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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6th Annual FDIC-JFSR Research Conference
Edward J. Kane
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).
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.
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.
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.
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!!]
Main Findings: Financial Institutions?
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.
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.
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
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.