The State of FinTech

The recent travails of Lending Club have shone a not-so-flattering light on FinTech startups. On the surface, the finance industry replete with large slow-to-innovate institutions appears ripe for disruption. However, the performance of the recently public FinTech companies shows that finance make not be as easy a target for disruption as FinTech companies have made out.

What is FinTech?

FinTech startups attempt to take a single business line in the finance industry and apply new technology and business practices. In general there are four primary business lines startups have focused on:

  • Wealth Management (Betterment, Wealthfront)
  • Transfers (Transferwise)
  • Payments (Square, Stripe)
  • Lending (Lending Club, OnDeck)

In this article I will focus on the the lending space which has seen the most turmoil in recent months. 2015 saw the debut of two lending companies on the public markets — namely Lending Club and OnDeck. Lending Club is a marketplace for personal small loans whereby the loans are on-sold to investors. OnDeck, by contrast, focuses on small businesses and utilizes its own balance sheet to fund the loans (although it also runs a small marketplace) and so more closely resembles a traditional bank.

The core proposition by the lending startups is that banks, who typically prefer loans under $15k to be done via credit card debt, have served the small loans market poorly. Furthermore, risk analysis can be done more efficiently and accurately using algorithms.

The IPOs for both Lending Club and OnDeck have proved disastrous with both Lending Club and OnDeck’s stock down by 80% and so slammed shut the door for FinTech IPOs in the short to medium term. Several categories of risk specific to FinTech startups have become apparent:

Funding Sources

FinTech lending companies are heavily dependent on consistent and stable sources of funding to back their loans (marketplaces such as Lending Club require investors to purchase the loans whereas on-balance sheet lenders like OnDeck require banks to finance their lending). The market turmoil of 2015 and 2016 has clearly demonstrated that these sources of funds are highly unpredictable — Lending Club is now having to warehouse loans on its balance sheet as it is unable to find counterparties willing to purchase them. Likewise, OnDeck which had previously planned to offload 40% of its loans was only able to offload 15%.

Thus, despite efforts to broaden their funding base, both OnDeck and Lending Club are having to rely on traditional institutional (mainly bank) funding to fund their loans, the withdrawal of these lines of credit could pose an existential risk for the companies.

Reputational Risk

Tech startups often endure several public setbacks such as security breaches or privacy violations. These are often swiftly overcome and forgotten and the company progresses. Not so for finance companies where infractions can lead to a loss of confidence and a withdrawal of funding. Lending Club’s changing of the dates of $3m of loans (out of a total of ~$3bn loans originated) as well as the CEO’s undisclosed shareholding in a related party have proved so serious that the firm has not only removed the CEO but cannot provide earnings guidance as there is so much uncertainty regarding the fallout.

Regulation

FinTech companies are currently more lightly regulated than the banks who they are competing with. This is likely to change as regulators turn their attention to ‘shadow banking’ and in loan marketplaces where individual investors are investing in loans (note that FinTech lenders are targeting individual investors as more stable source of funding)

Credit Quality

FinTech lending companies trumpet the sophistication of their risk analysis technology, however, this is essentially a black box and it is impossible for investors to evaluate until the company has gone through a complete credit cycle as credit risk assessment can be flattered by an overall macro environment strong credit (as is the case in 2016, demonstrated by the below chart).

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US Corporate Bankruptcies 2006–2016

Traditional Startup Metrics

All this naturally leads to how should FinTech companies be valued. There’s been considerable debate on the subject of FinTech company valuation — should they be valued as typical finance companies or tech companies? The issue has a massive impact on valuations since tech companies tend to be valued on a multiple of revenue (typically around 4x for Saas companies), whereas finance companies tend to be valued using a multiple of their net assets which usually gives a far lower valuation.

Needless to say FinTech companies argue vociferously that they are tech companies and should be valued as such. Yet the core reasons tech companies are valued on multiples of revenues don’t apply to FinTech companies:

  • Tech company revenues are relatively stable (it is usually the level of growth which causes uncertainty). This is because there is usually some degree of lock-in for tech companies’ customers — with social media companies it is the network of friends/colleagues, for Saas companies it is the customer’s investment in time to adapt to using the product and the difficultly in migrating to a rival product. None of this applies to FinTech lending companies whose customers can easily move to alternative platforms.
  • Tech company margins tend to be predictable and manageable. The costs of delivering a tech product or service do not swing wildly and so gross margins can be forecast and managed. Not so for FinTech lending companies who are at the mercy of the vicissitudes of the economy which determines interest rates, demand for loans and default rates all of which have a direct impact on gross margins.

Despite all of this, the market is still focused on revenue as the primary driver of valuation although this is likely to change as the companies mature and the market’s approach to valuing tech companies evolves.

Correlation – The Need For ‘Stationary’ Data

How correlated are Intel and Google’s stock  prices? The below graph shows the daily close of prices of both from mid 2009 to mid 2011.

GOOG INTL Stock Prices

From first impressions, it certainly looks like the two price series move in tandem and should have a high correlation. Indeed it turns out that correlation coefficient of the two stock price series is 0.88 , indicating a high 88% correlation  between the Google and Intel stock prices.

However, this is totally misleading – in reality the correlation between the two is a mere 36%.

Correlation, in common with most time-series data analysis techniques requires ‘stationary’ data as an input. To be stationary the data must have a constant variance over time and be mean reverting. Stock price data (and many other economic data series) exhibit trending patterns which violates the criteria of stationarity. Transformation to stationary data is quite simple, however, as converting the daily price closes into daily returns will normally be sufficient. The return series of a stock is usually considered as stationary for time series analysis purposes, since it is mean-reverting (as the daily returns oscillate above and below and constant mean) and has a constant variance (the magnitude of the returns above and below the mean will be relatively constant over time despite numerous spikes).

The daily series of returns (ie percentage price changes) for both Google and Intel stocks can be seen below. Not that there is no trend to the series which moves above and below a constant mean – which for daily stock price returns is almost always very close to 0%.

GOOG INTL Stationary Series

The requirement for stationary data in calculating correlation can also be explained intuitively. Imagine you were looking to hedge a long position in Google stock with a short position in Intel, you would want the return on the Google stock to the match the return on the Intel stock. Hence correlating the prices would be irrelevant, in such a scenario you would want to know the correlation between the two sets of returns as this is what you would essentially be attempting to match with the hedge.

 Correcting For Drift And Seasonality

In correlating stock price data, transforming the raw price data to returns is usually considered sufficient, however , to be more rigorous any additional trends could be stripped out of the data. Most models of stock price behaviour include the risk free interest rate plus a required rate of return as a constant drift over time – the argument being that stock investors require this return for holding the stock and over the long term the stock should deliver that return. Thus, the this return could be backed out of the series before calculating correlation. In practice, since we are dealing with daily returns, the long term drift as a minimal impact on the calculation of correlation.

Some economic data series such as durable goods orders exhibit strong effects of seasonality. When raw durable goods orders data is transformed into percentage changes, it is indeed mean reverting with a constant mean. However, the series will still not be stationary due to the strong seasonality effects – orders will be much much higher during the Christmas shopping season and so the percentage changes will always spike at the time resulting in a non constant variance.

Seasonality can be dealt with by cleaning the data series using another series which exhibits the same seasonality. In the case of durable goods orders, the raw CPI index (note: not the percentage change in CPI) would be such as series since the CPI index will typically spike during shopping seasons. Thus the durable goods orders could be divided by the CPI to arrive at a ‘deflated’ durable goods series which could then be made stationary by transforming it into percentage changes between periods.