The Time Series Convergence of Dispersion in Financial Analysts' Forecasts

164 Pages·2014·1.73 MB·English

Fredrik Le Bell
The Time Series Convergence of
Dispersion in Financial Analysts’
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Forecasts d
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Financial analysts play an important role in capital markets as informa- B
tion intermediaries. In filtrating information, resulting in earnings fore- e
casts, analysts generally tend to disagree. This thesis focuses on the disa- ll | T Fredrik Le Bell
greement between financial analysts. h
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Previous research and data indicate that when a company reports losses, Tim
analysts start disagreeing more about the future earnings of that compa- The Time Series Convergence of
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ny. Intuition would suggest that this is due to more uncertainty. Anecdotal S
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evidence from analysts earnings reports corroborates this intuition, find- r
ie Dispersion in Financial Analysts’
ing analysts more uncertain around negative earnings, precisely where s
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disagreement tends to increase. o
n Forecasts
However, the theoretical models for belief formation that lay the math- v
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ematical foundations for this thesis, incorporate a somewhat strange rg
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implication - If analysts start disagreeing more, it can only mean they n
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become more certain. In the theoretical setup, one that is used exten- e
sively in the literature, it is only asymmetric information that can give rise o
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to increased disagreement. D
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In order to resolve the certainty/uncertainty contradiction, this thesis e
r
shows that a model taking into account the public information flow in s
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earnings announcements over time, can produce only small levels of disa- n
greement between analysts, levels of disagreement that are too small to in
encompass observed levels of disagreement. F
in
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As a result, this thesis concludes that the theoretical models used in the n
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literature for explaining analyst disagreement, as such seem insufficient, ia
and increases in disagreement could instead be interpreted as increased l A
uncertainty, in accordance with evidence from analysts’ reports. n
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ly
The evidence in this thesis contributes to the Accounting literature, since s
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many studies employ these models the other way around, in that when s’ F
an increase in disagreement in empirical data is observed, the observed o
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disagreement is thought to signify an increase in asymmetric informa- c
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tion. Extensive reliance on the underlying models obscures our under- s
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practice, and are also extensively studied in the Accounting literature. 1
4
The results in this thesis indicate that conclusions in other studies regar-
ding increases in disagreement resulting from information asymmetry
might be premature.
9 789517 657624
Åbo Akademi University Press | ISBN 978-951-765-762-4
Fredrik Le Bell (born 1980)
- M.Sc. (Econ. & Bus. Adm.), 2008, Åbo Akademi University
- B.Sc. , 2008, Åbo Akademi University (Natural Sciences)
- B.Sc. (Econ. & Bus. Adm.), 2007, Åbo Akademi University
Åbo Akademi University Press
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E-mail: [email protected]
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E-mail: [email protected]
THE TIME SERIES CONVERGENCE OF DISPERSION IN
FINANCIAL ANALYSTS’ FORECASTS

The Time Series Convergence of
Dispersion in Financial Analysts’ Forecasts
Fredrik Le Bell
Åbo Akademis förlag | Åbo Akademi University Press
Åbo, Finland, 2014
CIP Cataloguing in Publication
Le Bell, Fredrik.
The time series convergence of
dispersion in financial analysts’
forecasts / Fredrik Le Bell. - Åbo :
Åbo Akademi University Press, 2014.
Diss.: Åbo Akademi University.
ISBN 978-951-765-762-4
ISBN 978-951-765-762-4
ISBN 978-951-765-763-1 (digital)
Painosalama Oy
Åbo 2014
Abstract
The standard Bayesian learning model under asymmetric information due to Barry
and Jennings (1992) and Barron, Kim, Lim and Stevens (1998), shows how dis-
persion in the forecasts made by financial analysts emerges as a function of the
uncertainties that the analysts are facing. A key implication arising from these
modelsisthatdispersioninforecastscanonlyincreaseduetoincreasedinformation
asymmetry.
This study expands the models of Barry and Jennings (1992) and Barron et al.
(1998) by explicitly considering the role that releases of common information, in-
terpreted as annual earnings releases, have on belief convergence. The study shows
thatlearningfromcommoninformationinafixedlearningregime,causesrapidcon-
vergenceofsubjectivebeliefs. Theconvergenceofbeliefsoncommoninformationin
turn dictates the magnitude of maximum dispersion that asymmetric information
can cause, resulting in a monotonically decreasing maximum amount of dispersion.
Empirical estimations using data on all US listed companies between 1995-2010
confirmthatobservedlevelsofforecastdispersionexceedtheoreticallyimpliedmax-
imumswhentakingintoaccounttheamountofcommonlyobservedinformationthat
hasbecomeavailablethroughearningsannouncements. Exceedanceoftheoretically
impliedmaximumsforforecastdispersionisprominentwhenacompanyexperiences
negative earnings.
Thejointevidenceofthestudysuggestthatlevelsofdispersioninanalysts’forecasts
aretoohightofindtheoreticalsupport. Consequentlyasymmetricinformationalone
cannot yield observed levels of forecast dispersion.
Theresultshaveintuitiveappeal,sincedispersionincreasingfromasymmetricinfor-
mation as in Barry and Jennings (1992) and Barron et al. (1998), implies increased
certainty. Theevidenceinthisstudyinsteadsuggeststhatdispersionresultingfrom
asymmetric information alone is not possible, and thus opens up possibilities for
interpreting increased dispersion as increased uncertainty. The study finally dis-
cusses potential pathways for such interpretations, involving agents restarting their
learningprocedures,oragentsactingasiftheconditioningdistributionisnon-fixed.
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Svensk sammanfattning
Spridning i prognoser gjorda av finansanalytiker anses vanligen kunna fo¨rklaras
med en Bayesiansk modell, utvecklad av Barry och Jennings (1992) samt Barron,
Kim, Lim och Stevens (1998). Modellen visar hur spridningen i prognoser uppst˚ar
som ett resultat av den os¨akerhet som analytikerna st˚ar inf¨or. Den huvudsakliga
fo¨ruts¨attningen fo¨r att spridning mellan analytikers prognoser skall kunna ¨oka, a¨r
att informationsasymmetri ¨okar.
Denna avhandling utvidgar modellerna av Barry och Jennings (1992) samt Barron
et al. (1998) genom att explicit beakta hur publikt tillg¨anglig information, infor-
mation som tolkas uppkomma i form av fo¨retags resultatrapporter, m˚aste p˚averka
konvergensen i analytikernas o¨vertygelser (beliefs). Avhandlingen visar att inla¨ran-
det som sker p.g.a. publik information i en fixerad Bayesiansk miljo¨ leder till en
snabb konvergens i subjektiva o¨vertygelser. Konvergensen i ¨overtygelser som sker
p.g.a. publik information visar sig i sin tur diktera den maximala ma¨ngd sprid-
ning som den asymmetriska informationen kan orsaka, och detta leder till att den
maximala spridningsma¨ngden m˚aste sjunka monotont o¨ver tiden.
De empiriska estimationerna i avhandlingen visar att den ma¨ngd spridning i prog-
noser som observeras i data ¨overstiger den teoretiska maximim¨angden fo¨r spridning
i prognoser, d˚a man beaktar den ma¨ngd publik information som blivit tillg¨anglig
genomfo¨retagensresultatrapporter. Detvisarsiga¨venatto¨vertra¨delserif¨orh˚allande
tilldeteoretiskamaximiniv˚aernaf¨orspridningiprognoserfo¨rekommerspecielltisit-
uationer da¨r ett fo¨retag rapporterar fo¨rluster. Data som anva¨nds a¨r Amerikanska
listade bolag under tidsperioden 1995 -2010.
Densammanlagdabevisma¨ngdeniavhandlingentyderp˚aattdeempirisktobserver-
adeniv˚aernafo¨rspridningianalytikerprognoser¨arfo¨rh¨ogafo¨rattkunnafinnast¨od
i de modeller som utvecklats fo¨r att fo¨rklara dem. Da¨rmed kan inte den asym-
metriska informationen som vanligtvis anva¨nds som en fo¨rklaring fo¨r en ¨okning i
spridningen av prognoserna, ensam˚astadkomma de niv˚aer i prognosspridning som
de facto observeras i data.
Resultatena¨rintuitivttilltalanded˚aeno¨kningiprognosspridningtillf¨oljdavasym-
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metrisk information, s˚a som i Barry och Jennings (1992) samt Barron et al. (1998),
samtidigt ¨aven innefattar att analytiker blir sa¨krare i sina o¨vertygelser d˚a sprid-
ningen i prognoser o¨kar. Resultaten ur denna avhandling ha¨vdar ista¨llet att prog-
nosspridningen inte enbart kan bero p˚a asymmetrisk information, och detta ¨oppnar
samtidigt mo¨jligheten till att i sj¨alva verket tolka en o¨kning i prognosspridning som
o¨kad osa¨kerhet. Slutligen diskuterar avhandlingen m¨ojliga banor f¨or tolkningar av
o¨kadprognosspridningsominvolveraro¨kados¨akerhet. Dettakanskegenomattana-
lytikertvingasstartaomsininla¨rningsprocessellergenomattanalytikernaanva¨nder
icke-fixerade sannolikhetsfo¨rdelningar i sin informationsma¨ngd.
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