As technology evolves, products, critical success factors, and industry characteristics change (Afuah and Utterback, 1997). This phenomenon has greatly affected the taxi and hotel industry, in which Uber and Airbnb have largely replaced the traditional services by offering decentralized online peer-to-peer platforms (Cannon and Summers, 2014). Since FinTech start-ups follow the same customer-centric approach by offering alternatives such as peer-to-peer lending (Lin et al., 2015; Wang et al., 2015a, b; Wang et al., 2015a, b; Yan et al., 2015), one might expect a shift in the banking industry as well. Researchers apply consumer theory to examine whether digital banking start-ups indeed negatively affect traditional retail banks. This theory states that a new service will act as a complement when utilized jointly with an old service and will serve as a substitute if it can replace the old service by satisfying the same needs (Aaker and Keller, 1990; Frank, 2009). Thus, the services digital banking start-ups offer would benefit the traditional retail banks in the former case and affect the incumbents’ performance in the latter case (Kaul, 2012). It might also be possible to observe no effect when examining stock returns, which could indicate that the complementary and substitution effects offset each other. Other explanations are that the start-ups are simply too small, our measure does not capture FinTech’s value well, or FinTech serves a new channel.
Substitution effect and disruptive innovation
FinTech start-ups offering successful substitutes for traditional services might disrupt the retail banking industry. Christensen (1997) coined the term “disruptive innovation,” which involves entrants that successfully target overlooked segments. In the case of FinTech, these could be ‘unbanked’ and ‘underbanked’ segments such as small businesses and the small dollar-loan market that do not generate enough profit for the labor intensive traditional banking industry. According to this theory, start-ups eventually displace the incumbents. FinTech companies could potentially spark such a disruptive evolution due to their new alternatives that enhance the efficiency and quality of services (Ferrari, 2016).
Efficiency increases are mainly due to loan personalization and the disintermediation of processes by eliminating middlemen, which significantly lowers transaction costs for consumers (PwC, 2016; KPMG, 2016). New technologies such as the “blockchain” also enhance efficiency (Peters and Panayi, 2015; Wood and Buchanen, 2015). These innovations will benefit FinTech firms more as banks often rely on decades-old IT infrastructure (Laven and Bruggink, 2016). Moreover, banks are usually less likely to adopt new technologies quickly due to the regulatory environment (Hannan and McDowell, 1984).
The quality of financial services also increases as the entrants have alternative methods to assess risk beyond the single credit score that banks examine. For instance, companies such as Kabbage and OnDeck get their information from social-media reviews and companies’ usage of logistics firms when assessing small business performance (The Economist, 2015a, b). Furthermore, Avant employs advanced machine-learning algorithms to underwrite consumers whose credit scores were affected by the financial crisis. FinTech also enables a more diverse, and thus more stable, credit landscape. This is mainly because the operations of internet-based firms are less geographically concentrated than those of incumbent banks are (The Economist, 2015a, b). Entrants are therefore able to attract the smaller risky enterprises that traditional retail banks would normally reject (Dunkley, 2015). Lastly, newcomers avoid the two major risks related to banking: mismatched maturities and leverage. In contrast to banks, FinTech firms simply match borrowers and savers directly instead of converting short-term deposits into long-term assets (The Economist, 2015a, b).
A concrete example of a FinTech company with a potential substitution effect is Lending Club. It is the world’s largest online marketplace connecting borrowers and investors, transforming the banking system since its establishment to make credit more affordable and investing more rewarding. Lending Club’s similarity to traditional banks is obvious: they both provide personal loans, business loans, and other financial services to their customers. However, besides it low-cost operation, Lending Club has a different business model than traditional banks do. This company, as most other FinTech firms, focuses on its lending platform to connect borrowers and investors, meaning that Lending Club does not absorb the risk that borrowers will not pay back their loans. Traditional banks need to take on these risks because they also convert short maturity savings into long maturity loans. Furthermore, Lending Club’s major source of income is transaction fees rather than interest income, which constitutes the key income of traditional banks. Lastly, due to its different business model, Lending Club is less exposed to regulations compared to traditional banks.
Complementary effect and collaborations
On the other hand, one might argue that FinTech firms will complement the retail banking services. A plausible reason is that many incumbent banks have seen the significance of FinTech and tried to incorporate these start-ups or technologies into their businesses, either through joint partnerships, service outsourcing, venture capital funding, or acquisitions. For these banks, FinTechs seem to benefit them more than disrupt them (PwC, 2016). Moreover, collaborations between banks and FinTech start-ups also benefit small players. By cooperating with banks, FinTechs may get access to global payment systems and the banks’ own customer base. This lowers the barriers of entry for FinTech firms to the financial sector and enables them to gain more trust from their customers (Juengerkes, 2016).
A real-world example from the FinTech industry with potential complementary effects is Wealthfront. It is considered one of the biggest investment management platforms and online financial advisors. Founded in 2011, Wealthfront raised $130 million, including a $64 million round closed in October 2014. Wealthfront’s similarity to traditional banks is that they both manage assets and investments for their customers. However, traditional banks, such as private banks, usually serve high-net-worth individuals with high income and sizable assets, while Wealthfront also provides investment services for less wealthy people. Additionally, Wealthfront only charges customers assets fees, with no commission fees. The simple and transparent pricing even enables Wealthfront to become a strong rival of the traditional banking industry.
No impact observed
If there is no effect, then FinTechs might serve a new channel because these firms often attract clients that traditional banking services do not normally cover. For instance, risky small companies, consumers with a lacking credit history, or the small-dollar loan market (Demos, 2016; Hayashi, 2016). FinTech companies use technology to assess these borrowers’ creditworthiness inexpensively—an advantage over traditional banks with many small stores in lower-income neighborhoods across the country (Hayashi, 2016).
Of course, existing banks may also acquire FinTech companies to gain access to new technology, which would make it more difficult to find a direct relationship between FinTech funding and incumbent retail bank stock returns. For example, Capital One, the tenth largest bank in the US in terms of total assets and market capitalization, acquired FinTech start-up Level Money in 2015. The start-up was a San Francisco-based digital banking technology firm, which described itself on their homepage as “the leader in helping the next generation spend with confidence, save more and achieve financial balance.” The company is best known for its award-winning personal finance app, which provides a simple solution to analyze and budget customers’ financial positions. With more than 800,000 downloads to date, the app connects to 250 US financial institutions and specifically targets Millennials.
After its acquisition by Capital One, the FinTech firm became part of Capital One’s Digital Innovation Team, which enables Capital One to strengthen its capabilities in digital banking technologies. In a blog post, Level Money CEO Fuentes said the deal helps his company continue to evolve and grow, called Capital One, “much younger and nimbler than any other top bank,” and said it is “on a mission to transform the way banking is done.”Footnote 1
No effect could also imply that the start-ups are still too small compared to large-established banks because these incumbents deal in trillions instead of billions. Furthermore, incumbents benefit from their ability to create credit easily and from ingrained strengths, such as their current accounts, which allow clients to securely store their money and have permanent access to it. Since this part of finance is heavily regulated, this sector attracts few FinTechs as competitors (The Economist, 2015a, b). Finally, no effect could also result from the substitution and complementary effects offsetting each other.
Hypothesis
To answer the research question, we need to test whether FinTech start-ups have a significant effect on the retail banking industry. If this effect exists, then they should affect the estimated stock returns of incumbent banks (Benner, 2007). Liu and Miller (2014) and Sood and Tellis (2009) add that the prospect of disruptive pressures should depress the stock prices of established firms. Therefore, the stock returns of incumbent retail banks should encounter a negative effect when disruption is expected.
To examine the likelihood of innovative disruption, we require a reasonable proxy. Prior studies show that external funding events provide a relevant and credible measure to compare the future success of start-ups, as external financing is critical for growth and survival (Dean and Giglierano, 1990; Davila et al., 2003; Mina et al., 2013). Therefore, it is reasonable to assume a positive relationship between the FinTech start-up’s value and the external funding it receives. Thus, we can use digital banking start-ups’ funding to examine the null hypothesis:
Funding in the US FinTech digital banking industry has no contemporaneous effect on the stock returns of US incumbent banks.
We proxy funding by the volume of funding and the number of funding deals. The latter might signal additional information about the potential value of the start-ups because a large volume of funding does not necessarily imply a large number of investors. Therefore, we introduce the number of funding deals as another industry measurement.
In addition to our hypothesis that links FinTech funding to stock prices, it would be interesting to investigate relationships with other metrics. For example, the relation between FinTech funding and bank competition, bank risk-taking, and merger and acquisition activity among incumbent retail banks. Unfortunately, we do not have reasonable proxies available to statistically test such hypothesis. This would be a worthwhile endeavor for future research.
Models of expected returns
We use a model that estimates the expected stock returns to test the hypotheses. Many studies use Sharpe’s (1964) capital asset pricing model (CAPM). However, the empirical implementation of this model is sufficiently poor to deny its validity (Fama and French, 2004).
To better explain the average returns on stocks and bonds, Fama and French (1993) extended the CAPM model to use three factors for the stock market: an overall market factor capturing the excess return of the market portfolio, a factor related to firm size, and a factor for book-to-market equity values, leading to the following modelFootnote 2 (Davis et al., 2000):
$$ E\left({R}_i\right)\kern0.5em -\kern0.5em {R}_f\kern0.5em =\kern0.5em {b}_i\left[E\left({R}_m\right)\kern0.5em -\kern0.5em {R}_f\right]\kern0.5em +\kern0.5em {s}_iE(SMB)\kern0.5em +\kern0.5em {h}_iE(HML). $$
Here, Ri is the return on asset i, Rf is the risk-free interest rate, and RM is the return on the value-weighted market portfolio.
SMB is the equal-weighted averages of the returns on the three small stock portfolios minus the three big stock portfolios. Similarly, HML is the average return on a portfolio of high book-to-market equity stocks minus the average return on a portfolio of low book-to-market equity stocks, constructed to be neutral with respect to size. Despite its high empirical validity in asset pricing, the three-factor model still has a theoretical shortcoming. The SMB and HML explanatory returns are not variables that capture investors’ concerns, but are “brute force constructs” instead (Fama and French, 2004). Furthermore, both the three-factor model and the CAPM suffer from the momentum effect, indicating that stocks that do well tend to continue to do well and vice versa (Jegadeesh and Titman, 1993).
As an extension of the three-factor model, Fama and French (2015) introduced a five-factor model, adding profitability and investment factors:
$$ E\left({R}_i\right)\kern0.5em -\kern0.5em {R}_f\kern0.5em =\kern0.5em {b}_i RMRF\kern0.5em +\kern0.5em {s}_iE(SMB)\kern0.5em +\kern0.5em {h}_iE(HML)\kern0.5em +\kern0.5em {r}_iE(RMW)\kern0.5em +\kern0.5em {c}_iE(CMA). $$
Here, Ri is the return on asset i, Rf is the risk-free interest rate, and RMRF is the excess return on the value-weight market portfolio.
RMW is the average return on the two robust operating profitability portfolios minus the average return on the two weak operating profitability portfolios. RMW is the average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios. This study uses both the Fama-French three-factor model and five-factor model to capture the expected stock returns of the incumbent retail banks. Note that there is no general agreement on which asset pricing model best describes the cross-section of stock returns. By including both models, we partially address the robustness of our results for different specifications of the asset pricing model.