new product or service—such as those developed by fintech startups—either act as complement or substitute to existing products or services (Aaker and Keller, 1990; Frank, 2009). The products and services that fintech startups offer are more likely to benefit traditional financial institutions if they constitute complements, but threaten incumbent institutions’ performance if they are substitutes (Kaul, 2012). While fintechs have the potential to develop revolutionary business models, collaborations between banks and fintechs have most often been evolutionary in nature (Bhalla, 2019). Thus, existing products or services have been merely enhanced and innovations did rarely replace existing ones (Merton, 1995). For example, invoice trading and factoring always existed but the innovation of fintechs was to scale these services down and offer them for small and medium-sized enterprises (Dorfleitner et al. 2017). 7
Most research concludes that information technology is beneficial for incumbent institutions, because it helps to reduce transaction costs, thereby improving service quality, optimizing business structure, and promoting business transformation and upgrading (Lapavitsas and Dos Santos, 2008; Martin-Olivier and Salas-Fumas, 2008; Shu and Strassmann, 2005). Moreover, there is empirical evidence that many incumbent institutions acknowledge the superiority of fintech startups and have incorporated these start-ups and/or their products and services into their own business model (Hornuf et al. 2020). For these financial institutions, the emergence of fintech startups results in a beneficial partnership rather than a threat (PwC, 2016). For example, the verification of customers' identity through account or video verification supports the customer onboarding process, without cannibalizing existing business from incumbents. Historically, some scholars have claimed that the opposite is true and information technology could bring enormous challenges to commercial banks (Holland et al., 1997), because information technology, globalization, and deregulation allow for new market entrants, disintermediation, innovation and customer changes on a massive scale. Accordingly, fintech startups would take over several key functions of traditional financial institutions (Li et al., 2017). New market entrants benefit from their lack of legacy infrastructure and low levels of organizational complexity, which allows them to be more agile, innovate faster, and be more radical in their approach to innovation (Brandl and Hornuf, 2020). Put differently, fintech startups are likely to absorb the existing business of traditional financial institutions where the latter are operating inefficiently. This substitution effect is also in line with disruptive theory (Christensen, 2013), which claims that new entrants effectively compete with traditional players by providing accessible and cost-effective goods and services to customers. As a result, startups eventually replace incumbents. Fintech have already sparked such a disruptive evolution when offering financial products and services to 8
customers in novel and more cost-effient ways (Ferrari, 2016). The efficiency increase due to fintechs results, for example, from disintermediation that significantly lowers transaction costs for consumers (PwC, 2016; KMPG, 2016). Blockchain technology is one of the most prominent inventions that can accomplish such efficiency increases (Peters and Panayi, 2016; Wood and Buchanen, 2015), for example, by making the clearing and settlement of securities and many other services of the financial sector more cost effective. Moreover, fintechs have developed applications to improve efficiency in financial services across a range of other services, including, for example, mobile and instant payment services, automated asset management, as well as digital information and data management (Villeroy de Galhau, 2016). These innovations take advantage of the traditional financial institutions because many incumbants still rely on outdated IT infrastucture (Laven and Bruggink, 2016; Brandl and Hornuf, 2020) and have difficulties to adopt new financial products and services or in the same quality as fintechs. Furthermore, traditional financial institutions are often less likely to adopt new technologies quickly because of restrictions stemming from the regulatory environment that applies to fully regulated institutions (Hannan and McDowell, 1984). One might also argue that fintechs have no effect on banks performance, because fintechs attract customers that traditional financial institutions do not serve. One of the most prominent examples is probably the implementation of mobile payment and banking services in Kenya (Jack and Suri, 2014; Suri and Jack 2016). Moreover, Jagtiani and Lemieux (2018) find that consumer lending activities on the platform LendingClub have penetrated areas that may be underserved by traditional banks, mostly in highly concentrated markets and areas that have had fewer bank branches. For example, risky startup firms and consumers that lack a credit history often do not obtain access to credit, especially if the desired loan amounts are small and are associated with 9
high transaction costs (Demos, 2016; Hayashi, 2016). Fintech startup often use novel, sometimes algorithm-driven technology to assess borrowers’ creditworthiness at lower costs, which has been an advantage over traditional banks that operate physical branches and employ human loan officers (Hayashi, 2016). Finally, existing financial institutions can acquire fintech startup that are perceived as “too” innovative and cost effective. In this way, incumbents institutions gain access to new technology and can adapt it to their own specific needs. For example, Capital One, one of the largest banks in the US in terms of total assets and market capitalization, acquired the fintech startup Level Money in 2015. Level Money was a San Francisco-based digital banking technology firm, which provided customers with a simple overview of their finances. With more than 800,000 downloads, the Level Money app connects to 250 US financial institutions (Li et al., 2017). After its acquisition by Capital One, Level Money became part of Capital One’s Digital Innovation Team, which enables Capital One to strengthen its capabilities in digital banking technologies.1 Because of fintech startups, financial institutions might not only acquire new retail customers, but could also extend their existing business through fintech corporate clients. Some of the more traditional financial institutions have realized the potential that stems from the emergence of fintech statups and have specialized in what is called “banking as a service” (BaaS) or “banking as a platform” (BaaP). In the BaaS business models financial institutions operate a licensed and regulated banking back-end and offer BaaS middleware to fintech startups that cannot or do not want to afford the costs of being fully regulated themselves. In other cases, financial institutions might offer regulatory advice or technology to fintechs, which have not acquired the respective 1 See: https://www.forbes.com/sites/peterhigh/2016/12/12/how-capital-one-became-a-leading-digital- bank/?sh=581083b615ee 10
knowledge or find it not cost-efficient to do so. In either case, the devision of value creation between fintechs and banks might ultimately benefit both. Overall, we therefore conjecture that financial institutions will not go down without a fight or without any attempt to improve their business models after the emergence of fintech startups. We therefore hypothesize the following: H1: Fintech startup formations are positively related to traditional financial institutions’ performance. Extensive theoretical and empirical research has investigated the determinants of the default risk of financial institutions, because financial stability is of utmost important for the economy and financial supervisory authorities. Finance scholars have studied the default risk of financial institutions mostly from two perspectives. The first stream of literature focuses on financial institutions’ characteristics including their size (Afonso et al., 2015; Laeven and Levine 2009; Saunders et al., 1990), liquidity (Diamond and Dybvid 2000; Diamond and Rajan 2012), diversification of funding activities (Demirguc-Kunt and Huizinga 2009), bank capital as a share of risk-weighted credit exposures (Furlong and Keely, 1989), and corporate governance (Agoraki, et al., 2010; Chen et al; 2017). The second stream of literature focuses on the determinants of risk- taking that results from external sources, such the degree of bank competition (Beck et al., 2006; Beck et al., 2013, Boyd and De Nicolo 2005), monetary policy (Borio and Zhu 2012; Chen et al., 2017), deposit insurance schemes (Angkinand and Wihlborg, 2010; Demirguc-Kunt and Detragiache, 2002), external regulation (Barth et al., 2004; Klomp and Haan 2012), such as creditor and minority shareholder protection (Houston et al., 2010; La Porta et al., 2000), and political institutions (Ashraf 2017; Chen et al., 2015; Wang and Sui 2019). 11
In this study, we investigate the default risk of financial institutions following the emergence of fintech startups. Which impact fintechs have on the default risk of financial institutions is per se not clear. Several factors could lead to an increase in the default risk in the financial industry. Fintech startup often provide similar financial products and services as incumbents (Dorfleitner et al., 2017; Yao et al., 2018; Kommel et al., 2019) and their business models are in some cases inherently linked to traditional financial institutions. For example, in many jurisdictions commercial loans can only be extended by institutions that possess a banking license. Marketplace lending platforms, for example, often do not possess a banking license and a bank in the background ultimately extends the loan between the borrower and the lenders (Cumming and Hornuf, 2020). Thus, banks are often an integral part of fintech business models. However, startups generally fail more often than established firms (Cressy, 2006; Evans, 1987; Dunne et al., 1989), which could increase the risk of those that collaborate with them. Buchak et al. (2018) provide empirical evidence for the US that the shadow bank market share in residential mortgage origination almost doubled from 2007 to 2015. The increase in shadow banks came with a dramatic growth in online fintech lenders, technological advantages, and regulatory differences among US counties. In other cases, banks and fintechs cooperate closely because the cooperation appears beneficial to all involved parties (Romānova and Kudinska, 2017, Hornuf et al., 2020). As a result of these interconnections, the risks resulting from fintech formations could spill over to individual financial institutions (European Banking Authority, 2017; He et al., 2017). Moreover, banks themselves are actively involved and participate in the development of fintech technology (Acar and Citak, 2019), which might result in increasing legal and technical risks, such 12
as data security risk 2 , data privacy risk, and transaction risk, which could increase financial institutions default risk (Cost of Data Breach Report, 2020; Yadron et al., 2014). On the other hand, fintechs could also lower the default risk of financial institutions. The digitalization of lending activities very likely lowers transaction costs and improves the efficiency of the loan origination and maintenance processes (BIS, 2017). This could reduce the costs of capital for borrowers and improve the risk-adjusted returns for fintechs and traditional financial institutions. Moreover, because fintech startups employ modern technology and use big data, they can at least theoretically better address information asymmetries (Ge et al., 2017; Lin et al., 2013; Xu et al., 2018). Ecosystems that promote the sharing of data can further enable the development of novel products and services. The European Banking Authority (2019) expects a positive effect of application programming interfaces (APIs), which allow for a more direct exchange of data, leading to increased competitive pressure, and an improved customer experience. We therefore hypothesize the following: H2a: Fintech startup formations decrease financial institutions’ default risk. Traditional financial institutions invest in fintech startups, which allows them to better access their knowledge (Lee and Shin, 2018, Hornuf et al., 2020). As fintechs grow larger and become more integrated and interconnected with the financial sector, they may also impact systemic risk. A prominent recent example is the German payment acquirer Wirecard, which in 2020 collapsed and subsequently filed for default because of a series of fraudulent accounting activities and inflated profits. Although Wirecard has been part of the Prime Standard, the market segment of the Frankfurt Stock Exchange with the highest transparency standards, it was itself considered to be a 2 See for example the massive data breach at JP Morgan, https://www.theguardian.com/business/2014/oct/02/jp- morgan-76m-households-affected-data-breach. 13
fintech company. Wirecard not only collaborated with other fintech startups such as Holvi, Lendico, Number 26 (now N26), Rate Pay, and Zencap3, but also engaged in alliances with large financial conglomerates such as insurance company Allianz.4 To offer lending services, Wirecard operated the subsidiary Wirecard Bank, which possessed a banking license and was fully regulated and monitored by the Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufsicht – BaFin). Wirecard was not classified as a financial holding company and only the subsidiary had been classified as a financial company by BaFin, which implied that the holding company’s activities were supervised only loosely and accounting fraud remained undetected (Navaretti et al. 2020). Although bank–fintech collaborations have been rapidly growing in many economies, the related supervision has developed only slowly, as the Wirecard case evidences. After the collapse of Wirecard, the German legislator proposed a draft law to strengthen financial market integrity (Finanzmarktintegritätsstärkungsgesetz – FISG) targeting a wide range of existing financial market regulation. While the Wirecard accounting scandal did not affect the German or European financial system as such, it as raised questions with regard to how financial subsidiaries of tech companies can seamlessly continue operating after a holding company files for default and how business partners can seamlessly switch their operations to another institution. Without doubt, as fintechs become more mature and interconnected, concerns about market risk and systematic risk rise. However, it should be mentioned that the collapse of Wirecard did by far not result in a financial turmoil comparable to the collapse of Lehman Brothers in 2008. 3See http://ir.wirecard.de/download/companies/wirecard/Presentations/WDIInvestorPresentationQ22015_01.pdf 4 See https://www.reuters.com/article/us-wirecard-accounts-allianz/allianz-to-end-wirecard-cooperation-amid- accounting-scandal-idUKKBN2425KO 14
Moreover, having access to alternative financial products such as marketplace or mobile loans, which are to a lesser degree correlated with other loans and institutions, can reduce systemic risk in the financial industry (BIS, 2017). A greater share of fintech credit through marketplace loans or mobile loans could thus mitigate problems of too-big-to-fail or too-systemic-to-fail institutions. Marketplace lending platforms operated by fintechs have little direct financial exposures to each other, a systemic benefit that might only disappear if fintechs become more interconnected over time (BIS, 2017). Furthermore, the use of biometric information and other enhanced data security measures that have been implemented by fintechs early on are considered to improve data security, potentially lowering the risk of cyber-attacks. Finally, systemic risk could also be reduced through enhanced market transparency, which could result from the more extensive use of cloud computing and decentralization (European Banking Authority, 2017). We therefore hypothesize the following: H2b: The exposure of traditional financial institutions to systemic risk is negatively related to fintech startup formations 3. Data and Method 3.1 Dependent variables To investigate whether fintech startup formations affect incumbent institutions performance and default risk, we consider eight dependent variables. For most of these variables, we need daily stock returns as a basis. For financial institutions from the US, we obtained daily stock returns from the Center for Research in Security Prices (CRSP) US Stock Databases and for all other countries we used the CompuStat World Database. Because fintechs might not only affect the business 15
models of banks but also other financial institutions, we extract 8,092 financial institutions5 from 87 countries with Standard Industrial Classification codes starting with 60 to 67 over the period from 2005 to 2018 (see Appendix Table A1 for an overview). For each listed financial institution, we collect adjusted prices or adjustment factors, the number of shares outstanding, the location of the headquarter, and calculated annual returns.6 Adjusted prices and number of shares outstanding enable us to compute market valuation. We use returns and market valuation of financial institutions to compute value-weighted market return indices of the financial sector of each country. Because we need yearly financial institution level variables to assess the impact of fintech formation on financial institutions performance, we collapsed all daily firm data to yearly data. To test hypothesis 1 and in line with Phan et al. (2020), we calculate the net interest margin, return on assets, return on equity, and Tobin’s Q as measures of financial institutions performance. Tobin’s Q traditionally measures the sum of the market value of equity and the book value of liabilities divided by the book value of total assets. We compute financial institutions’ performance also based on a market measure. The choice to study annual stock returns is motivated by the fact that stock prices better reflect current information and expectations about firms’ future profitability and growth (Anilowski et al., 2007). To test hypothesis 2a, we use accounting and market measures of risk in our analysis. The first measure of financial institution default risk is the Z-score of each financial institution, which equals the return on assets plus the capital-asset ratio divided by the standard deviation of the return on assets. The Z-score thus measures the number of standard deviations below the mean by which 5 Because of data limitations in our explanatory variables and given that we use a lag of one year, our sample reduces to the period from 2006 to 2018 covering only 6,406 financial institutions. 6 For the CompuStat World Database, we compute returns by considering adjustment factors according to the guidelines from the CompuStat manuals. 16
profits would have to fall to deplete the financial institution’s equity capital completely (Boyd et al., 2006). The measure has a long tradition in the finance literature (Roy, 1952) and is still used in empirical research to capture a financial institution’s distance from default (Bhagat et al., 2015; Pathan, 2009; Houston et al., 2010, Jin et al., 2013; Laeven and Levine, 2009). A higher value of the Z-score indicates a lower default risk and greater stability of the respective financial institution. Because the Z-score is often highly skewed, we follow Laeven and Levine (2009) and use the natural logarithm of the Z-score in our estimations. Our second measure of financial institution default risk is the volatility of stock returns, which has been widely used in prior research (Brown et al., 2015; Pathan, 2009; Sun and Liu, 2014). It captures the market’s perception of the risk inherent in banks’ assets, liabilities, and off-balance sheet positions (Pathan, 2009). To test hypothesis 2b, we consider the marginal expected shortfall, which captures a financial institutions’ exposure to systemic risk. It measures the average of individual stock returns on a subset of sample days that corresponds with the 5 percent worst days of the equally weighted market index. 3.2 Explanatory variables The data source for our explanatory variable of interest is the CrunchBase database, which contains detailed information on fintech startup formations and their financing. The database is assembled by more than 200,000 company contributors, 2,000 venture partners, and millions of web data points7 and has recently been used in scholarly articles (Bernstein et al., 2017; Cumming et al., 2016, Haddad and Hornuf, 2019). We retrieved the data that we use in our analysis on July 9, 2019. Because CrunchBase might collect some of the information with a time lag, the observation period 7 See https://about.crunchbase.com. 17
in our sample ends on December 31, 2018. Overall, we identified 12,549 fintech startups from 87 countries for our relevant sample period. To account for financial institution and cross-country heterogeneity, we consider several variables, which are frequently used as controls in the bank performance literature (Agoraki et al., 2011; Tabak et al., 2012, Phan et al., 2020). Following Pathan and Faff (2013), Shaban and James (2018), Dietrich and Wanzenried (2014) and Berger et al., 2017,we control for total assets as a measure for average firm size, the capital ratio, the cost income ratio, the interest income margin, and the book to market ratio. All variables were retrieved from the CRSP and CompuStat databases. To address country-time specific heterogeneity, we consider several macroeconomic indicators. We control for the cross domestic product (GDP), because it might influence bank performance through the business cycle. When the economy faces a recession or an economic crisis, the quality of borrowers deteriorates, which in turn worsens banks’ loan portfolio and affects bank performance. On the loan demand side, borrowers are less willing to invest in long term projects in times of crisis and prefer to cut their spending. Not surprisingly, the empirical literature shows that economic growth also stimulates the financial system (Athanasoglou et al., 2008; Albertazzi and Gambacorta, 2009). We also account for inflation as a measure of financial institutions performance, because earlier research shows a positive relationship between inflation and profits (Kasman et al. 2010; Trujillo- Ponce, 2013). However, if inflation is anticipated and financial institutions fail to adjust their interest rate, costs can increase faster than profits, which negatively affects bank performance. Therefore, the effect of inflation on bank performance is ambiguous. To control for the extent to which countries’ political decisions affects bank performance, we include the variable size of government, which combines five components: Government consumption, transfers and subsidies, government entreprises and investment, top marginal tax rate 18
and state ownership of assets. The variable ranges from 0 to 10, with higher values indicating that countries rely more on personal choice and markets rather than government budgets and political decision-making. To control for differences in the efficiency of legal protection and enforcement of laws across economies, we consider the variable legal protection that was curated by the Fraser Institute database. It entails several legal system components, including rule of law, security of property rights, an independent and unbiased judiciary, and impartial and effective enforcement of the law. These components are indicators of how effectively the protective functions of the legal system are performed. The variable ranges from 0 to 10, with higher values indicating better government efficiency in terms of legal protection. Finally, we control for the impact the concentration of banks has on bank performance. Empirical research still shows ambiguous results for this variable. In the European context, Delis and Tsionas (2009) find that firms with market power tend to operate inefficiently, because managers enjoy monopoly profits. Another study by Maudos and Fernandez de Guevara (2007) finds no evidence for a significant relation between firm concentration and performance. The measure was extracted from the World Bank database. The variable is defined as the sum of the market share in terms of total assets of the three largest banks. Definitions of all variables and their sources are reported in the Appendix Table A2. 3.3 Model specifications Our empirical approach is motivated by recent research estimating determinants of bank performance (Köster and Pelster, 2017; Shaban and James, 2018, Phan et al., 2020). To test our hypotheses, we use a two-step generalized method of moments (GMM) system dynamic panel estimator (Arellano and Bover, 1995; Blundell and Bond, 1998). This approach allows us to treat 19
the explanatory variables as endogenous respectively using their past values as instruments (Wintoki et al., 2012). First differences help to eliminate time-invariant unobserved heterogeneity and thus omitted variable bias. Regarding the lagged explanatory variables of the dependent variable, it is important to determine the correct number of lags to sufficiently capture the past. We argue that older lags are more likely to be exogenous with respect to the residuals of the present and should therefore be valid instruments. We follow Wintoki et al. (2012) and include two lags to capture the persistence of performance of financial institutions. Our baseline regression model is: , = + 1 , −1 + 2 , −1 + 3 , −2 + 4 , + 5 , + 6 , + 7 , + 8 , + 9 , + 10 , + 11 , + 12 , + 13 , + , whereby PER represents one of five different dependent variables: net interest margin, return on assets, return on equity, Tobin’s Q, and annual stock return. Analogously, we estimate a two-step GMM system dynamic panel model to test hypothesis 2a and 2b: , = + 1 , −1 + 2 , −1 + 3 , −2 + 4 , + 5 , + 6 , + 7 , + 8 , + 9 , + 10 , + 11 , + 12 , + 13 , + , whereby RISK represents one of three different dependent variables: z-Score, stock return volatility, and marginal expected shortfall. We use year dummies in all models to account for business cycle effects. In addition, we use heteroscedasticity-robust standard errors clustered at the financial institution level. 20
4. Results 4.1. Benchmark model Table 1 reports the baseline regression. Columns represent the five dependent variables measuring performance: net interest margin, return on assets, return on equity, Tobin’s Q, and annual stock return. We find sector specific and macro-level variables to have an economically meaningful and statistically significant impact on financial institutions’ performance. The control variables that are significant performance predictors in three models are lagged cost income ratio and market to book ratio. Inflation and bank concentration are statistically significant in two of five models, the interest income margin is significant in one of the models. --- Table 1 About Here --- 4.2. Lag effect of fintech startup formations on bank performance and risk taking In Table 2, we examine whether fintech formations positively affect the performance of financial institutions. In four out of five models, the coefficient of fintech is statistically different from zero. The number of fintech startup formations in a country positively predicts net interest margin, return on assets, the return on equity, and annual stock returns of traditional financial institutions. The coefficients imply that 10 extra fintech firms entering the market in a given year increase financial institutions net interest margin by 1.7%, return on assets by 34.6%, return on equity by 8.1% and annual stock returns by 78.9% of the mean value, respectively. For Indonesia, Phan et al. (2020) find net interest margin to change by 5.3%, return on assets by 93.2%, and return on equity 27.3% for 10 extra fintech startups entering the market. --- Table 2 About Here --- 21
Next, we test whether the effect of fintech startup formations on financial institutions’ performance differs for large and small institutions. Recent research suggests that financial characteristics of institutions are important predictors of their performance (Dietrich and Wanzenried, 2011; Köster and Pelster, 2017; Talavera, Yin and Zhang, 2018). We consider the market value of a financial institutions as proxy to differentiate large, universal financial institutions from small, specialized financial institution. We expect large, universal financial institution to adapt their business models slower than smaller, specialized financial institution, which presumably have already specialized in business models such as BaaP and BaaS. On the other hand, large financial institutions often have deeper pockets and can more forcefully pursue change through acquisitions and inhouse experimentation. Our results show a positive and significant association between the formation of fintech startups and large financial institutions performance. We find that for financial institutions with above median market value, fintech startup formations have a positive and robust effect on three of five measures for financial institutions’ performance—return on assets, return on equity, and annual stock return. For financial institutions with below median market value, we do not observe any significant association between fintech formations and financial institutions performance. Large, universal institutions might benefit from alliances with fintechs, for example, through product related corporations or partial acquisitions of fintechs, which help them to obtain specialized knowledge and improve their performance (Hornuf et al. 2020). This result does not necessary imply that smaller financial institutions are reluctant to change. In fact, smaller, more specialized financial institutions might already possess a more modern IT infrastructure and hence benefit only at the margin from fintech startups. --- Table 3 About Here --- 22
Recent research posits a non-linear relationship between fintech formations and the behaviors of financial institutions over time (Wang, Liu, and Luo; 2020). The relationship is explained by the initial threat that fintech startups posed to traditional financial institutions, especially during and shortly after the 2007-2008 financial crisis, which later turned towards more cooperative business relations. We suspect fintechs to have exercised more pressure on incumbent institutions during the first wave of fintech startups formations, while later this pressure has relaxed as traditional financial institutions acquired fintech and adapted their business models. Acquisitions and alliances may, however, not unfold their full value, if incumbent institutions simply eliminate an unpopular competitor from the market. A recent event study shows that at least in the short run announcement of bank–fintech alliances are perceived negative by the market (Hornuf et al. 2020). In a next step, we therefore divide our sample into two subsamples and test whether the development of fintech startups has a differential impact on financial institutions’ performance for the periods from 2005 to 2011 and from 2012 to 2018. The results in Table 4 show that fintech positively affect bank performance during the period from 2005 to 2011 for return on assets and return on equity. During the later period from 2012 to 2018 the impact of fintech startup formations on financial institutions’ performance is only positive and significant at conventional levels for Tobin’s Q. For net interest margin, return on assets, and return on equity we still find a positive, but only weakly significant association between fintech startup formations and financial institutions performance. Thus, it appears that the pressure resulting from fintech startups following the financial crisis vanished over time potentially as a result of more cooperative business models, although the positive association between performance and fintech formations has not entirely disappeared in recent years. --- Table 4 About Here --- 23
In Table 5, we test whether fintech startup formations predict the default risk of financial institutions. The columns report estimates for our dependent variables of interest—z-Score, stock return volatility, and marginal expected shortfall. Fintech startup formations have a negative and statistically weakly significant effect on the accounting measure Z-score. If this result stems from a lack of statistical power, it would indicate that fintech startups are associated with a higher probability of default of financial institutions. However, the result we have obtained for the Z-score are not only weakly significant, they should also be interpreted with cautious. First, the Z-score computation is based on accounting data, which is only as good as the underlying accounting and auditing framework. The case of Wirecard constitutes a recent example that accounting measures might not reflect the actual situation of a financial institution. Moreover, if financial institutions are able to smooth out the reported data, the Z-score provides an overly positive assessment of the financial institution’s stability. Using a market measure for our dependent variable, we find that the development of the fintech sector has decreased financial institutions’ stock return volatility. Finally, the stock return volatility looks at each financial institution separately, neglecting that a default of one financial institution may cause losses to other financial institution in the system. During the 2007-2008 financial crises, it became evident that many financial institutions are interconnected and market contagion occurs as a domino effect. Using the marginal expected shortfall as our dependent variable, we capture the effect that fintech formations have on the financial institutions’ exposure to systemic risk. We find that the development of fintech startups decreases incumbents’ exposure to systemic risk. The spread of fintechs might not only result in more competition and better performance of traditional financial institutions, it increasingly diversifies the use and execution of financial services over 24
different market players. In this sense, the rise of fintech might to some degree counteract the too- systemic-to-fail problematic. --- Table 5 About Here --- To test the robustness of our results, we calculate the ratio of total fintech founded per year and country and divide them by the total number of startups founded during that year in the respective economy as an alternative measure of fintech startup formations. The data for startup formations was obtained from the Crunchbase database. Table 6 reports that the results are very similar to previous findings and evidences that fintech formations positively predict bank performance. With regard to default risk, we also find that fintech startup formations negatively affect financial institutions’ default risk, which is illustrated by the decrease in stock return volatility. --- Table 6 About Here --- 5. Discussion and Conclusion The article investigates whether fintech startup formations affect financial institution’ performance and default risk. We evidence that fintech startup formations improve financial institution’ performance in terms of accounting and market measures. These findings are in line with previous research (Vives, 2019), which posits that banks rethink and reshape their business model when confronted with competitive pressure. One potential way how financial institutions accomplish performance improvements when confronted with fintechs is through cooperation with and integration of the new players in their organization (Hornuf et al. 2020). Moreover, we use the 25
marginal expected shortfall as a measure of systemic risk and find that financial institutions exposure to systemic risk decrease with more fintech startups entering the market. The latter finding sheds light on how financial institutions benefit from technology spillovers when confronted with novel technological solutions that were developed by fintechs (Blalock and Gertler, 2008; Newman et al., 2015). Technological improvements and new business models improve the efficiency of risk management and consequently reduce default risk. To provide an example, the Industrial and Commercial Bank of China intercepted approximately 900,000 risky transactions by employing digital technology in 2018, which significantly reduced ICBC’s credit risk (Cheng and Qu, 2020). Moreover, the blockchain technology and cloud computing cater decentralized, real-time transactions, which could improve financial institutions risk management and may reduce their contribution to systemic risk. Our analysis has some clear limitations. While we find evidence that fintech startups have a positive effect on financial institutions’ performance, the same might not hold for large technology companies such as Alibaba, Alphabet (Google), Amazon, Apple, Facebook, Microsoft, and Tencent, which have started to implement and offer financial services to their customers as well. In fact, these companies are not only interconnected with large parts of the real economy, they are also by themself systemically relevant. For example, Amazon operates their own payment service (Amazon Pay), lending business (Amazon Lending), and cloud computing business (Amazon Web Services). Although these services are all operated by formally independent companies, no one can so far foresee how a default of one of these companies will affect the other services provided by Amazon. Thus, fintech services provided by large technology companies might negatively impact financial institutions performance, not least due to their sheer size and market power, and could also negatively affect systemic risk. 26
When comparing the period from 2005 to 2011 and from 2012 to 2018, we find that the pressure resulting from fintech startups on financial institutions performance has somewhat vanished, although the positive association has not entirely disappeared yet. Future research might thus investigate whether the relationship has completely disappeared by now and which impact large technology companies have on financial institutions performance and default risk. Moreover, while we have investigated the overall effect of fintech startup formations on the performance and default risk of incumbent financial institutions, information systems and finance scholars might disentangle in more detail through which channel fintechs impact the performance and default risk of incumbents. Such research most likely has to be based on case studies and/or experimental interventions on individual branches of financial institutions. 27
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