A great deal of the analysis of securities class action
lawsuit settlements revolves around measures of aggregate, average and median
settlement amounts. These data, while useful, are relatively unhelpful in
trying to anticipate the outcome of any particular case, particularly at the
outset. To try to develop a way to predict likely case outcome at the outset of
a securities class action lawsuit, four academics conducted a detailed
statistical analysis of securities class action settlements in order to
identify factors that affect outcomes.
In their April 30, 2012 paper entitled "Predicting
Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of
Federal Securities Class Action Lawsuits" (here),
Northwestern University Business Professor Blakeley
McShane, Juridigm Principal and Vice President Oliver Watson, U. Penn Law
Professor Tom Baker
and Fordham University Law Professor Sean Griffith set out to
create a "predictive model to forecast case outcomes based exclusively on
information available at the time the lawsuit is filed."
Their model, described in their paper, "estimates (i) the
probability of the settlement versus dismissal of a securities class action
lawsuit and (ii) the amount for which the class action will settle conditional
on the settlement."
A great deal of the authors' paper is devoted to a
description of the methodology used to derive the data on which their analysis
is based. Another significant part of the paper is devoted to a description of
their analytic methodology, which, as their title suggests, employs high level
statistical approaches and techniques. A detailed description of the authors'
data derivation and statistical methodologies is beyond the scope of this blog
(which is another way of saying that I know my limits).
For purposes of understanding the authors' conclusions,
it is useful to note that the authors derived a data set of nearly 1200
securities class action lawsuits and associated case resolutions. Among other
critical steps taken to derive their data set, the authors focused exclusively
on cases filed post-PSLRA that were filed five years or more before the
starting date of their analysis. (The five year cut-off was used to ensure the
likelihood that the cases in the data set had been finally
resolved). Essentially, the authors looked at cases filed between 1996 and
2005 that otherwise survived the authors' filters and sorting criteria.
The authors also derived several of their own measures
using variety of data sources. For example, in order to determine the
"notoriety" or "newsworthiness" of a particular company or case, the authors
considered the number of Google News Archive hits associated with the company
in the year prior to the lawsuit filing.
Using these and other data points and applying selected
statistical methods to develop their model, the authors identified a number of
variables predictive of whether a case is settled or dismissed, and variables
predictive of the settlement amount if a case is settled.
The variables the authors identified that indicate that a
case will most likely settle include "a number of classes or types of
securities associated with the case, a higher return on the S&P 500 during
the class period, whether or not GAAP violations were alleged and having an
individual plaintiff listed." Factors that indicate that a case is less likely
to settle (that is, more likely to be dismissed) include "longer filing times,
higher market capitalization, a higher company return during the class period,
having an institutional plaintiff listed, and greater public notoriety (as
measured by the number of Google hits in the year prior to filing)."
The variables the authors found that positively impact
the settlement amount include "the total number of securities, the length of
the class period, market capitalization, the company return during the class
period, whether or not earnings were restated, whether or not the case was a Securities
Act Section 11 case, whether or not insider trading was alleged, the existence
of an institutional plaintiff, and the number of Google hits." Factors
associated with lower settlement amounts include "longer filing times and not
having an institutional investor listed (i.e., having only an individual
plaintiff listed or having no plaintiff listed)."
The authors also found that though GAAP cases are more
likely to settle, the GAAP cases that do settle do not have higher settlement
amounts. The authors speculate that this is likely due to the fact that an
allegation of a GAAP violation significantly bolsters the merits of the case,
which increases the chances the case will survive a dismissal motion. The
authors suggest that this makes it more appealing for plaintiffs to take on a
GAAP violation case even if the potential damage award is relatively low.
At the same time, Rule 10b-5 cases are less likely to
settle (that is, more likely to be dismissed) but those that do settle have
higher settlement amounts. The authors attribute this to the greater damages
available to Rule 10b-5 plaintiffs. The authors suggest that plaintiffs
rationally might be willing to pursue cases with a lower survivability
probability when the cases are likelier to have larger settlements, assuming
the cases survive dismissal. Cases without institutional plaintiffs are more
likely to survive motions to dismiss, which the authors interpret to suggest
both that institutional investors select the high potential value cases and that
plaintiffs' lawyers exercise more care regarding the merits of cases with only
an individual plaintiff.
The authors also noted a number of differences among the
various circuits and industries. For example, the authors note that the
eleventh circuit appears to have modestly lower settlement amounts whereas the
ninth and tenth circuits have modestly higher settlement amounts. Similarly,
utilities have somewhat higher settlement amounts.
I have necessarily summarized here the authors' much more
detailed analysis. The only way to fully understand and appreciate the authors'
predictive analysis, as well as the ways in which the authors' conclude that
the various factors are predictive, is to read their paper in full, which I
I do note that the ability to predict case outcomes at
the outset is important for a number of process participants, including in
particular the affected D&O insurers. Among other things, D&O insurers
must have reliable means to assess and predict case outcomes at the outset in
order to try and set case reserves appropriately. In addition, D&O insurers
whose coverage attaches only in the excess layers will want to be able to
assess cases at the outset in order to try to determine the likelihood that losses
associated with any particular claim will penetrate their attachment point. For
the involved D&O insurers, the authors' predictive model could provide a
The authors' model could prove a useful tool for the
defendant companies themselves as well as for their defense counsel. It is
critically important for companies and their counsel in setting their
litigation strategy to have an accurate understanding of the seriousness of the
claim. The authors' model may provide a useful way for companies and
their counsel to make a realistic assessment of the seriousness of the case in
order to try to set defense strategy appropriately.
If I were to make one suggestion to the authors in order
to make their analysis more accessible, it would be to expand their summary
description of the relevant factors so that the factors are not only identified
but also so that the nature of their relevance is more apparent. For example,
it is of course important for the authors to state in the summary of their
conclusions that, for example, "the length of the class period" is a relevant
factor positively impacting settlement. It would be even more helpful for the
non-mathematician reader for the authors to explain in the conclusion section
how the variation of the length of the class period affects the settlement
(that is, is it a shorter or a longer class period that positively affects the
settlement?). A more detailed explanation in the paper's discussion section of
the authors' specific conclusions with respect to each of the identified
factors would make the authors' otherwise somewhat intimidating paper more
approachable to a wider variety of readers and would make the authors'
conclusions both clearer and more useful for those trying to understand the
implications of the authors' analysis.
I would like to thank Professor Tom Baker for providing
me with a copy of this interesting paper.
other items of interest from the world of directors & officers liability,
with occasional commentary, at the D&O Diary, a blog by Kevin LaCroix.
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