Silicon Valley Bank Data Project

In January 2011, two representatives Silicon Valley Bank gave a presentation at the Haas business school, organized by Jerry Engel of the Lester Center for Entrepreneurship and Innovation. The purpose of this presentation was to explore the possibility of having Haas PhD students and faculty conduct research with the SVB's data. This page details findings from that meeting and the follow-up on-site meeting between SVB and Haas's representative, Ed Egan.

The January 18th Meeting (at Haas)

Attendees

From Haas:

• Jerry Engel (engel@haas.berkeley.edu), Faculty Director, Lester Center for Enterpreneurship and Innovation
• Toby Stuart (tstuart@haas.berkeley.edu), Visiting Faculty from Harvard Business School
• Javed Ahmed (jahmed@haas.berkeley.edu), PhD Candidate in finance
• Ron Berman (ron_berman@haas.berkeley.edu), PhD Student in marketing
• Ed Egan (ed_egan@haas.berkeley.edu), PhD Student in BPP
• Orie Shelef (orie_shelef@haas.berkeley.edu), PhD Student in BPP

The following are interested in this data but were unable to attend the meeting:

• Sharat Raghavan (sharat_raghavan@haas.berkeley.edu), PhD Student in BPP
• Neil Thompson (neil_thompson@haas.berkeley.edu), PhD Candidate in BPP

From SVB:

• Michael Graham ‎(MGraham@svb.com), Senior Managing Director of SVB Analytics
• Dave Krimm‎(DKrimm@svb.com), Head of Strategy and Research for SVB Analytics

Where: Haas School of Business

Notes From the Meeting

Joining Data

The meeting opened with an impassioned plea from Toby, echoed immediately by the other academics, to be allowed to join the data to other datasets. This could be accomplished in a number of ways, including leaving 'identity identifiers' such as either names or numbers that are linked to names, in the data. SVB did not seem adverse to this.

Obvious examples of dataset which would be joined to the data include:

• Thompson VentureXpert
• SDC Mergers and Acquisitions
• Global New Issues data
• The NBER Patent Data, or other patent data
• Bankruptcy data

Generally, joined data would be available to Haas students/faculty through our library licenses, but not to SVB. SVB has a license to Dow Jones' VentureSource database, though their license does not permit them to see the identities of firms.

SVB Datasets

SVB has three datasets that they are considering sharing with us in some fashion. These are:

• The Valuations Data
• The Benchmarking Data
• The CAPMX data

The Valuations Data

Michael estimated that that SVB has 'valuations' data on 2600 early stage firms, with many firms having multiple valuations conducted over time. Internal Revenue Code 409A requires that there be no discrepency between an options value and the value of common stock, in part to prevent issue with backdating of options, and that valuations be conducted by a third-party at an arm's length from either the 'service recipient' (i.e. employee/executive/etc) and the 'service provider' (i.e. the firm). Thus firms may have approached SVB to provide them with valuations on the firms common stock potentially every time that there is an event, such as a stock option issue, which would require compliance. SVB has been collecting this data for approximately 4 years.

SVB is interested in (co-)authoring an article on valuation for publication in a (trade) journal. Michael has noticed that option pricing models (i.e. Black-Scholes models) lead to over-valuation of the stock, especially for non-participating preferred stock, and that the use of 'mulitple models' (i.e. those that use simple 1x-2x, 2x-3x, 4+ x valuations) are far more accurate valuations, and would like assistance in exploring this.

The Benchmarking Data

SVB Analytics was looking to answer "what is the next business for SVB?" Their conclusion was that they should launch a benchmarking service for their clients. They have assembled a large proprietary database of financial statements for privately-held, predominantly venture capital-backed, firms in certain sectors (specifically life science, software and cleantech). They estimated that they have one or more financial statements for approximately 50% of the venture-capital backed firms in these certain sectors, that were active in 2010.

A primary service for SVB is lending, whether in the form of loans, credit cards, or other credit, to venture capital backed startups. These loans are essentially guaranteed by the reputation of the investing venture capitalists, and data on these loans was not discussed or offered to us. However, each time that a firm applies for a loan, or has some other credit-event, this triggers a request for a full set of financial statements. The bank has been collecting these statements and is now rolling out a 'benchmarking' service, which allows firms to compare their performance on various financial measures against aggregate data on their 'peers'.

The SVB VentureSource license allows SVB to provide aggregate venture capital data to their clients, and integration of this data into the benchmarking data is being considered by SVB.

The CapMx Data

One core service that the bank offers to its clients is the management of their firm's capital tables. The CapMx database was estimated to have approximately 2000 users, and contains details on the capital structure of the firm including common stock outstanding, preferred stock outstanding, warrants and stock options, employee share option plans, and liquidation preferrences. The users of this data are both the firms themselves, and accounting/law firms that work for these firms. The tables are 'initialized by agents' and then accessed by the firms.

Caveat

This write up contains some subjective interpretation of the facts, because I believe that this is useful. However, it is possible that I might be error in my understanding, and where this is relevant I note it with the bracketed moniker [EJE].

Attendees

From Haas:

• Ed Egan (ed_egan@haas.berkeley.edu), PhD Student in BPP

From SVB:

• Michael Graham ‎(MGraham@svb.com), Senior Managing Director of SVB Analytics
• Dave Krimm‎(DKrimm@svb.com), Head of Strategy and Research for SVB Analytics
• Dan Zaelit ‎(dzaelit@svb.com)‎, SVB Analytics
• Jan, a manager partially responsible for the CapMx data entry

Where: SVB San Francisco office - 8th Floor, 555 Mission St, San Francisco.

Background on SVB Analytics

SVB Analytics is a 'non-bank affiliate' of SVB. It runs two for-profit services: the valuation services, and the equity compensation management services (CapMx). It also provides the benchmarking service (which is currently being rolled out [EJE]), and other advisory/industry reporting services, that are run primarily to increase awareness of the bank and its other services [EJE]. Crucially, the SVB analytics group directly 'controls' the valuation data, whereas the data for the benchmarking service (the financial reports) is generated and maintained elsewhere in the bank. The CapMx data appears to fall between these two - with the R&D group running the underlying databases and SVB Analytics providing the service [EJE].

The organizational hierarchy is: Michael <- Dave <- Dan. Michael has the authority to distribute the data to, or to otherwise engage in a relationship with, Haas [EJE]. Michael may report to Iris Hit-Shagir, the president of SVB Analytics [EJE]. A new individual has been/is being hired, possibly with the title of Director of Technology, to work with Dave, but has not yet started [EJE].

The Benchmarking Data

The bank's loan clients must file either monthly or quarter financial statements (depending on how often they issue statements) with the bank. These are aggregated into quarterly statements for all loan recipients. From 2004 forward, these statements have had various financial variables extracted from them and stored in a centralized electronic repository. Starting in 2008 the 'granularity' of data, that is the number and fineness of financial variables was dramatically increased. Nevertheless the major variables (Sales, Total Assets, Net Income, etc) are available back to 2004. Expense variables such as Sales and Marketing (S&M) expenses, R&D expenses, and General and Administrative (G&A) expenses are broken out starting in 2008.

Aside from financial statement data, the data also includes:

• A proprietary but extremely fine-grained industry classification schema that has been mapped to VentureSource's classification schema. The schema has a three level depth: Segment, Area, Niche.
• The location of the firm: ZIP Code, State and Region.
• Year founded
• Stage of development

The data exists in two databases: an operational database, and a development database which is populated by quarterly draws from the the operational database. Both databases run on an Oracle platform. The development data is validated and cleaned manually by SVB staff, and we could expect draws from this source. The data lives in a series of (flat) tables, with the main table containing the financials keyed as CompanyName-DateOfFinancials, and other tables, such as for the year of founding, keyed by CompanyName.

SVB takes data from the validated and cleaned development database and uploads this onto their online Birst based web-platform, to provide their benchmarking service. Companies accessing this data through the Birst interface are restricted the selecting aggregate benchmarking portfolios to compare their firm against. The interface allows them to construct portfolios on the basis of geography, industry, comparability in terms of financial ratios, and so forth, and reports the size of the comparing portfolio as either 5-30 firms, 31-100 firms, and so forth. Firms are restricted to seeing comparison portfolios composed of at least 5 firms. SVB is trying to advance this service into a CEO desktop tool, which will report things like Josh James' Magic Number - this requires fine grained data as well as uninterupted sequential financial statements, which is surely good news for researchers going forward.

The process of uploading the data to Birst is as follows:

• Within one month of the quarter end financial statements are sent to India
• The contractor in India 'converts' the data into electronic format within one month
• SVB Analytics staff clean and validate the data.
• Within three months of the quarter end the data is uploaded to the Birst web-platform

There is a plan in progress to shorten this process and to move to monthly financial data. Specifically, SVB are considering allowing/facilitating input from Quickbooks and other accounting systems, to get electronic data in predetermined formats directly from their firms.

It seems possible that Haas could enter into an agreement to get a feed of this data simultaneous with the upload to the Birst platform [EJE].