Kadam, Su esh; Se hi, Madh i
A icle
Ta ge p ice accu acy o sell-side analys s: e idence om
India
Cogen Economics & Finance
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Sugges ed Ci a ion: Kadam, Su esh; Se hi, Madh i (2024) : Ta ge p ice accu acy o sell-side analys s:
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Ta ge p ice accu acy o sell-side analys s:
e idence om India
Su esh Kadam & Madh i Se hi
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FINANCIAL ECONOMICS | RESEARCH ARTICLE
Ta ge p ice accu acy o sell-side analys s: e idence om India
Su esh Kadam
a,b
and Madh i Se hi
a
a
Symbiosis Ins i u e o Business Managemen (SIBM), Symbiosis In e na ional (Deemed Uni e si y) (SIU), Bengalu u,
Ka na aka, India;
b
School o Comme ce & Managemen , D. Y. Pa il In e na ional Uni e si y (DYPIU), Pune, Maha ash a,
India
ABSTRACT
Ta ge p ices o ecas ed by sell-side equi y esea ch analys s play a c ucial ole in ma -
ke pa icipan s’in es men decisions. We, using a la ge sample o Indian ma ke s,
de e mine du ing he pe iod and end o he pe iod 12-mon h ahead a ge p ice
achie emen s, examine he e ec i eness o alua ion me hods o de e mining a ge
p ices, e alua e a ge p ice accu acy using p edic ion e o me ics, and in es iga e
he ac o s in luencing a ge p ice accu acy. Ou indings indica e ha sell-side ana-
lys s ha e easonable o ecas ing abili ies, achie ing 63% o hei a ge p ices o e a
12-mon h o ecas ing ho izon. The le el o achie emen dec eased wi h inc easing
op imism in p edic ions. Analys s gene ally p e e holis ic and mul iple-based alua ion
app oaches o de e mine he a ge p ices. The DCF me hodology was less e ec i e
han he SOTP hyb id and mul iple-based app oaches in p edic ing a ge p ices. We
ind ha mo e op imis ic a ge p ices and highe be a con ibu e o inc eased p edic-
ion e o s, whe eas be e ma ke e u ns educe e o s. Analys s s uggle o p edic
p ices o loss-making en e p ises, and ha e di icul y o ecas ing a ge p ices in cap-
i al-in ensi e sec o s. These indings con ibu e o he exis ing body o knowledge and
ha e signi ican implica ions o s akeholde s in inancial ma ke s.
IMPACT STATEMENT
The s udy examines he accu acy o analys s’ a ge p ice o ecas s and he ac o s in lu-
encing ha accu acy. We ind ha analys s demons a e easonable o ecas ing abili ies,
wi h 63% o hei a ge p ices being accu a e wi hin a 12-mon h pe iod. Ou esul s
sugges ha analys s and b oke age i ms should u ilize mo e igo ous alua ion models,
such as he Sum o he Pa s (SOTP) Hyb id, when se ing a ge p ices, whene e ele-
an . In es o s should be awa e o he limi a ions linked o analys s’s ock p ice o ecas s,
pa icula ly in capi al-in ensi e indus ies, and should be cau ious when conside ing
o e ly op imis ic a ge p ice ecommenda ions, especially o small-cap o loss-making
companies. Ou insigh s in o he impac o alua ion me hods and capi al in ensi y on
a ge p ice accu acy con ibu e new knowledge o he exis ing li e a u e.
ARTICLE HISTORY
Recei ed 16 July 2024
Re ised 14 Oc obe 2024
Accep ed 25 Oc obe 2024
KEYWORDS
Ta ge p ices; analys
ecommenda ion; equi y
esea ch; alua ion
me hods; analys epo s;
a ge p ice accu acy
SUBJECTS
Finance; Business;
Managemen and
Accoun ing; Economics
1. In oduc ion
Analys s play an impo an ole in dissemina ing aluable in o ma ion o he ma ke pa icipan s (B. Ba be
e al., 2001). A ypical sell-side analys equi y esea ch epo includes an ea nings o ecas , ecommenda ion
( o example buy, hold, sell), and a ge p ice. P e ious esea ch has ound ha hese ecommenda ions a e
impo an o sha e p ice disco e y (Asqui h e al., 2005). Gi en ha he o al ma ke capi aliza ion o domes ic
companies lis ed on s ock exchanges wo ldwide is es ima ed a 112 illion USD as o July 2023,
1
equi y esea ch
i ms spend billions o dolla s annually analyzing companies and publishing esea ch epo s o in es o s. O e
he yea s, academic esea ch has been de o ed o analyzing he alue, impac , and accu acy o analys s’ epo s.
One aluable measu e ha eme ged om hese epo s was he a ge p ice. The a ge ep esen s he po en ial
change in secu i y alue and may ha e an impac on in es o s’in es men decisions.
CONTACT Madh i Se hi [email p o ec ed] Symbiosis Ins i u e o Business Managemen , Symbiosis In e na ional (Deemed
Uni e si y) (SIU), Elec onics Ci y, Hosu Road, Bengalu u - 560100, Ka na aka, India
ß2024 The Au ho (s). Published by In o ma UK Limi ed, ading as Taylo & F ancis G oup
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion License (h p://c ea i ecommons.o g/licenses/by/4.0/), which
pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly ci ed. The e ms on which his a icle has been
published allow he pos ing o he Accep ed Manusc ip in a eposi o y by he au ho (s) o wi h hei consen .
COGENT ECONOMICS & FINANCE
2024, VOL. 12, NO. 1, 2423261
h ps://doi.o g/10.1080/23322039.2024.2423261
Analys s o en p o ide a ge p ices o suppo hei ecommenda ions (B adshaw, 2002) and a e co -
ela ed wi h alue- ele an undamen als, such as ea nings expec a ions (F ankel & Lee, 1998). Ta ge p i-
ces signi ican ly a ec ma ke p ices (B a & Leha y, 2003) and in es o s conside he a ge p ice
o ecas o be aluable (Asqui h e al., 2005). Academic esea ch was la gely silen on a ge p ices, a
ac mainly a ibu ed o he la e co e age o a ge p ices by majo da abases (Ke l, 2011). P e ious
s udies o analys epo s ha e ocused on he impac o ecommenda ions and ea nings o ecas on s ock
p ices and he accu acy o ea nings o ecas s, along wi h s udies de e mining he ac o s o such an
impac . Analys ecommenda ions we e ound o gene a e abno mal e u ns and ou pe o m bench-
ma ks (B. M. Ba be & Loe le , 1993; Beneish, 1991; Desai e al., 2000; Womack, 1996). Analys co e age
p omo es co po a e inno a ion (Zhang & Wang, 2023). Recommenda ions a e expec ed o ha e a g ea e
p ice impac i hey a e accompanied by long- e m ea nings g ow h o ecas s (Jung e al., 2012; S ickel,
1995). Be e o ecas accu acy esul ed in mo e p o i able ecommenda ions (Hall & Tacon, 2010). La ge-
sample s udies ac oss he US (Asqui h e al., 2005; B adshaw, B own, e al., 2013), Ge man (Ke l, 2011)
and I alian (Bonini e al., 2010) ma ke s ha e documen ed a ious le els o p edic i e abili y o analys s
and ac o s impac ing analys accu acy.
This s udy aims o expand and con ibu e o he exis ing esea ch on he accu acy o a ge p ice
achie emen by sell-side equi y esea ch analys s. The e o e, ou s udy add esses he ollowing esea ch
ques ions: (i) Do sell-side analys s ha e supe io abili y o p edic a ge p ices? (ii) E ec i eness o he
choice o alua ion me hod used by analys s in p edic ing a ge p ices, (iii) impac o ecommenda ion
classes on a ge p ice accu acy, and (i ) de e minan s o a ge p ice accu acy. Ou sample consis s o
22,807 analys ecommenda ions o he pe iod o i e yea s om 1 Janua y 2016 o 31 Decembe 2020,
om 34 equi y esea ch i ms o 805 companies, co e ing abou 95% o he o al ma ke cap o all
lis ed companies in India. All epo s we e indi idually analyzed o cap u e he necessa y da a poin s
equi ed o empi ical analysis. Ve y ew s udies ha e been conduc ed on analys s’ ecommenda ions in
he Indian ma ke , and hose ha exis ha e used small and es ic i e samples (Cha e jee e al., 2020;
Pa el, 2021; Sayed, 2015; Sayed & Chaklade , 2014). Ou esea ch expands he exis ing li e a u e on he
analys s a ge p ices o Indian s ock ma ke .
Ou empi ical analysis was conduc ed in i e s ages: Fi s , we de e mine he a ge p ice achie emen s
o all ecommenda ions and u he analyze each ecommenda ion class (ie s ong buy, buy, hold, sell,
s ong sell’) a he end o he 12-mon h pe iod om he ecommenda ion da e and any ime du ing he
12-mon h pe iod. We also obse e he c oss-sec ional achie emen o he a ge p ice along he ma ke
cap o companies (g ouped as la ge-cap, mid-cap, and small-cap) and sec o s. Second, we s udy he
alua ion me hods analys s use o a i e a he a ge p ice and ca ego ize hem. We s udy he e ec i e-
ness o a ious alua ion me hods o p edic ing a ge p ices. Thi d, we use he accu acy me ic model
de eloped by Bonini e al. (2010), om he pe spec i e o an in es o , o es a ge p ice accu acy.
Fou h, we s udied he impac o ecommenda ion class on accu acy. Fi h, we analyze he ac o s ha
de e mine he a ge p ice accu acy.
Ou indings demons a e ha he achie emen o he a ge p ice su passes he majo i y o p e-
iously documen ed esul s in he li e a u e (Asqui h e al., 2005;Boninie al.,2010;B adshaw&
B own, 2006;Ke l,2011) and is in line wi h B adshaw, B own, e al. (2013) wi h 63% achie emen
du ing he 12-mon h pe iod and 38% a he end o he 12-mon h pe iod, indica ing easonable p e-
dic i e abili ies o analys s. Achie emen dec eases wi h op imis ic p edic ions (‘s ong buy’,‘s ong
sell’). Analys s p e e holis ic and mul iple-based alua ion app oaches o a i e a he a ge p ices.
The DCF me hodology has limi ed e ec i eness, whe eas he sum o he pa s (SOTP) hyb id and
mul iples-based app oaches o alua ion a e highly e ec i e in p edic ing a ge p ices. The ecom-
menda ion classes signi ican ly impac a ge p ice accu acy. Ex-pos ma ke e u ns, i m be a, DCF
alua ion me hodology, and business capi al in ensi y con ibu e o p edic ion e o s. Ou indings
on he e ec s o alua ion me hods and capi al in ensi y on a ge p ice accu acy a e a new addi ion
o he li e a u e.
The es o he pape is o ganized as ollows: Sec ion 2 e iews p io esea ch; Sec ion 3 ou lines he
hypo heses; Sec ion 4 co e s da a collec ion; Sec ion 5 de ails he me hodology; Sec ion 6 p esen s he
esul s; and Sec ion 7 concludes he pape .
2 S. KADAM AND M. SETHI
2. Li e a u e e iew
2.1. Ma ke impac o analys ecommenda ions
Analys s play a i al ole in dissemina ing in o ma ion o inancial ma ke s in he o m o ea nings o e-
cas s, ecommenda ions (such as buy, hold, and sell), and a ge p ices. Ea lie esea ch ocused mainly
on ea nings o ecas s and s ock ecommenda ions.
Cowles (1933) no ed ha s ock ma ke o ecas e s ail o gene a e abno mal e u ns. Howe e , abno -
mal e u ns we e documen ed by Beneish (1991) and B. M. Ba be and Loe le (1993). Womack (1996)
no ed ha pos - ecommenda ion excess e u ns a e no mean e e ing. Desai e al. (2000) also
obse ed ha s ocks ecommended by All S a analys s in he Wall S ee Jou nal ou pe o med bench-
ma ks con olled o size and indus y. Se e al s udies (Jegadeesh & Kim, 2006; Moshi ian e al., 2009;
Womack, 1996) ha e no ed ha analys s publish mo e buy ecommenda ions han sell ecommenda-
ions. S ickel (1995) ound ha downg ades ha e a g ea e nega i e impac han upg ades. B adley e al.
(2014) no ed ha he ma ke eac ion o he con a ian ( ecommenda ion in opposi e di ec ion o ecen
p ice mo emen ) upg ades and downg ades is mo e han non-con a ian, concluding ha con a ian
upg ades a e expec ed o ha e p i a e in o ma ion. Analys ecommenda ion changes a e mo e likely o
be in luen ial i hey a e om leade s, s a s, p e iously in luen ial analys s, issued away om consensus,
accompanied by ea nings o ecas s, and issued on g ow h, small, high ins i u ional owne ship, o high
o ecas dispe sion i ms (Loh & S ulz, 2011).
Con a y o wha has been obse ed in o he s udies, B. Ba be e al. (2003) poin ou ha om 2000 o
2001, s ock ecommenda ions om analys s pe o med wo se han s ocks leas a o ed by analys s. Loh and
S ulz (2011) s udied whe he indi idual ecommenda ions a e in luen ial and documen ed ha only 12% o
he ecommenda ion changes a e in luen ial. Michaely and Womack (1999) obse ed ha unde w i e s’ ec-
ommenda ions a e biased and, in he long un, in e io o hose o non-unde w i e s. Jegadeesh and Kim
(2010) no iced ha analys s he d a ound his consensus. Chan e al. (2018) no iced ha abou 56% o ana-
lys s e mina ed hei owne ship o he s ock while ha ing ou s anding buy ecommenda ions.
2.2. Ta ge p ice
Al hough a ge p ice is one o he key componen s o analys esea ch ou pu , he ocus has ecen ly
shi ed owa d a ge p ices. Ke l (2011), based on he in o ma ion aken om B a and Leha y (2003)
and o he s udies (Asqui h e al., 2005; B adshaw, B own, e al., 2013; Gleason e al., 2013), a ibu ed
his la e in e es in a ge p ices o he ac ha majo da abases such as Fi s Call om Thomson
Financial began co e age o a ge p ices only a he end o 1996.
B a and Leha y (2003) ound ha a ge p ices signi ican ly a ec ma ke p ices and concluded ha a -
ge p ice in o ma ion o ma ke pa icipan s is inc emen ally in o ma i e beyond ea nings o ecas s and s ock
ecommenda ions. Acco ding o B adshaw (2002), analys s use a ge p ice jus i ica ions in a majo i y o hei
epo s. Addi ionally, highe a ge p ices a e linked o mo e a o able ecommenda ions.
Asqui h e al. (2005) pe o med a de ailed s udy o a ge p ices. The au ho s analyzed he achie e-
men o a ge p ices du ing he 12 mon hs pos - ecommenda ion and no ed ha p ice o ecas s we e
achie ed in 54.28% o all cases. They documen ed o e shoo ing o achie ed a ge p ices by 37.27%
and unde shoo ing in he case o unachie ed a ge p ices by 15.62%. Asqui h e al. (2005) no ed ha
he a ge p ices p o ide aluable in o ma ion o he ma ke . Ke l and Wal e (2008) p o ides simila e i-
dence o he Ge man ma ke .
Bonini e al. (2010) de eloped an accu acy me ic o a ge p ice accu acy and es ed i in he I alian ma -
ke . They no ed 20.0% (end o pe iod) and 33.12% (any ime du ing he pe iod) accu acy o a ge p ices
wi h p edic ion e o s o up o 36% and concluded ha o ecas ing accu acy o analys s is e y limi ed and
a ge p ices a e sys ema ically biased. Ke l (2011) no ed a a ge p ice accu acy o 56.53% du ing his pe iod
and ound a nega i e co ela ion be ween accu acy and analys op imism in he Ge man ma ke .
B adshaw, B own, e al. (2013) examined o e all and indi idual analys accu acy and no ed ha , on
a e age, 38% (end o pe iod) and 64% (any ime du ing he pe iod) o analys s’ a ge p ices a e me .
They concluded ha he equency o accu a e p edic ion is low, wi h an absolu e a ge p ice o ecas
e o o 45%, and a ibu ed he lack o accu acy o he ac ha a ge p ice o ecas ing was mos ly an
COGENT ECONOMICS & FINANCE 3
unmoni o ed ac i i y. In hei wo king pape (B adshaw & B own, 2006) o he sample pe iod om 1997
o 2002, he accu acy no ed was 24% (end o he pe iod) and 45% (any ime du ing he pe iod).
Gleason e al. (2013) s udied analys s’ alua ion echniques o se ing a ge p ices and no ed ha a ge
p ice accu acy imp o es wi h mo e igo ous alua ion echniques han wi h simple heu is ics. In a simila s udy
on alua ion echniques, E kile e al. (2022) no e ha he income and ma ke app oach leads o mo e accu a e
p ices along wi h holis ic alua ion, ins ead o he sum o pa s alua ion. Bonini e al. (2022) obse ed ha a -
ge p ice o ecas s ha di e om hose o he mul iple-based alua ion app oach a e mo e accu a e.
B adshaw e al. (2019) a gued ha analys s in coun ies wi h s ong ins i u ional amewo ks p o ide mo e
alue- ele an a ge p ices. An ^
onio e al. (2017) also showed ha accu acy inc eases wi h g ea e go e nmen
e ec i eness and g ea e analys consensus in a La in Ame ican s udy. Bou eska and Mili (2022) obse ed ha
analys s issue mo e accu a e ecommenda ions o well-go e ned i ms and ha s ong co po a e go e nance
imp o es a ge p ice accu acy (Cheng e al., 2019). Uma e al. (2022) no ed ha ESG sco es posi i ely impac ed
a ge p ice accu acy. Uma e al. (2023) obse ed ha inancial es a emen s nega i ely impac ed a ge p ice
p ecision. F edj and Gana (2023) obse ed ha a ge p ice accu acy is nega i ely ela ed o boa d independ-
ence. Ins i u ions closely ollow analys ecommenda ions and end o o e eac o analys a ge p ice e isions,
he eby des abilizing s ock p ices due o he ding beha io (Gu e al., 2022).
In he Indian ma ke con ex , Sayed and Chaklade (2014) analyzed 1000 a ge p ices wi h buy a -
ings and documen ed a a ge p ice accu acy o 57.6%. Sayed (2015) analyzed 340 esea ch epo s o
Ni y 50 index i ms and s udied he alua ion models used by analys s o ‘buy’ ecommenda ions and
obse ed he highes a ge p ice accu acy o 70% wi h he DCF model and he lowes TPA o 51.1%
wi h book alue-based o ecas s. Cha e jee e al. (2020) analyzed he a ge p ice accu acy o analys
calls o a speci ic window (30 days o echnical calls and 180 days o undamen al calls) and no ed
ha 43% o echnical calls and 52% o undamen al calls me he a ge p ices. Pa el (2021) analyzed
Banking S ocks lis ed on he Na ional S ock Exchange and no ed ha p omo e holdings ha e a signi i-
can nega i e associa ion wi h a ge p ice accu acy.
Ou li e a u e e iew unco e s he ollowing gaps (a) he sec o -based c oss-sec ional analysis o he
a ge p ice achie emen s o he analys ecommenda ions is no e ec i ely e alua ed, (b) he impac o
alua ion me hods on he accu acy o analys s ock ecommenda ions is s udied wi h limi ed classi ica-
ion o alua ion models o o a es ic ed sample o la ge capi aliza ion index s ocks, (c) he impac o
business capi al in ensi y as measu ed by he capi al-in ensi e o labo -in ensi e na u e o he business
on analys s ock ecommenda ion accu acy is no explo ed and (d) he s udies on analys a ge p ice
accu acy in he Indian con ex use a es ic ed sample (only buy ecommenda ions and majo ly o la ge
lis ed i ms), and esul s a e hus no compa able wi h simila s udies in o he economies.
Ou s udy aims o ill in he esea ch gaps and lays ou he ollowing objec i es.
1. To s udy whe he he sell-side analys s ha e supe io a ge p ice p edic ing abili ies.
2. To s udy he e ec i eness o alua ion me hods analys s use in p edic ing a ge p ices.
3. To s udy he impac o ecommenda ion classes on a ge p ice accu acy.
4. To s udy he de e minan s o he a ge p ice accu acy.
3. Hypo heses
Based on ou esea ch objec i es, we es he ollowing hypo heses.
H1: Sell-side analys s ha e supe io a ge p ice p edic ing abili ies, and ecommenda ion class, i m size, and
sec o ha e an impac on he a ge p ice achie emen s o analys s’ ecommenda ions.
H2: Valua ion me hods used by sell-side analys s o a i e a a a ge p ice impac he a ge p ice
achie emen s o he analys .
H3: Recommenda ion classes (s ong buy, buy, sell, s ong sell) ha e an impac on he a ge p ice accu acy
o he analys ecommenda ions.
H4: Valua ion me hods, business capi al in ensi y (capi al/labo ), i m p o i abili y, analys op imism, i m ola ili y,
and pos -ma ke e u ns ha e an impac on he a ge p ice accu acy o he analys ecommenda ions.
4 S. KADAM AND M. SETHI
4. Da a
We collec ed 28,486 sell-side analys s’ ecommenda ions om he da abase o ET In elligence, he
esea ch a m o The Economic Times, India’s la ges business pape , and pa o The Times G oup,
India’s la ges media g oup. The ecommenda ions a e o a pe iod o i e yea s, om 1 Janua y 2016 o
31 Decembe 2020. All he epo s a e manually analyzed o ex ac necessa y in o ma ion such as com-
pany name, da e o epo , ecommenda ion, a ge p ice, and alua ion me hods.
The epo s a e u he il e ed by emo ing hose epo s ha a e (a) wi hou any speci ic ecommen-
da ion such as buy, sell, e c.; (b) wi hou any speci ic a ge p ice; (c) whe e he e is a s ock spli o
bonus issue wi hin he ecommenda ion pe iod o 12 mon hs om he da e o publica ion o he epo ;
(d) whe e he epo is o REIT o INVIT; (e) whe e he alua ion me hod is no speci ied; and ( ) whe e
he ecommenda ions a e be o e he lis ing o he s ocks on he s ock ma ke . Finally, we analyzed
22,807 ecommenda ions by 34 esea ch i ms o 805 companies, which co e s abou 95% o he o al
ma ke cap o all lis ed companies in India.
Analys ecommenda ions a e classi ied in a s epwise manne . Ini ially, ecommenda ions we e classi-
ied acco ding o he anking adop ed by he esea ch i m. As each esea ch i m has i s own scale o
anking he ecommenda ions, we eclassi ied ecommenda ions on a s anda d i e-poin scale o ‘s ong
buy/buy/hold/sell/s ong sell’. This is consis en wi h he classi ica ions in he li e a u e e iewed, and
ou sample can be compa ed c oss-sec ionally wi h o he s udies. The p ocess ollowed o con e sion o
ecommenda ions o s anda d i e-poin scale is (a) i he o iginal scale o he esea ch i m is a i e-
poin scale ha ing cen al ecommenda ion as ‘hold’o ‘neu al’ hen we ha e con e ed he o iginal
ecommenda ion di ec ly o he s anda d i e-poin scale adop ed by us; (b) i he o iginal scale o he
esea ch i m is a h ee-poin scale hen he cen al ecommenda ion is classi ied as ‘hold’, a buy ecom-
menda ion wi h an implici e u n abo e 20% is classi ied as ‘s ong buy’and balance as ‘buy’, a sell ec-
ommenda ion wi h an implici all o la ge han 20% a e classi ied as ‘s ong sell’and balance as ‘sell’.
The b eakdown o he da a is p esen ed in Table 1. Panel A p esen s he mon h-wise b eakup o he
ecommenda ions o he i e-yea pe iod. The ecommended da a we e ai ly dis ibu ed ac oss yea s.
Highe ecommenda ions a e obse ed in Janua y, May, July, Oc obe , and No embe , consis en wi h
he hypo hesis ha analys s upda e hei ecommenda ions on he a ailabili y o new in o ma ion
h ough qua e ly esul s, sha eholde mee ings, and so on. Panel B p esen s he dis ibu ion o he da a
acco ding o he ecommenda ion class. I can be obse ed ha ‘s ong buy’and ‘buy’ ecommenda ions
exceed ‘hold,’‘sell’and ‘s ong sell’ ecommenda ions ac oss he yea s. This is consis en wi h he
hypo hesis ha analys s end o p o ide mo e buy ecommenda ions han sell ecommenda ions
(Jegadeesh & Kim, 2006; Moshi ian e al., 2009; Womack, 1996). Panel C ep esen s he dis ibu ion o
da a acco ding o he 11 sec o classi ica ions. The highes ecommenda ions we e obse ed in he con-
sume disc e iona y (23.3%) sec o , ollowed by inancial se ices (14.8%). I can be seen ha all sec o s
a e well ep esen ed in he sample.
The da a co e 805 companies, and he highes co e age o a company (IndusInd Bank L d.) is 189.
The a e age and median co e age pe company a e 28.30 and 13%, espec i ely. O he 34 esea ch
i ms, he highes ecommenda ion om a i m (Edelweiss Secu i ies L d.) was 3283. The a e age and
median ecommenda ions o esea ch i ms we e 670 and 210, espec i ely.
We e iew each epo o de e mine he alua ion me hod used o es ablish he a ge p ices.
Seg ega ion o he alua ion me hods is pe o med, as shown in Figu e 1. The i s b oad ca ego y is he
holis ic app oach o he sum o he pa s (SOTP) app oach (E kile e al., 2022). Holis ic alua ion is u -
he ca ego ized in o a mul iples-based app oach, a discoun ed cash low app oach, and o he s.
Mul iples ha e subca ego ies such as income, ea nings, sec o mul iples (P/E, EV/EBITDA, Rela i e PE,
PEG, e c.), and book alue mul iples (P/B). SOTP is ca ego ized in o homogeneous (using he same
app oach ac oss pa s) o hyb id (using di e en alua ion app oaches o di e en pa s).
Table 2 p esen s he a ious app oaches used by he analys s o hei ecommenda ions. Analys s
p e e ed a holis ic app oach (83%) o he sum o he pa s (17%). DCF was used in only 4% o ecom-
menda ions. The mul iple -based app oach is he mos p e e ed app oach o analys s o a i e a a ge
p ices. O e all, 93% o he ecommenda ions used a mul iple app oach (78% unde holis ic and 15%
unde homogeneous SOTP). These obse a ions a e consis en wi h hose o p e ious s udies.
COGENT ECONOMICS & FINANCE 5
5. Me hodology, ools and echniques
Asqui h e al. (2005) in oduced he i s measu e o a ge p ice achie emen whe e hey de ised a
simple me ic as he a ge p ice p edic ion should be conside ed ‘achie ed’i he s ock p ice o he
analyzed company equals o exceed ( alls below, in case o sell ecommenda ions) he a ge p ice a
any ime du ing he 12-mon h pe iod om he elease o he ecommenda ion. B adshaw, B own,
e al. (2013) di ided he 12 mon hs p edic ion ho izon in o wo segmen s, namely, du ing he pe iod
and he end o he pe iod, and measu ed he a ge p ice achie emen s o he wo segmen s
sepa a ely.
Bonini e al. (2010) de eloped a measu e o es o inaccu acy om an in es o pe spec i e. They
de eloped wo me ics o de ine he ideal s a egy ha an in es o can adop du ing he p edic ion
ho izon and a easible s a egy by ocusing only on he end o he p edic ion ho izon along wi h
he p edic ion e o s. Ke l (2011) imp o ed on Bonini e al. (2010) measu es by ocusing on p ecise
accu acy and conside ing he absolu e alues o e o s o e and unde achie ing he a ge p ice.
Ke l (2011) a gued ha accu acy may be de e mined by he capaci y o an analys o p edic exac
p ices. B adshaw, B own, e al. (2013) added ex an e and ex pos op imism measu es o de e mine
he le el o op imism and accu acy. An ^
onio e al. (2017) de i ed he a ge p ice accu acy o analys
consensus es ima es and hypo hesized a ela ionship be ween he s anda d de ia ion o consensus
es ima es and p edic ion e o . Pa el (2021) models o a ge p ice accu acy beyond a one-yea
ho izon.
Table 1. De ails o analys ecommenda ions.
Panel A
a
Mon h 2016 2017 2018 2019 2020 To al
Jan 497 (11.3%) 381 (8.5%) 343 (7.6%) 423 (8.5%) 535 (11.9%) 2179 (9.6%)
Feb 541 (12.3%) 676 (15.2%) 509 (11.3%) 405 (8.2%) 667 (14.8%) 2798 (12.3%)
Ma 141 (3.2%) 124 (2.8%) 102 (2.3%) 167 (3.4%) 98 (2.2%) 632 (2.8%)
Ap 327 (7.4%) 265 (5.9%) 283 (6.3%) 296 (6%) 201 (4.5%) 1372 (6%)
May 585 (13.3%) 719 (16.1%) 831 (18.5%) 665 (13.4%) 400 (8.9%) 3200 (14%)
Jun 200 (4.5%) 194 (4.4%) 204 (4.5%) 216 (4.4%) 639 (14.2%) 1453 (6.4%)
Jul 358 (8.1%) 493 (11.1%) 481 (10.7%) 563 (11.3%) 522 (11.6%) 2417 (10.6%)
Aug 509 (11.6%) 606 (13.6%) 532 (11.9%) 754 (15.2%) 554 (12.3%) 2955 (13%)
Sep 217 (4.9%) 181 (4.1%) 144 (3.2%) 166 (3.3%) 200 (4.4%) 908 (4%)
Oc 447 (10.2%) 226 (5.1%) 485 (10.8%) 534 (10.8%) 268 (6%) 1960 (8.6%)
No 417 (9.5%) 444 (10%) 447 (10%) 629 (12.7%) 294 (6.5%) 2231 (9.8%)
Dec 159 (3.6%) 148 (3.3%) 128 (2.9%) 144 (2.9%) 123 (2.7%) 702 (3.1%)
To al 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%)
Panel B
b
Recommenda ion 2016 2017 2018 2019 2020 To al
S ong buy 1469 (33.4%) 1292 (29%) 2072 (46.2%) 1885 (38%) 1540 (34.2%) 8258 (36.2%)
Buy 1597 (36.3%) 1792 (40.2%) 1378 (30.7%) 1377 (27.8%) 1566 (34.8%) 7710 (33.8%)
Hold 966 (22%) 1094 (24.5%) 852 (19%) 1298 (26.2%) 1046 (23.2%) 5256 (23%)
Sell 280 (6.4%) 209 (4.7%) 144 (3.2%) 335 (6.8%) 269 (6%) 1237 (5.4%)
S ong sell 86 (2%) 70 (1.6%) 43 (1%) 67 (1.4%) 80 (1.8%) 346 (1.5%)
To al 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%)
Panel C
c
Sec o 2016 2017 2018 2019 2020 To al
Commodi ies 554 (12.6%) 651 (14.6%) 568 (12.7%) 636 (12.8%) 656 (14.6%) 3065 (13.4%)
Consume disc e iona y 1012 (23%) 1047 (23.5%) 1106 (24.6%) 1151 (23.2%) 996 (22.1%) 5312 (23.3%)
Ene gy 167 (3.8%) 156 (3.5%) 217 (4.8%) 325 (6.5%) 328 (7.3%) 1193 (5.2%)
Fas mo ing consume goods 297 (6.8%) 280 (6.3%) 273 (6.1%) 337 (6.8%) 348 (7.7%) 1535 (6.7%)
Financial se ices 595 (13.5%) 593 (13.3%) 699 (15.6%) 727 (14.7%) 771 (17.1%) 3385 (14.8%)
Heal hca e 422 (9.6%) 482 (10.8%) 441 (9.8%) 424 (8.5%) 359 (8%) 2128 (9.3%)
Indus ials 601 (13.7%) 648 (14.5%) 685 (15.3%) 694 (14%) 525 (11.7%) 3153 (13.8%)
In o ma ion echnology 423 (9.6%) 348 (7.8%) 280 (6.2%) 390 (7.9%) 324 (7.2%) 1765 (7.7%)
Se ices 110 (2.5%) 84 (1.9%) 92 (2%) 132 (2.7%) 95 (2.1%) 513 (2.2%)
Telecommunica ion 96 (2.2%) 71 (1.6%) 70 (1.6%) 68 (1.4%) 45 (1%) 350 (1.5%)
U ili ies 121 (2.8%) 97 (2.2%) 58 (1.3%) 78 (1.6%) 54 (1.2%) 408 (1.8%)
To al 4398 (100%) 4457 (100%) 4489 (100%) 4962 (100%) 4501 (100%) 22,807 (100%)
a
Panel A p esen s he mon h-wise b eakup o he ecommenda ions o i e yea s o da a.
b
Panel B p esen s he b eakup o he ecommenda ions as pe he ecommenda ion class, namely, s ong buy, buy, hold, sell and s ong sell.
c
Panel C p esen s he b eakup o ecommenda ions pe sec o classi ica ion. These classi ica ions a e based on he classi ica ion o s ocks p o-
ided by BSE Limi ed, one o he oldes and la ges s ock exchanges in India.
6 S. KADAM AND M. SETHI
5.1. Ta ge p ice achie emen
To de e mine he a ge p ice achie emen , we adop ed he me hodology in B adshaw, B own, e al.
(2013). Fi s , o each ecommenda ion, we de e mine he a ge p ice achie emen by compu ing
‘TPMe End’and ‘TPMe Du ing’.
Whe e,
TPMe End ¼1 i he unde lying sha e p ice eaches o exceeds ( alls below in he case o sell ecom-
menda ions) he a ge p ice a he end o he yea om he ecommenda ion da e.
TPMe Du ing ¼1 i he unde lying sha e p ice eaches o exceeds ( alls below, in case o sell
ecommenda ions) he a ge p ice du ing o a he end o he yea om he ecommenda ion
da e.
Fu he , he c oss-sec ion o he ecommenda ions is analyzed ac oss ecommenda ion classes (S ong
Buy, Buy, Hold, Sell, S ong Sell), he ma ke capi aliza ion o he i m (La ge-cap, Mid-Cap, Small-Cap),
he sec o o he i m, and he yea o ecommenda ion.
Figu e 1. Classi ica ion o alua ion me hodologies.
Table 2. Analys ecommenda ions as pe alua ion me hodologies.
Me hodology To al %
Holis ic alua ion 18,852 83%
Mul iples 17,854 78%
Income, ea nings & sec o mul iples 15,219 67%
Book mul iples 2635 12%
DCF 829 4%
O he s 169 1%
Sum o he pa s alua ion 3955 17%
Homogeneous
a
3342 15%
Hyb id 613 2%
To al 22,807 100%
a
Homogenous in ou da ase using mul iples app oaches o di e en pa s.
COGENT ECONOMICS & FINANCE 7
Ou indings o analys op imism, i m ola ili y, and pos -ma ke e u ns a e nega i ely ela ed o a -
ge p ice accu acy, which ma ches he indings o Bonini e al. (2010) and Ke l (2011). Ou EPS indings
a e consis en wi h hose o p e ious s udies o Bonini e al. (2010). Ou indings on he e ec s o alu-
a ion me hods and capi al in ensi y on a ge p ice accu acy a e a new addi ion o he li e a u e.
7. Conclusion
We analyzed a la ge sample o 22,807 ecommenda ions o e a i e-yea pe iod om 2016 o 2020 o
he Indian S ock Ma ke , co e ing 805 companies. We obse ed highe ecommenda ions a e he a ail-
abili y o new in o ma ion h ough qua e ly esul s, sha eholde mee ings, and so on. These ecommen-
da ions we e pa icula ly high in Janua y, May, July, Oc obe , and No embe . ‘S ong Buy’and ‘Buy’
ecommenda ions signi ican ly exceeded sell ecommenda ions. In ou analysis,
i. We obse ed ha he a ge p ice was achie ed by 38% a he end o he 12-mon h pe iod and
63% a any ime du ing he 12-mon h pe iod a e he ecommenda ion. Acco ding o ou li e a u e
e iew, 63% achie emen is he highes eco ded ac oss simila s udies in he US, Ge man, and
I alian ma ke s, sugges ing ha analys s demons a e easonable p edic i e abili y in he Indian
ma ke . Ta ge p ice achie emen is sligh ly lowe o small-cap s ocks, which can be a ibu ed o
hei high ola ili y. We ound he highes a ge p ice achie emen o he FMCG and IT sec o s
and he lowes achie emen o he u ili ies and elecommunica ions sec o s, indica ing analys s’
limi ed p edic i e abili ies in capi al-in ensi e sec o s.
ii. We documen he limi ed e ec i eness o he DCF me hodology and he high e ec i eness o he
SOTP hyb id and mul iple-based app oach o alua ion in p edic ing a ge p ices. Ou indings sug-
ges ha bo h he holis ic and SOTP me hods a e a pa . Analys s p e e holis ic and mul iple-based
alua ion app oaches o a i e a he a ge p ices. We ind me i in he use o mo e igo ous alu-
a ion echniques, such as he SOTP Hyb id app oach, o imp o e accu acy.
iii. Ta ge p ice accu acy was es ed using a model de eloped by Bonini e al. (2010). The p edic ion
e o s o he ideal e u n s a egy a e signi ican and nega i e, indica ing conse a i e es ima es by
analys s o he ime ho izon; he e, ou esul s di e om Bonini e al. (2010). Fo a easible e u n
s a egy, he p edic ion e o s we e la ge, posi i e, and signi ican , indica ing o e shoo ing. Ou esul s
indica e ha s ock p ices mo e in he di ec ion o ecommenda ion, p ima ily mee he a ge p ices,
Table 7. De e minan s o a ge p ice accu acy.
a
IS_PE_M FS_PE_M
Coe icien T-s a Coe icien T-s a
In e cep 0.0101 1.47 0.3338 3.33
IR 0.2947 84.14 0.8930 17.45
MKT_CAP 0.0000 0.18 −0.0105 −2.59
MKTRTN_PAST 0.01091.68 −0.1934 −2.04
PB_RATIO 0.0001 0.55 −0.0052 −3.44
FIRM_BETA 0.0069 5.54 0.1364 7.52
MKTRTN_POST −0.1290 −28.88 −1.4008 −21.46
EPS −0.0000 −3.91 −0.0004 −2.80
VAL_MULT 0.0018 0.24 0.0740 0.68
VAL_DCF 0.0239 2.92 0.1911 1.60
VAL_SOTP 0.0000 0.00 0.0385 0.35
CAP_INT 0.0092 2.59 0.1412 2.73
LAB_INT 0.0009 0.27 0.1927 3.80
Adj R
2
0.284 0.044
F-s a is ic 821.5 95.45
Obse a ions 22,807 22,807
No e: Signi icance a he 10%,5% and 1% le els a e deno ed by ,, and , espec i ely.
a
We ha e also pe o med a obus ness check o de e mine he signi icance o he de e minan s o he
ac o s a ec ing accu acy. The obus ness check is pe o med by modi ying he dependen a iables o
ou de e minan s o a ge p ice accu acy ‘IS_PE_M’and ‘FS_PE_M’ o ‘0’i he a ge p ices a e achie ed
and ‘1’i he a ge p ices a e no achie ed unde he Ideal and Feasible s a egy. We ind simila signi i-
cance o a iables and signs o he coe icien s o he signi ican a iables, which assu es he obus ness
o ou esul s.
14 S. KADAM AND M. SETHI
and consolida e/mean e e du ing he 12-mon h ho izon. We documen he signi ican impac o
ecommenda ion classes on a ge p ice accu acy, especially, al hough analys s gi e a e y low num-
be o ‘s ong sell’and ‘sell’signals, hey end o o e shoo when hey ecommend a sell.
i . We ound ha analys op imism, ep esen ed by implici e u ns in a ge p ices, nega i ely impac s
a ge p ice accu acy. ‘S ong sell’and ‘sell’ ecommenda ions a e low in numbe bu o e shoo .
Ex-pos ma ke e u ns posi i ely impac accu acy, owing o mo e buy ecommenda ions han sell ec-
ommenda ions. A highe i m be a con ibu es o p edic ion e o s, indica ing analys s’limi ed abili y
o model ola ili y. The signi ican nega i e coe icien s o EPS unde sco e analys s’di icul y in p e-
dic ing p ices o loss-making en e p ises. The use o he DCF me hodology was obse ed o add o
he p edic ion e o s. The capi al-in ensi e na u e o a i m adds o he p edic ion e o , indica ing he
limi ed abili y o analys s o o ecas he bene i s o capex and i s impac on a ge p ices.
Ou esea ch indica es ha analys s ha e easonable p edic i e abili ies, and o e -op imism educes
he p edic ion accu acy. Ou indings on alua ion me hods and capi al in ensi y impac ing a ge p ice
accu acy u he add o he exis ing li e a u e. The s udy o e s p ac ical insigh s o bo h in es o s and
policymake s. In es o s should ecognize he limi a ions o analys s’s ock p ice o ecas s, pa icula ly in
capi al-in ensi e indus ies, and exe cise cau ion when e alua ing highly op imis ic a ge p ice ecom-
menda ions, especially o small-cap o loss-making companies. Analys s and b oke age i ms a e encou -
aged o adop mo e igo ous alua ion models, such as he SOTP Hyb id, whe e e applicable, when
se ing a ge p ices. F om a policy pe spec i e, he e is a need o de elop egula ions and disclosu e
equi emen s o be e p o ec in es o s by ensu ing anspa ency om analys s and b oke age i ms.
We ha e analyzed sec o capi al in ensi y impac on he accu acy o a ge p ices, u u e esea ch could
in es iga e he in luence o analys cha ac e is ics, b oke age i m a ibu es, and addi ional sec o -
speci ic ac o s on he accu acy o a ge p ice p edic ions.
No es
1. See h ps://www.s a is a.com/s a is ics/274490/global- alue-o -sha e-holdings-since-2000/.
2. SEBI has, ide i s ci cula no. SEBI/HO/IMD/DF3/CIR/P/2017/114 da ed 6 h Oc obe 2017, de ined la ge cap, mid-
cap and small-cap companies.
Au ho con ibu ions
Su esh Kadam: concep ualiza ion; da a cu a ion; me hodology; o mal analysis; w i ing–o iginal d a ; w i ing –
e iew & edi ing (equal). Madh i Se hi: supe ision; alida ion; w i ing – e iew & edi ing (equal).
Disclosu e s a emen
No po en ial con lic o in e es was epo ed by he au ho (s).
Funding
No unding was ecei ed.
Abou he au ho s
Su esh Kadam is an Assis an P o esso a DY Pa il In e na ional Uni e si y. He comple ed his MBA a IIT Kanpu and
has indus y expe ience in in es men banking. His esea ch in e es s include inancial ma ke s, co po a e inance,
and FinTech. Cu en ly, he is also a esea ch schola a Symbiosis In e na ional Uni e si y.
D . Madh i Se hi is he P o esso and Di ec o a SIBM, Bengalu u. She comple ed he pos -doc o al ellowship om
Indian School o Business (ISB), Hyde abad a e doing he Doc o a e in he a ea o inancial ma ke s. He esea ch
in e es s lie in he a ea o inancial ma ke s, inancial economics, me ge s and acquisi ions and capi al s uc u e
decisions.
COGENT ECONOMICS & FINANCE 15
ORCID
Su esh Kadam h p://o cid.o g/0009-0002-8286-4058
Madh i Se hi h p://o cid.o g/0000-0001-8687-7740
Da a a ailabili y s a emen
The da a ha suppo he indings o his s udy a e a ailable om he co esponding au ho , Madh i Se hi, upon
easonable eques .
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