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September 15, 2006. 6th Annual FDIC-JFSR Research Conference. Session on Information and Transparency. Discussion by Edward J. Kane Boston College. Central Issue: How Do Changes in Accounting Rules Affect Financial Institutions?.

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September 15, 2006

6th Annual FDIC-JFSR Research Conference

Session on Information and Transparency



Edward J. Kane

Boston College

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Central Issue: How Do Changes in Accounting Rules Affect Financial Institutions?

  • Does Enhanced “Accounting Disclosure” Increase Transparency and Stability?

  • Because the Degree of Information Asymmetry is a Managerial Decision Variable, Does Expanding Disclosure Requirements Merely Force Malevolent Insiders (Where They Exist) to Hide Threaded Needles of Information Inside an Even Larger Haystack of Disinformation?

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I Financial Institutions?. Paper by Rocco Huang

  • Huang focuses primarily on: the correlation between two proxy indices:

    1. Nier’s 17-variable index of a firm’s accounting informativeness (AI).

    2. LLorente, Michaely, Saar, and Wang’s index of private information trading (PIT).

  • And on how these variables correlate with bank-specificvariables and country-level indices of supervisory power(SP).

  • Omitted Variables: No consideration of country-level measures of supervisor’s enforcementtechnology or imposed legal penalties for violating focal laws and regulations. [Example of token $1 fines for exceeding 55 mph in Montana in 1970s and early 1980s.]

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MODELLING ISSUES Financial Institutions?

1. Only PIT is modelled as an endogenous variable; AI and SP are not.

2. PIT is the output of a prior regression , so it has its own error term. (PIT is the “slope shifter” in a regression of the form:

y = a + (b + cV)x + u.

Because coefficient standard errors are understated, conventional t-values greatly overstate the true significance of such variables.

3. PIT is aggressively interpreted. More simply, it is a measure of market liquidityper se.

4. Especially where insider trading is illegal, insiders would be smarter to do their trading in credit default swaps and other derivatives markets.

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Sampling and Interpretation Issues Financial Institutions?

1. Representativeness: What is the statistical population? Inclusion in Datastream and the benign macroeconomic era of 2003-2005 limits reach of findings.

2. Potential Heckman Bias: Most country-level variables may be conceived as resulting endogenously from sectoral bargaining in the political economy.

Given his marginal t-values, the author should test for this directly. For example, for reverse causation from AI to SP.

  • The deeper policy question is not only “Why does society regulate banks?”, but also “Why do banks in a given country permit themselves to be regulated and in particular ways?”

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NIER Paper Financial Institutions?

  • Uses a partly larger and partly smaller dataset than Huang.

    • More banks (550)

    • More years (1994-2000), but no overlapping.

    • Fewer countries (32). Huge sample sizes (N = upwards of 2500) raise issue of Lindley Paradox.

    • Same 17-Variable Disclosure Index (AI) as Huang.

    • Endogenous variable is a zero-one “individual-bank crisis indicator” c(i, t).

      Crisis is defined as a stock return in the 5% lower trail of the distribution of annual equity returns across all banks and years in the sample.

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Probit Model Financial Institutions?*of c(i, t)

Pr[c(i, t) = 1] is made a function of selected:

1. Macroeconomic Variables

2. Bank-Level Variable

3. Structural Variables

a. Transparency (lagged AI)

b. Deposit-Insurance Characteristics

4. Time Trend

* Hazard models could handle trends better.

[5. Endogeneity of AI is Investigated in a Two- Stage Framework. Evidence of Simultaneous- Equation Bias Emerges: Negative Coefficient Assigned to AI Becomes Almost Twice as Large!!]

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Main Findings: Financial Institutions?

  • AI Has Negative Sign

  • Unlimited Deposit Insurance Coverage Has a Positive Sign

    Main Criticism:

    Need to remove simultaneous-equation bias.

    Not only has AI and DI coverage been shown to be endogenous, but Hovakimian, Laeven, and a third author (JFSR, 2003; 390 banks, 56 countries, 1991-1999) show via a Heckman model that it is important to allow for the endogeneity of DI design features and link them to country characteristics before concluding anything about their effects on bank-level variables.

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Additional Criticisms Financial Institutions?

1. Need to set tougher significance levels for samples of this size.

2. Need to investigate the waste of stock-price information built into the indicator definition. Could try to explain GARCH-type models of return volatility instead.

3. Need to do more with simultaneity.

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Beatty Paper Financial Institutions?

  • Focuses on two 2003 changes in the Accounting Treatment of one Instrument: Trust Preferred Securities (TPS).

  • This instrument was invented specifically for banks as a device to lighten tax and regulatory burdens from capital requirement.

  • By itself, the first accounting change firmed up the legality of the tax benefit.

  • Neither change altered the regulatory benefit.

  • Issue: Did Accounting Change in How TPS Were Reported Affect Future TPS Issuance?

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Two Kinds of Statistical Tests Financial Institutions?

  • Differences in Means of Two-Year “Issuance Indicator” Before and After the Accounting Change For Subsamples of BHC That Fall on One Side or Another of Selected Conditioning Variables

    a. Sample Composition: Pre-Change N Post-Change N

    b. Results: Many differences are significant only in the pre-period.

    2. Aggregate Probit Regression of indicator on Predetermined Conditioning Variables, With Slope-Shifts For Post-Period.

    a. Sample included 905 issuances and 10,449 non-issuances.

    b. Results: Three-slope dummies are significant: Public debt and negative-tax positive. Four others are not significant.

Issuances: 806 107

Non-Issuances: 1,314 1,475

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Criticism: Post Hoc Ergo Proper Hoc Financial Institutions?

  • The paper produces evidence of a regime change in issuance. However, it does not show:

    1. That there is exactly one regime change and that 2003 is the best place to locate the switching point.

    2. Whether other changes in bank or investor environments (e.g., the effects of other tax and regulatory events) might help to explain the shift.