Understanding earnings quality - MIT Sloan School of Management
Understanding earnings quality - MIT Sloan School of Management
Understanding earnings quality - MIT Sloan School of Management
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Exhibit 1: Summary <strong>of</strong> <strong>earnings</strong> <strong>quality</strong> proxies<br />
This exhibit lists the commonly used proxies for <strong>earnings</strong> <strong>quality</strong> and the most common specification(s) <strong>of</strong> the variable. The exact specification <strong>of</strong> the proxies<br />
can vary by study. For each, we summarize the theory for its use as a measure <strong>of</strong> <strong>quality</strong> and an abbreviated summary <strong>of</strong> the strengths and weaknesses <strong>of</strong> the<br />
proxy. Details are in the review in Section 3.<br />
Empirical proxy Theory Strengths and weakness<br />
Persistence<br />
Earningst+1=α+ βEarningst + εt<br />
β measures persistence.<br />
Smoothness<br />
σ(Earnings)/ σ(Cash flows)<br />
A lower ratio indicates more<br />
smoothing <strong>of</strong> the <strong>earnings</strong> stream<br />
relative to cash flows.<br />
Timely loss recognition (TLR)<br />
Earningst+1=α0+α1Dt+β0Rett<br />
+β1DtRett +εt<br />
where D = 1 if Ret < 0. Higher β1 is<br />
greater TLR.<br />
Benchmarks<br />
∗ Kinks in <strong>earnings</strong> distribution<br />
∗ Changes in <strong>earnings</strong> distribution<br />
∗ Kinks in forecast error distribution<br />
∗ String <strong>of</strong> positive <strong>earnings</strong> increases<br />
Firms with more persistent <strong>earnings</strong><br />
generate more accurate DCF-based<br />
equity valuations.<br />
Managers opportunistically smooth<br />
<strong>earnings</strong>. Therefore, greater<br />
smoothness is artificial relative to<br />
the fundamental process; or,<br />
smoothness reduces noisy variation<br />
in cash flows as a measure <strong>of</strong> the<br />
process.<br />
There is a demand for TLR to<br />
combat management’s natural<br />
optimism. TLR represents high<br />
<strong>quality</strong> <strong>earnings</strong>.<br />
Unusual clustering in <strong>earnings</strong><br />
distributions indicates <strong>earnings</strong><br />
management around targets.<br />
Observations at or slightly above<br />
targets have low <strong>quality</strong> <strong>earnings</strong>.<br />
172<br />
Pros: Fits well with a Graham and Dodd view <strong>of</strong> <strong>earnings</strong> as a summary<br />
metric <strong>of</strong> expected cash flows useful for equity valuation.<br />
Cons: Difficult to control for persistence <strong>of</strong> the fundamental <strong>earnings</strong> process,<br />
but this persistence is likely to be a large contributor to persistence <strong>of</strong> reported<br />
<strong>earnings</strong>. Thus, it is difficult to make statements about the effect <strong>of</strong><br />
measurement on persistence. Greater persistence may be achieved via<br />
opportunistic <strong>earnings</strong> management.<br />
Pros: In cross-country tests, measures <strong>of</strong> artificial smoothness appear to<br />
capture meaningful variation in <strong>earnings</strong> management.<br />
Cons: It is difficult to disentangle smoothness <strong>of</strong> reported <strong>earnings</strong> that<br />
reflects smoothness <strong>of</strong> the fundamental earning process from artificial<br />
smoothing.<br />
Pros: Aimed at disentangling the measurement <strong>of</strong> the process from the<br />
process itself.<br />
Cons: The net effect <strong>of</strong> TLR on <strong>earnings</strong> <strong>quality</strong> is unknown because TLR<br />
results in lower persistence during bad news periods than during good news<br />
periods (Basu, 1997). Both persistence and TLR affect the decision<br />
usefulness <strong>of</strong> <strong>earnings</strong>. TLR is a returns-based metric, see comments on<br />
ERCs.<br />
Pros: The measure is easy to calculate, the concept is intuitively appealing,<br />
and survey evidence suggests <strong>earnings</strong> management around targets.<br />
Cons: In addition to statistical validity issues, evidence that kinks represent<br />
opportunistic <strong>earnings</strong> management is mixed, with credible alternative<br />
explanations including non-accounting issues (e.g., taxes). It is difficult to<br />
distinguish firms that are at kinks by chance versus those that have<br />
manipulated their way into the benchmark bins.