Bob DeYoung’s Discussion FDIC Fall Conference, “Does the Stock Market Value Bank Diversification?” Baele, De Jonghe, and Vander Vennet. 2.“Product Diversification in the European Banking Industry: Risk and Loan Pricing Implications,” Lepetit, Nyes, Rous, and Tarazi.

1. Baele, De Jonghe, and Vander Vennet  Test whether market assessments of risk and bank value are related to “diversification.”  Data from 17 European countries,  “Functional diversification” measures: NII share of revenue; Loan share of assets. Diversity of Revenues (NII vs. other) and Assets (Loans vs. other); measures range from 0 to 1.  Risk measures: Market model parameters.  Bank value (franchise value) measure: Noise-adjusted Tobin’s Q (adapted from Hughes, Lang, Mester, and Moon, 1999).

Main results (1)  Franchise value increases with both NII Share and Revenue Diversity. “Level” result and “Diversity” result are really the same result, because mean NII is well less than.50. Positive Q-NII relationship may be spurious. Many fee-based activities are off-balance sheet; if so, they show up in Q numerator but not in Q denominator.  “Stock market thinks NII levels and Revenue Diversity are signals of future growth in earnings.” Okay. But why? How?  A result that is not emphasized: Q increases with Beta. This establishes a positive Risk-Return tradeoff.

Main results (2)  Systematic Risk increases with NII. But Idiosyncratic Risk decreases with NII. Positive NII-Beta relationship is now a stylized fact. On average, NII increases banks’ exposure to the business cycle and macro-economy. WHY?? Negative NII-Idiosyncratic Risk finding less robust in this literature. WHY?? (No explanation in paper.)  Another under-emphasized result: Beta increases with Loans-to-Assets. WHY? (No discussion in the paper)  Authors remind us that implications of results vary for investors, managers, and regulators.

Franchise Value Measure (1) Objective: Create a more accurate measure of Q by removing “noise” from market prices.  Q = Mkt Val / Bk Val  Estimate market value frontier: ln(Mkt Val) = a + b*ln(Bk Val) + c*ln(Bk Val) 2 + random error + ineff.  Set random error=0, and solve for Frontier Mkt Val.  Q NA = Frontier Mkt Val / Bk Val*exp(ineff)  When ineff=0: exp(ineff) = 1 so franchise value Q is maximized.  When ineff>0: exp(ineff) > 1 so franchise value Q falls short of maximum.

Franchise Value Measure (2)  This may yield an improved measure of franchise value. But it is very unorthodox. Need more intuition in text to help finance reader.  Is Q NA an ad hoc construct? Or can this measure be formally derived?  Q NA versus Q: Authors argue Q NA is theoretically superior to Q. Authors show correlation(Q NA, Q) is high. Can authors demonstrate superiority of Q NA ?

2. Lepetit, Nys, Rous, and Tarazi  Test whether bank risk and loan pricing are related to “diversification.”  Data from 11 European countries,  “Diversification” measures: NII, Commissions, and Trading shares of revenue.  Risk measures: Accounting ratios; Market measures; Pr(insolvency).  Loan pricing measures: Interest rate margins; Risk spreads.  A story to interpret results: A strategic link between increased NII and lending practices.

Main results  Most measures of risk are positively associated with NII and Commission Income. Uncontrolled tests, not regressions. QUESTION: What’s included in “Commission Income?”  Loan interest margins are negatively associated with NII (multivariate regressions).  Proposed Story: These two results consistent with each other if banks are pursuing the following strategy: Banks use loans as a “loss leader” to attract sales of fee-based financial services.

Loss Leader interpretation  I am unconvinced. More proof is required: Is there a link between loan customers and fee-based customers? Are returns from marginal fee-based activities large enough to offset reductions in infra-marginal spreads? Is there anecdotal or case-study evidence from bankers to corroborate the story?  DeYoung, Hunter, Udell (2003) story: New technology and deregulation led to new strategy. Credit scoring and asset securitization  high NII. Loans become commodities  low loan spreads. Geographic freedom  provides necessary scale.

Common to both papers (1)  High level of NII used as a proxy for “diversification,” but this is a poor proxy. NII can increase without a change in product mix (e.g., loan securitization, back-up credit lines). DeYoung and Rice (2004): Correlation between ROE and Net Interest Income grew stronger in U.S. during 1990s, even as NII was increasing.  Both papers produce clean statistical results, but discussion is “sterile.” Little interpretation of economics driving the results. These are not standard, multi-industry finance tests. These are industry-specific tests. Extant literature contains some plausible stories.

Common to both papers (2)  DeYoung and Roland (1999) offer a story (and some evidence) consistent with the positive NII-Beta results.  Traditional banking: Bank-Borrower Relationships create switching costs that stabilize income across business cycle.  Fee-based (transactions) banking: Fewer relationship-based switching costs. Fee-based activities can be quite pro-cyclical (e.g., securities brokerage, mortgage banking). Fee-based activities can require higher operating leverage, which amplifies revenue volatility.

A conflicting result across the papers  Lepetit, et al: Positive NII-idiosyncratic result. Short time frame. Uncontrolled tests.  Baele, et al: Negative NII-idiosyncratic result. L onger time frame. Controlled regression tests.  What would we expect? NII from market-sensitive or non-relationship-based actitivies  Risk mainly systematic  NEGATIVE. NNI from relationship-based activities  Risk will be less systematic  POSITIVE.

Bob DeYoung’s Discussion FDIC Fall Conference, “Does the Stock Market Value Bank Diversification?” Baele, De Jonghe, and Vander Vennet. 2.“Product Diversification in the European Banking Industry: Risk and Loan Pricing Implications,” Lepetit, Nyes, Rous, and Tarazi.