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 a Haas 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.
The February 4th Meeting (at SVB)
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 and the SVB Analysis group uses the Toad client to run SQL statements on it. 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
The first two steps above should be elaborated. The CRM (Customer Relationship Management) team gets the filings and notes the stage of their relationship with the customer on it. The 'Credit Team' (which is partly in the US and partly in India [EJE]) then uses Moody's Risk Analyst to 'spread' the financial statements into a database. Data is then 'reshaped' to clean it up and to remove "prospects" sections and other data that for contractual reasons are restricted; the spreading and the reshaping combined result in a loss of granularity, but standardization. Firms are tagged with a CIF number, which is unique to the firm, as well as a Customer ID. There may be N-1 relationship between Customer ID's and firms, as some customers may be responsible for many firms, however, most of these are apparently 1-1. Each statement is then assigned a Statement ID. In addition, the credit team assigns a "risk-stage" code to each statement.
When the SVB Analytics staff get the data back they get a table called "T_Moodys" which contains all of the financials keyed by both CIF and StatementID. They then clean this up further, removing duplicate statements and errors, and add extra codes, including a "development stage code".
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].
Financial statements can also be accumulated by SVB for reasons other than credit issuance. These include the Entrepreneur Services group that matches (apparently with a 20% success rate [EJE]) VCs to Entrepreneurs, as well as the Emerging Technologies Team that examines sectors for growth. At present these financial statements do not make it into the system described above. However, the Emerging Technologies Team does use the development database, and does mark it with potentially useful codes.
At present SVB gets draw downs of data from VentureSource every six months. They use this data to:
- Validate their own coverage
- Provide reprots to clients (they have 328 charts made every time by a group in India for use in presentations)
- Potentially to enhance their benchmarking service (though this has not been fully implemented as yet [EJE]).
The CapMx Data
The CapMx data is a electronic collection of firms' capital tables. In theory it contains the fully detailed capital structure for each firm in the database. The bank works with various law firms, including Orrick and Gunderson, who 'stored' this data in Excel sheets, as well as venture capital funds and the companies themselves (who 'stored' this data on paper). The bank responded to this situation by creating and maintaining the CapMx database, to store the data on behalf of their various clients, and to facilitate various transactions on this data.
You can fill out a form to view a demo of the online interface into the CapMX data (though I had problems playing with it [EJE]). This online interface provides users with access to their data, and allows the users to conduct a limited number (and range) of transactions on it.
The database can accomodate data on the the authorized capital of the firm, individuals/entities associated with this capital, and details on the firm itself. However, there is little to no incentive for the firms or representatives of the firms to fill out any data beyond that which is required to record the capital structure at a particular moment in time.
The original capital structure of the firm is inputted by staff at SVB. There is an 'upload template' to guide the staff in this process, and data is sourced from the certificate of incorporation (or certificate of authorization for later issues). However, the input suffers from a number of issues:
- There is free text input for the class of stock, so that COMMON, Common, COM, Common1, etc, are all possible ways of recording a Common Stock entry.
- It is not possible to close out a series of stock
- There is no standardization on the naming of series (SERIES A, etc), and in the case of recapitalizations or reorganizations it is possible to have two series named say SERIES A and SERIES 1.
- Preferences may not reflect the most recent series
- Preferences are often recorded as a static value (taken from the certificate)
- Likewise, the conversion ratio is often recorded as a number, rather than an formula (1:1 conversions are less problematic)
- There may be issues with the recording of preference priorities, participation, and payment-in-kind clauses
- It would be highly problematic to record capital structures for firms organized as LLCs or LPs (or anything other than a C-CORP or S-CORP).
