Entrepreneur and Venture Capital Networks in Fintech

John Clemow

I will be taking 2^nd module

Entrepreneur and Venture Capital Networks in Fintech

TOPIC

For my project in the second module of this course, I will be doing social network analysis on the rapidly growing field of financial technology – AKA “fintech.” This social network analysis will contribute to my capstone research on venture capital (“VC”) funding in fintech and the performance of VC-backed fintech startups. Specifically, I will be analyzing rapidly growing startups focused in major segments of fintech – such as payments, blockchain, online lending, and insurance – with the overall goal of identifying “powerful” participants in each network; either in their degree, eigenvector, or other combined measures.

BACKGROUND

Fintech is a rapidly emerging field, while lacking a codified definition, I believe was best defined by Andrew Ross Sorkin of the New York Times as:

“A catchall for a near-revolution of new technologies [and software] aimed at upending parts of the financial world, including payments, wealth management, lending, insurance, and currency.”[1]

While Sorkin’s use of “catchall” may seem almost sarcastic or diminutive here, it is also extremely accurate based on my personal experience working in and researching fintech. In fact, this trait is also partially what drew me to this further researching fintech– it is an ill-defined field, yet one that lead economists, investment bankers, and central bankers have all joined in recognizing as a source of significant innovation and economic growth.

Fintech ventures typically lack the collateral or creditworthiness required for bank loans, and therefore their growth is largely funded by venture capital funds. Therefore, the trends and network effects

Having reviewed aligned SNA projects from previous years, I believe the closest example is Ying-Ju (“Ruthy”) Li’s “Networks, Clusters, and Entrepreneurial Ecosystems in the United States” from 2015[2]. In this study, Ruthy measured the total venture capital funding originating from major cities in the U.S. – most notably the San Francisco Bay, Boston, and New York – and analyzed trends among funders based in each city. Additionally, Ruthy did a very effective job of visualizing and analyzing the networks of mutual investors shared among major VC-backed startups such as Pinterest, Airbnb, Dropbox, and Uber. I believe a similar trend analysis of investors joining in investing in the same fintech ventures would be an effective addition to my study.

RESEARCH QUESTIONS

How do U.S. fintech startups obtain funding from VCs?

• Does the regional location of a fintech startup have an effect on what VCs it attracts or the amount of money it raises?
• Who are the most active fintech startups in generating VC funding?
• How frequently do fintech startups gain rounds of VC funds? Is this influenced by a company’s segment within fintech?
• In terms of exits, are fintech ventures more likely to go public or be acquired?
• How has fintech startup funding changed over time?

 How do VCs investing in fintech ventures relate to one another?

• Who are the key actors driving fintech venture funding?
• What VCs are investing in the same fintech startups? What trends are there between funds that frequently co-invest?
• Are there any subgroups of fintech VCs that have helped drive growth in fintech?
• What do these trends indicate about the future of fintech VC activity?

SNA METHODOLOGY

Due to having two distinct groups – fintech startups and VC funds – this will be a two-mode network tracking which VC funds have invested in what fintech startups. For each of the startups, I will look at attributes such as location, fintech segment, amount of money raised, total number of funding rounds, and whether the startup served as an exit (e.g., went public or acquired). For each of the VCs, I will look at location, total fund size, whether the VC was a lead investor in the startup, what the fund invests in aside from fintech, and how many of the firm’s fintech investments were exits. The reason I highlight “exits” as an attribute for both fintech startups and VCs is that, from the perspective of a VC, achieving an exit is the primary rationale behind investment decisions as this is how investor returns are typically generated.

The measurements that I believe will provide the most applicable insights are centrality measures such as degree, eigenvector, and betweenness. Together, I expect these measures will provide considerable insights into the extent to which certain startups and VCs play a role in connecting nodes within this two-mode network.

DATA COLLECTION

I have already accumulated considerable data from Crunchbase – the largest platform and source of data such applicable venture/startup activity – on investment activity in all startups labeled within their system as “Fintech.” Having already cleaned this initial data – comprised of 193 investment rounds in 121 startups – I will grow this dataset further by pulling additional startups within Crunchbase’s database that are incorrectly classified as not being fintech due to the previously mentioned taxonomy issues in fintech. From my preliminary data collection to date, I am confident the resulting dataset will be more sufficient for social network analysis. Additionally, my prior experience in database management and cleaning data will prove helpful in accumulating data in such a way that I can perform meaningful analysis.

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[1] NY Times, Andrew Ross Sorkin, “Fintech Firms Are Taking on the Big Banks, but Can They Win?” April 6, 2016.

[2] Ying-Ju Li, Blog Post, “Investing Network of the Startup Ecosystem in the United States” http://crtunnard.blogspot.com/2015/10/investing-network-of-startup-ecosystem.html