Banks and Bubbles: How Good are Bankers at Spotting Winners?

1. Banks and Bubbles: How Good are Bankers at Spotting Winners? Laura Gonzalez Chris James University of Florida

2. Bank Lending During the Technology Bubble • What types of start-up firms establish bank lending relationships? • Do the most informationally opaque firms borrow from banks? • Are some firms not “bankable”? • How important are cash flows and collateral in determining lending relationships? • Is VC financing a substitute or complement to bank financing? • What types of bank lenders specialize in lending to the most informationally challenging borrowers? • Do firms with banking relationship perform better after their IPOs? Rock stars versus Accountants

3. Motivation: Why Look at Lending to Start-ups? • Banks and other intermediaries add value in two principal ways: • Project screening: Reducing pre-investment information asymmetries and adverse selection problems • Monitoring: Reducing post project selection information and agency problems • Where information and agency problems become potentially more important intermediaries are expected to play a more important role in the capital acquisition process. • Start-up companies in the tech sector are a good place to look to see whether banks play a role in screening and monitoring

4. Motivation: Why Look at Lending to Start-ups in the Technology Sector? Screening firms based on soft information is likely to be particularly important among start-ups in the tech sector. • Intermediated lending is distinguished from “Arms Length” lending by the relative importance of “Soft” versus “Hard” information. • Soft information is customer specific information that is inherently difficult to quantify and transfer. Eg. How smart and honest is the CEO, proprietary information and value of “elevator” assets. • Hard information: Accounting/performance information that can be quantified and transferred at relatively low cost. Eg. How good of a track record does the firm have in making earnings forecasts? • Since intangible assets and growth options are the principal assets of tech start-ups, soft information in lending is likely to be particularly important. • The median age of tech firms in our sample is less than 5 years. • About 75% had negative operating income. • Average (Median) first day price to sales multiples over 50 (16).

5. Motivation: Why Look at Lending to Start-ups in the Technology Sector? • Empirical Implications: • The cash flows and collateral are likely to be less important determinants of bank credit relationships among tech firms. • If banks play an important role in screening firms based on near term prospects then controlling for ex ante observable risk characteristics bank lending relationships are expected to be informative of future operating performance. • Alternatively, banks lending to our sample firms was simply bridge financing.

6. Motivation: Why Look at Lending to Start-ups in the Technology Sector? • Theory versus Practice • Most models of financial intermediation predict that (holding contract type constant) the most informationally challenging firms will find intermediated funding most attractive. • However, in practice bankers’ focus on cash flows and collateral may make some firms not “bankable credits”. • Models of VC financing assume that VC financing is a substitute or alternative to bank financing. • However, boutique lenders claim that they work with VC. • Data for publicly traded firms is not very helpful in sorting out these issues • Empirical Issues • How are proxies for information problems (age, size, profitability and the tangibility of assets) related to the bank borrowing? • What is the relationship between VC backing and banking relationships?

7. Motivation: Why Look at Lending to Start-ups in the Technology Sector? • Does function follow form? • Berger (and everyone else) argue that small banks have a comparative advantage in lending based on “soft” information. • Small tech lenders claim they provide “innovative” solutions to start-up needs: “You'll find that traditional bank credit facilities often mandate multiple loan covenants, such as minimum profitability or a minimum level of liquidity. In contrast, our Commercial Finance division usually requires one operating covenant, based on a review of your financial forecast.” Silicon Valley Bank Empirical implication: • If small boutique banks have a comparative advantage in lending based on soft information (or a higher tolerance for risk) then we would expect their borrowers to be younger and have lower cash flows than firms that borrow from large banks.

8. Motivation: Why Look at Lending to Start-ups in the Technology Sector? • Finally, did bankers (like VCs and Investment bankers and stock analysts) get caught up in the tech bubble and change their lending standards? • Metamor (an internet technology consulting firm) borrows $80 million on a secured basis for working capital. The catch is that “collateral” is its stock in Xpedior a provider of e-business solutions (which as of 1/25/00 was selling at 14 times sales and 300 times forward looking EPS).

9. Sample Selection • Sample consists of 529 tech and 142 non tech IPO firms that went public during 1996 through 2000. • Technology firms are identified based on 4 digit SIC codes and internet identifications using Loughran and Ritter criteria. • The non-tech sample is based on a random sample of 175 non-tech firms. • We include firms for which we could find offering prospectuses and post IPO 10k’s from which we could determine whether or not the firm had a bank lending relationship. • Bank borrowing is defined narrowly to included loans or lines from commercial banks and other depository institutions. • Other debt (regardless of source) is classified as non-bank debt.

10. Summary of Findings: Firm Characteristics • Despite virtually no earnings and little collateral most (75%) tech firms have bank lending relationships. • Firms with banking relationships tend to be: • Older • More profitable • More likely to use VC financing • Rely less on non-bank sources of borrowing • Cash flows are a much less important determinant the bank lending for tech than for non-tech firms. • Mean and median EBITDA/Sales for tech firms with banking relationships are -57% and -20% respectively. For non-tech firms the mean and median are 12.2% and 9.4%.

13. Summary of Findings: Who Lends to Tech Firms? • Fifty four percent of tech lenders are boutique banks (under $10 billion in assets) • Silicon Valley • Imperial (now Comerica) • Borrowers from boutique lenders are smaller, younger and less profitable than borrowers from large banks consistent with the function follows form argument. • Median EBITDA/Sales for boutique lenders is -40.55% versus .33% for large banks. • Boutiques are more likely to take equity (warrant positions). Given IPO underpricing the value of these positions is significant.

14. Why do Underwriting Standards Differ for Tech Firms? • Differences in Collateral? • Differences in Income Recognition and Expenses? • Differences in the size and type of loan? • Differences in the source of repayment?

16. Did Loan Underwriting Standards Change During the Bubble? • During the bubble, the median age of tech firms with banking relationships declined from 7 to 5 years. • The median industry adjusted EBITDA/Sales for tech firms with banking relationships fell from 1.8% pre-bubble to -41%. For non-tech firms the EBITDA/Sales increased from -.17% pre-bubble to 4.54% during the bubble. • Changes in lending at boutique lenders

17. Can Banks Spot Future Winners? Do Banks Simply Establish Relationships With Firms With the Best Current Prospects or are They Able to Identify Firms With the Best Future Prospects as Well? • Measures of Operating Performance • EBITDA/Sales, OCF/Sales • Industry Adjusted Performance (4 or 3 digit SIC code) • Problem: Does not control for firm performance characteristics or mean reversion • Controls of industry effects • Barber Lyon--Peer Adjusted • 4digit SIC code within 10% of IPO firm performance • 3 digit SIC code within 10% of IPO firm performance • 2 digit SIC code within 10% of IPO firm performance • Two Problems with Barber-Lyons Peer adjustments • Matches are hard to find • Do they have banking relationships?

18. Figure 1 Difference in the Median Industry-Adjusted Performance of Bank Versus Non-Bank Technology Firms 33.65 19.01 % 18.31 11.69 Year 0 Year 1 Year 2 Year 3

19. Industry Adjusted Difference in EBITDA/Sales Bank versus Non-Bank Non-Tech Firms 6.54% 4.10% % 1.77% 0.80% Year 0 Year 1 Year 2 Year 3

21. Can Banks Spot Winners? • Controlling for pre-IPO operating performance, the post IPO performance of firms with pre-IPO banking relationships is much better than the performance of firms without banking relationship. This is consistent with banks as screeners argument. • The effect of banking relationships on post-IPO performance is most pronounced among tech firms. This is consistent with the hypothesis that soft information in lending decisions is more important for tech firms. • There is no relationship between post-IPO performance and continued borrowing post IPO.

22. Conclusions • Among small start-ups, banks specialize in lending to the most profitable firms. The accountants not the rock stars • Bank lending relationships and VC financing appear to be complements not substitutes. • Boutique lenders appear to use different underwriting standards. • Banking relationships are informative of future performance. • Banking relationships seem to be most informative when soft information is most important.