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

Laura Gonzalez Laura Gonzalez Chris JamesChris James

University of FloridaUniversity of Florida

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What types of start-up firms establish bank lending What types of start-up firms establish bank lending relationships? relationships? – Do the most informationally opaque firms borrow Do the most informationally opaque firms borrow

from banks?from banks?– Are some firms not “bankable”?Are some firms not “bankable”?– How important are cash flows and collateral in How important are cash flows and collateral in

determining lending relationships?determining lending relationships?– Is VC financing a substitute or complement to bank Is VC financing a substitute or complement to bank

financing?financing?What types of bank lenders specialize in lending to the What types of bank lenders specialize in lending to the most informationally challenging borrowers?most informationally challenging borrowers?Do firms with banking relationship perform better Do firms with banking relationship perform better after their IPOs? Rock stars versus Accountantsafter their IPOs? Rock stars versus Accountants

Bank Lending During the Technology Bubble

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Banks and other intermediaries add value in two principal ways:Banks and other intermediaries add value in two principal ways:– Project screening: Reducing pre-investment information Project screening: Reducing pre-investment information

asymmetries and adverse selection problems asymmetries and adverse selection problems – Monitoring: Reducing post project selection information and Monitoring: Reducing post project selection information and

agency problemsagency problemsWhere information and agency problems become potentially Where information and agency problems become potentially more important intermediaries are expected to play a more more important intermediaries are expected to play a more important role in the capital acquisition process.important role in the capital acquisition process.Start-up companies in the tech sector are a good place to look to Start-up companies in the tech sector are a good place to look to see whether banks play a role in screening and monitoringsee whether banks play a role in screening and monitoring

Motivation: Why Look at Lending to Start-ups?

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Screening firms based on soft information is likely to be particularly important among Screening firms based on soft information is likely to be particularly important among start-ups in the tech sector. start-ups in the tech sector. – Intermediated lending is distinguished from “Arms Length” lending by the Intermediated lending is distinguished from “Arms Length” lending by the

relative importance of “Soft” versus “Hard” information.relative importance of “Soft” versus “Hard” information.Soft information is customer specific information that is inherently difficult Soft information is customer specific information that is inherently difficult to quantify and transfer. Eg. How smart and honest is the CEO, proprietary to quantify and transfer. Eg. How smart and honest is the CEO, proprietary information and value of “elevator” assets.information and value of “elevator” assets.Hard information: Accounting/performance information that can be Hard information: Accounting/performance information that can be quantified and transferred at relatively low cost. Eg. How good of a track quantified and transferred at relatively low cost. Eg. How good of a track record does the firm have in making earnings forecasts?record does the firm have in making earnings forecasts?

– Since intangible assets and growth options are the principal assets of tech start-Since intangible assets and growth options are the principal assets of tech start-ups, soft information in lending is likely to be particularly important.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.The median age of tech firms in our sample is less than 5 years.About 75% had negative operating income.About 75% had negative operating income.Average (Median) first day price to sales multiples over 50 (16). Average (Median) first day price to sales multiples over 50 (16).

Motivation: Why Look at Lending to Start-ups in the Technology Sector?

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Motivation: Why Look at Lending to Motivation: Why Look at Lending to Start-ups in the Technology Sector?Start-ups in the Technology Sector?

Empirical Implications:Empirical Implications:– The cash flows and collateral are likely to be less important The cash flows and collateral are likely to be less important

determinants of bank credit relationships among tech firms.determinants of bank credit relationships among tech firms.– If banks play an important role in screening firms based on If banks play an important role in screening firms based on

near term prospects then controlling for ex ante observable near term prospects then controlling for ex ante observable risk characteristics bank lending relationships are expected risk characteristics bank lending relationships are expected to be informative of future operating performance.to be informative of future operating performance.

– Alternatively, banks lending to our sample firms was Alternatively, banks lending to our sample firms was simply bridge financing.simply bridge financing.

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Motivation: Why Look at Lending to Motivation: Why Look at Lending to Start-ups in the Technology Sector?Start-ups in the Technology Sector?

Theory versus PracticeTheory versus Practice– Most models of financial intermediation predict that (holding contract Most models of financial intermediation predict that (holding contract

type constant) the most informationally challenging firms will find type constant) the most informationally challenging firms will find intermediated funding most attractive. intermediated funding most attractive.

– However, in practice bankers’ focus on cash flows and collateral may However, in practice bankers’ focus on cash flows and collateral may make some firms not “bankable credits”.make some firms not “bankable credits”.

– Models of VC financing assume that VC financing is a substitute or Models of VC financing assume that VC financing is a substitute or alternative to bank financing. alternative to bank financing.

– However, boutique lenders claim that they work with VC.However, boutique lenders claim that they work with VC.– Data for publicly traded firms is not very helpful in sorting out these Data for publicly traded firms is not very helpful in sorting out these

issuesissues Empirical IssuesEmpirical Issues– How are proxies for information problems (age, size, profitability and How are proxies for information problems (age, size, profitability and

the tangibility of assets) related to the bank borrowing?the tangibility of assets) related to the bank borrowing?– What is the relationship between VC backing and banking What is the relationship between VC backing and banking

relationships? relationships?

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Motivation: Why Look at Lending to Motivation: Why Look at Lending to Start-ups in the Technology Sector?Start-ups in the Technology Sector?

Does function follow form? Does function follow form? – Berger (and everyone else) argue that small banks have a comparative Berger (and everyone else) argue that small banks have a comparative

advantage in lending based on “soft” information. advantage in lending based on “soft” information. – Small tech lenders claim they provide “innovative” solutions to start-Small tech lenders claim they provide “innovative” solutions to start-

up needs:up needs:““You'll find that traditional bank credit facilities often mandate You'll find that traditional bank credit facilities often mandate multiple loan covenants, such as minimum profitability or a multiple loan covenants, such as minimum profitability or a minimum level of liquidity. In contrast, our Commercial Finance minimum level of liquidity. In contrast, our Commercial Finance division usually requires one operating covenant, based on a division usually requires one operating covenant, based on a review of your financial forecast.” Silicon Valley Bankreview of your financial forecast.” Silicon Valley Bank

Empirical implication:Empirical implication:– If small boutique banks have a comparative advantage in lending based If small boutique banks have a comparative advantage in lending based

on soft information (or a higher tolerance for risk) then we would on soft information (or a higher tolerance for risk) then we would expect their borrowers to be younger and have lower cash flows than expect their borrowers to be younger and have lower cash flows than firms that borrow from large banks.firms that borrow from large banks.

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Motivation: Why Look at Lending to Motivation: Why Look at Lending to Start-ups in the Technology Sector?Start-ups in the Technology Sector?

Finally, did bankers (like VCs and Investment Finally, did bankers (like VCs and Investment bankers and stock analysts) get caught up in the tech bankers and stock analysts) get caught up in the tech bubble and change their lending standards?bubble and change their lending standards?

– Metamor (an internet technology consulting firm) Metamor (an internet technology consulting firm) borrows $80 million on a secured basis for borrows $80 million on a secured basis for working capital. The catch is that “collateral” is its working capital. The catch is that “collateral” is its stock in Xpedior a provider of e-business solutions stock in Xpedior a provider of e-business solutions (which as of 1/25/00 was selling at 14 times sales (which as of 1/25/00 was selling at 14 times sales and 300 times forward looking EPS). and 300 times forward looking EPS).

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Sample SelectionSample Selection

Sample consists of 529 tech and 142 non tech IPO firms that went public Sample consists of 529 tech and 142 non tech IPO firms that went public during 1996 through 2000.during 1996 through 2000.Technology firms are identified based on 4 digit SIC codes and internet Technology firms are identified based on 4 digit SIC codes and internet identifications using Loughran and Ritter criteria.identifications using Loughran and Ritter criteria.The non-tech sample is based on a random sample of 175 non-tech firms.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 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 IPO 10k’s from which we could determine whether or not the firm had a bank lending relationship.bank lending relationship.– Bank borrowing is defined narrowly to included loans or lines from Bank borrowing is defined narrowly to included loans or lines from

commercial banks and other depository institutions.commercial banks and other depository institutions.– Other debt (regardless of source) is classified as non-bank debt.Other debt (regardless of source) is classified as non-bank debt.

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Summary of Findings: Firm Summary of Findings: Firm CharacteristicsCharacteristics

Despite virtually no earnings and little collateral most (75%) Despite virtually no earnings and little collateral most (75%) tech firms have bank lending relationships.tech firms have bank lending relationships.Firms with banking relationships tend to be:Firms with banking relationships tend to be:– OlderOlder– More profitable More profitable – More likely to use VC financingMore likely to use VC financing– Rely less on non-bank sources of borrowing Rely less on non-bank sources of borrowing

Cash flows are a much Cash flows are a much less less important determinant the bank important determinant the bank lending for tech than for non-tech firms.lending for tech than for non-tech firms.– Mean and median EBITDA/Sales for tech firms with Mean and median EBITDA/Sales for tech firms with

banking relationships are -57% and -20% respectively. For banking relationships are -57% and -20% respectively. For non-tech firms the mean and median are 12.2% and 9.4%. non-tech firms the mean and median are 12.2% and 9.4%.

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Table 1

IPO Firm Summary Statistics

Panel A: Technology IPO firms

With Bank Relationships N=395

Without Bank Relationships N=134

Firm Characteristics Mean Median Mean Median Assets 57.86* 17.82* 30.62 14.62 Sales 39.6 17.09* 24.09 8.79 Age 8.58* 5* 6.13 4 Tangible/Assets 20.27% 14.62% 21.52% 13.87% Debt/Assets 28.25%* 18.31%* 20.48% 2.02% Other Debt/Assets 13.54%* 1.40% 20.48% 2.02% Interest Expense/Sales 7.46% 1.07% 9% 0.63% Operating Cash Flows to Sales -44.44%* -12.16%* -70.19% -30.92% EBITDA/Sales -51.05%* -20.07%* -74.77% -45.12% Industry Adjusted Operating Cash Flows /Sales

-43.9%* -13.71%* -69.85% -30.62%

Industry Adjusted EBITDA/Sales -26.27% -12.96%* -33.97% -32.02% VC Backing 70.89% 64.18% % Internet 59.75% 56.72%

Offer Characteristics Initial Return 62.38% 28.76% 75.61% 43.35% Offer Size 73.79 52.5 73.01 52.75 Underwriter Rank 8.18 8.1 8.22 8.1 Aftermarket Price to Sales Ratio 40.37* 12.75* 65.00 27.43

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Table 1 Cont’d. Panel B: Non-Technology IPO firms

With Bank Relationships N=86 Without Bank Relationships N=66 Firm Characteristics Mean Median High Low Mean Median High Low Assets ($ millions) 1031.07+ 43.04+ 76966.7 1.26 3812.88+ 36.38 217380 1 Sales($ millions) 198.91+ 52.67*+ 6902 1.69 654.36+ 36.14 22478 1.06 Age (Years) 17.36+ 10+ 80 0 13.81+ 6 80 0 Tangible/Assets 24.83% 18.67% 96.59% 0% 23.34% 13.84% 99.38% 0.05% Debt/Assets 48.97%* 48.84%*+ 157.22% 0% 26.48% 15.29% 112.31% 0% Other Debt/Assets 21.88% 13.87% 116.21% 0% 26.48% 15.29% 112.31% 0% Interest Expense/Sales 4.04%* 2.07%+ 235.47% 0.0% 9.14% 1.93% 80.06% 0.0% Industry-Adjusted Interest Expense/Sales 0.89% 0.71% 12.76% -30.59% 4.44% 0.02% 72.86% -35.01% Operating Cash Flows/ Sales 6.22%+ 6.04%+ 77.35% -200% -11.13% -3.23% 405.5% -200% EBITDA/Sales 12.2%*+ 9.4%*+ 63.56% -59.14% -14.65% 1.30% 76.17% -200% Industry-Adjusted Operating Cash Flows /Sales 2.58%+* 0.62%*+ 99% -170.35% -12.47%+ -2.7% 377.75% -196.56% Industry- Adjusted EBITDA/Sales 29.9%+* 1.31%+ 200% -64.02% -26.27%+ 0.71% 200% -200% VC Backing 29.07%+ 26.98%+ Offer Characteristics Initial Return 12.55%+ 9.97%+ 76.12% -10% 13.62% 6.25% 212.5% -12.4% Offer Size ($ millions) 80.61 44.7+ 1734 7 225.85 56 3657 5.3 Underwriter Rank 7.43+ 8.1+ 9.1 2.1 7.67 8.1 9.1 2.1 Aftermarket Price to Sales Ratio 10.67+ 3.17+ 126 .93 13.31+ 3.40+ 124 .65

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Summary of Findings: Who Lends to Summary of Findings: Who Lends to Tech Firms?Tech Firms?

Fifty four percent of tech lenders are boutique banks Fifty four percent of tech lenders are boutique banks (under $10 billion in assets)(under $10 billion in assets)– Silicon ValleySilicon Valley– Imperial (now Comerica)Imperial (now Comerica)

Borrowers from boutique lenders are smaller, younger Borrowers from boutique lenders are smaller, younger and less profitable than borrowers from large banks and less profitable than borrowers from large banks consistent with the function follows form argument.consistent with the function follows form argument.Median EBITDA/Sales for boutique lenders is -40.55% Median EBITDA/Sales for boutique lenders is -40.55% versus .33% for large banks. versus .33% for large banks. Boutiques are more likely to take equity (warrant Boutiques are more likely to take equity (warrant positions). Given IPO underpricing the value of these positions). Given IPO underpricing the value of these positions is significant.positions is significant.

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Why do Underwriting Standards Differ Why do Underwriting Standards Differ for Tech Firms?for Tech Firms?

Differences in Collateral? Differences in Collateral? Differences in Income Recognition and Differences in Income Recognition and Expenses?Expenses?Differences in the size and type of loan?Differences in the size and type of loan?Differences in the source of repayment?Differences in the source of repayment?

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Table 2

Characteristics of Lending Relationships

Technology N=395 Non-Technology N=86 Mean Median Mean Median Loan Amount (Millions) 13.41 1.3* 38.51 8.93 Commitment Amount (Millions) 21.67* 4.5* 50.27 11.6 Loan/Assets 14.71%* 7.61%* 27.1% 21.11% Commitment/Assets 31.31% 25.57% 34.64% 25.97% Loan/EBITDA 6 2.1 10.63 2.14 Loan/Net Worth .74* .11* 2.75 .68 Post-IPO Loan/Assets 1.62%* 0.0% 11.98% 0.0% % Secured 90.31% 88.31% % Positive EBITDA 34%* 79% % Positive Book Equity 57% 66% % With Bank Taking Equity Positions 20% 13%

*Significantly Different from the non-technology firms at the .05 level.

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Did Loan Underwriting Standards Did Loan Underwriting Standards Change During the Bubble?Change During the Bubble?

During the bubble, the median age of tech During the bubble, the median age of tech firms with banking relationships declined from firms with banking relationships declined from 7 to 5 years.7 to 5 years.The median industry adjusted EBITDA/Sales The median industry adjusted EBITDA/Sales for tech firms with banking relationships fell for tech firms with banking relationships fell from 1.8% pre-bubble to -41%. For non-tech from 1.8% pre-bubble to -41%. For non-tech firms the EBITDA/Sales firms the EBITDA/Sales increasedincreased from -.17% from -.17% pre-bubble to 4.54% during the bubble.pre-bubble to 4.54% during the bubble.Changes in lending at boutique lendersChanges in lending at boutique lenders

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Can Banks Spot Future Winners? Can Banks Spot Future Winners?

Do Banks Simply Establish Relationships With Firms With the Best Current Prospects 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?or are They Able to Identify Firms With the Best Future Prospects as Well?

Measures of Operating PerformanceMeasures of Operating Performance– EBITDA/Sales, OCF/SalesEBITDA/Sales, OCF/Sales– Industry Adjusted Performance (4 or 3 digit SIC code)Industry Adjusted Performance (4 or 3 digit SIC code)

Problem: Does not control for firm performance characteristics or mean Problem: Does not control for firm performance characteristics or mean reversionreversionControls of industry effectsControls of industry effects

– Barber Lyon--Peer AdjustedBarber Lyon--Peer Adjusted4digit SIC code within 10% of IPO firm performance4digit SIC code within 10% of IPO firm performance3 digit SIC code within 10% of IPO firm performance3 digit SIC code within 10% of IPO firm performance2 digit SIC code within 10% of IPO firm performance2 digit SIC code within 10% of IPO firm performance

– Two Problems with Barber-Lyons Peer adjustmentsTwo Problems with Barber-Lyons Peer adjustmentsMatches are hard to findMatches are hard to findDo they have banking relationships?Do they have banking relationships?

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19.01

33.65

11.69

18.31

Year 0

%

Year 1 Year 2 Year 3

Figure 1Difference in the Median Industry-Adjusted Performance of Bank Versus Non-Bank Technology Firms

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Industry Adjusted Difference in EBITDA/Sales Bank versus Non-Bank Non-Tech Firms

4.10%

6.54%

0.80%

1.77%

Year 0 Year 1 Year 2 Year 3

%

2020

Figure 2 Difference in Median Barber-Lyon Peer Adjusted Cummulative Growth in EBITDA/Sales of Bank and

Non-Bank Technology Firms

4.0%

0.13%

2.4%

-0.78%

-10.21%

-9.37%

0

Year 2 Year 1 Year 3

Bank

Non-Bank

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

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