Sangh i, Manisha; Sha ma, Pankaj; Chandani, A i
A icle
A s udy o explo e he mo i es o in es o s o in es in
de i a i e ma ke s: A PLS-SEM app oach
Financial In e ne Qua e ly
P o ided in Coope a ion wi h:
Uni e si y o In o ma ion Technology and Managemen , Rzeszów
Sugges ed Ci a ion: Sangh i, Manisha; Sha ma, Pankaj; Chandani, A i (2024) : A s udy o explo e
he mo i es o in es o s o in es in de i a i e ma ke s: A PLS-SEM app oach, Financial In e ne
Qua e ly, ISSN 2719-3454, Sciendo, Wa saw, Vol. 20, Iss. 3, pp. 1-12,
h ps://doi.o g/10.2478/ iq -2024-0015
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Abs ac The objec i e o he s udy is o explo e he mo i es o in es o s o in es in de i a i e ma ke s. I
is a quan i a i e s udy whe e a su ey me hod was used o collec da a om he in es o s using
a p obabili y sampling me hod. The da a was analyzed using PLS-SEM o es he concep ual
model. The esul s o he s udy show ha specula ion, hedging, and inancial li e acy a e s ong
p edic o s o in es o s' mo i es o in es in he de i a i es ma ke . The R2 was 0.447 implying
specula ion, hedging, and inancial li e acy explain 44.7% o he a iance o he dependen a ia-
ble, ha is, he mo i es o in es o s o in es in equi y de i a i es, and he adjus ed R-squa e is
0.432 (43.2%) which alida es he model. Few s udies explo e he easons o in es in de i a i es
using seconda y da a. Howe e , o he bes o he au ho 's knowledge s udies explo ing he mo-
i es o in es o s a e a e, and he e ha e been none using p ima y da a om an Indian pe spec-
i e. The s udy p o ides empi ical e idence ha could be use ul o companies, in es o s, b o-
ke s, and policymake s o unde s and he mo i es o in es o s o in es in de i a i es.
JEL classi ica ion:
Keywo ds: Specula ion, Hedging, Financial Li e acy, Mo i es o In es , De i a i es, PLS-SEM
Recei ed: 02.05.2024 Accep ed: 21.05.2024
Ci e his:
Sangh i M., Sha ma P., Pa hak M. & Chandani A. (2024). A s udy o explo e he mo i es o in es o s o in es in de i a i e ma ke s: a PLS-SEM
app oach. Financial In e ne Qua e ly 20(3), pp. 1-12.
© 2024 Manisha Sangh i e al., published by Sciendo. This wo k is licensed unde he C ea i e Commons A ibu ion-NonComme cial-NoDe i a i es
3.0 License.
1 Symbiosis In e na ional Uni e si y, India, e-mail: manisha_sangh [email protected], ORCID: h ps://o cid.o g/0000-0001-7745-6258.
2 Symbiosis Cen e o Managemen and Human Resou ces De elopmen (SCMHRD), India, e-mail: pankaj_sha [email p o ec ed], ORCID: h ps://
o cid.o g/0000-0003-1107-0891.
3 Jaipu ia Ins i u e o Managemen , Lucknow, India, e-mail: a i.chandani@jaipu ia.ac.in, ORCID: h ps://o cid.o g/0000-0001-7662-7967.
a specula o ades s a egically o ea n high-yield e-
u ns (Bezzina & G ima, 2012; Lan a a, 2010), whe eas,
hedging is a way o educing and e adica ing he isk
associa ed wi h o ex, commodi y, and s ock p ices
(Acha ya e al., 2009). De i a i es help in es o s o
combine hedging and specula ion s a egies oge he
o ea n p o i by limi ing hei po en ial losses (Bezzina
& G ima, 2012). The ola ili y in he s ock ma ke and
de i a i es ma ke ac i i y a e ela ed (Chen & Tang,
2009). The inc ease in unexpec ed ola ili y in he ma -
ke leads in es o s o pa icipa e mo e as hedge s com-
pa ed o specula o s (Jongadsayakul, 2019). De i a i es
a e conside ed in es ing ools o isk managemen
since indi iduals can mo e he isk om hose who
wan o a oid oo much isk (hedge ) o hose who a e
p epa ed o accep such isk (specula o ).
Ano he ac o ha impac s he mo i es o in es
in he inancial ma ke is inancial li e acy, i plays an
impo an ole o in es o s in hei in es men deci-
sions. Financial li e acy is an unde s anding, awa eness,
alen , and expe ise o make inancial decisions (Al es,
2023). Financial li e acy helps us o make sound in es -
men decisions and o gene a e highe e u ns. In es-
o s wi h low inancial li e acy a oid using complex
p oduc s like de i a i es (Hsiao & Tsai, 2018).
This s udy ocuses on indi idual in es o s in India,
due o he inc easing pa icipa ion o indi idual in es-
o s in he de i a i es ma ke and, a huge demand o
de i a i es. Recen ly he egula o s ha e epo ed ha
9 ou o 10 indi idual in es o s in India ha a e ading
in de i a i es ha e shown losses (Secu i ies and Ex-
change Boa d o India, 2023). This has led o he need
o an unde s anding o why in es o s en e his ma -
ke , and i hey p e e his ma ke o specula ing o
hedging.
Looking a he gaining popula i y o de i a i es
among indi idual in es o s, his s udy aims o con ib-
u e o he exis ing li e a u e by unde s anding he in-
es o s’ mo i es o in es in de i a i es. The emaining
pa o he pape has been a anged as ollows: Sec ion
2 Re iew o Li e a u e Sec ion 3 Me hodology, Sec ion
4 Da a Analysis and Resul , Sec ion 5 Discusses and
Summa y, and Sec ion 6. Conclusion, Implica ions, and
Limi a ions.
Se e al s udies ha e examined why in es o s in-
es in inancial ma ke s and hei mo i es o ading
in he de i a i es ma ke and so his s udy aims o add
o his s and o li e a u e. The p esen s udy ocuses
on indi idual in es o s and ele an li e a u e is p e-
sen ed in his sec ion ollowed by he hypo heses.
Equi y de i a i es ha e changed he adi ional
in es men s yle o holding only a buy posi ion, i has
allowed ading in he alling p ice o s ocks also. The
s ock ma ke allows in es o s o hold a sho -selling
posi ion only o in aday, whe eas de i a i es allow
one o hold posi ions like sho u u es, sho call op-
ions, o a long-pu op ions o a mon h o mo e o
ade in alling p ices (Baue e al., 2009; Meye e al.,
2014). The ad an age o he economy is ha de i a-
i es help in disco e ing he p ice o he asse in he
inancial ma ke by in eg a ing in o ma ion in o asse
alues mo e apidly and e ec i ely (Si isawad & Suk-
cha oensin, 2018). In June 2000, u u es con ac s o
he Index and s ocks we e in oduced i s , and la e in
June 2001 s ock index op ions we e s a ed in India a
he Na ional S ock Exchange (NSE). I has been mo e
han 2 decades and he de i a i es ma ke has shown
emendous g ow h. Now he NSE has been classi ied
as he globally la ges exchange o de i a i es ading
o he las i e consecu i e yea s and i has been
anked i s o he highes numbe o con ac s aded,
wi h a ound 17.26 billion con ac s aded in he yea
2021 (Panda, 2023).
Indi idual in es o s play a c ucial ole in pa ici-
pa ing in equi y de i a i es. In he las ew yea s, he
pa icipa ion o indi idual in es o s in he de i a i es
ma ke has inc eased. The de i a i es a e le e aged
p oduc s and equi e less capi al han an unde lying
asse and low ansac ion cos s a e he main eason o
in es o s' in e es in he de i a i es ma ke (Pandey,
2014; Si isawad & Sukcha oensin, 2018). The de i a-
i es ading olume in India has exceeded he cash
ma ke by a huge ma gin. De i a i e- o-cash olume
a io is 422 imes in India as compa ed o Ge many
36 imes and he USA 9 imes. In India, he indi idual
in es o con ibu es an o e one- ou h sha e in u no-
e in equi y de i a i es (Li emin , 2023). This shows
a huge demand o de i a i e p oduc s by indi idual
in es o s. Wi h he inc ease in he numbe o in es o s
pa icipa ing in he de i a i es ma ke , he in e es in
academic esea ch on eme ging economies has also
su ged d as ically (A ilgan e al., 2016).
Today, inancial ma ke s a e ull o di e se ypes o
ade s, e e y in es o has di e en mo i es o in es
in a ious inancial ma ke s and hey beha e di e en ly
(Ab eu & Mendes, 2020). This in e s ha a ious ad-
e s pa icipa e in he de i a i es ma ke wi h di e en
mo i es o ading- some in es o s ade o specula-
ion (Golds ein e al., 2014; S i as a a e al., 2008) in-
s ead o adi ional in es ing (Meye e al., 2014), and
some ade o educe hei p ice isk by hedging (Chang
e al., 2000; Golds ein e al., 2014; Hsiao & Tsai, 2018;
Pan, Liu & Ro h, 2003). Specula ion is an ac ion whe e
mid- e m in he index ha gi es ne p o i . The specu-
la o s a e usually he day ade s and en e he ma ke
du ing high ola ili y (Chen & Tang, 2009). In es o s
wi h highe expec a ions o e u n, ade mo e and
p e e g ea e ansac ion size, high u no e unde ly-
ing asse s and use de i a i es o specula ion
(Ho mann e al., 2015). In es o s p e e o use di e -
en ypes o de i a i e con ac s depending on hei
mo i es (Si isawad & Sukcha oensin, 2018).
H1: Specula ion and mo i es o in es in he de i a i es
ma ke a e posi i ely ela ed.
Hedging allows in es o s o educe and elimina e
p ice isks o a ious inancial asse s such as o ex,
s ocks, and commodi ies. De i a i e ins umen s a e
used as isk managemen ools o educe p ice isk
(Dixon & Bhanda i, 1997). The expec a ion o high p ice
mo emen s in s ock p ice and inc eased ola ili y du -
ing he ea nings announcemen leads o an inc ease in
demand o hedging (Choy & Wei, 2012). In con as ,
Baue e al. (2009), Lakonishok e al. (2007), and
Schmi z and Webe (2012) ha e ound ha hedging is
no he main mo i e o pa icipa e in he equi y de i a-
i es ma ke , and he majo i y o in es o s don' hedge
hei unde lying s ock posi ions wi h op ions.
Kuma and Sup iya (2014) using a bi a ia e Gene -
alized Au o eg essi e Condi ional He e oskedas ici y
(GARCH) model o bank u u es and CNX Ni y con-
ac s based on i e yea s o da a ecommended
a hedging s a egy ha p o ides an imp o ed hedg-
ing s a egy along wi h aking ca e o he ansac ion
cos s. Pandey (2014) ound ha in es o s use op ions
mo e as a hedging ool while ades in index op ions
a e mo e commonly mo i a ed by he hedging de-
mands o expe ienced in es o s (Lemmon and Ni,
2011). In es o s who a e less in o med and ha e less
isk ole ance, demand de i a i es o use o hedging
o educe hei p ice isk (Golds ein e al., 2014; Kyle e
al., 2011). Pas s udies ha e associa ed open in e es
wi h unan icipa ed ola ili y in he ma ke and ound
ha mos in es o s use de i a i es as a hedging ool o
educe unan icipa ed ola ili y in he u u e (Chen
& Tang, 2009; Jongadsayakul, 2019).
H2: Hedging and mo i es o in es in he de i a i es
ma ke a e posi i ely ela ed.
Financial li e acy di ec ly impac s he in es o s’
decision o in es in a ious inancial ma ke s (Hassan
& Anood, 2009). Highe inancial li e acy allows in es-
o s o di e si y hei in es men s and allows hem o
channel sa ings in o a ious inancial ma ke s. Pe -
cei ed knowledge abou de i a i es was ound low in
Eu ope (Ab eu & Mendes, 2010). The complexi y o he
De i a i e ins umen s like Fu u es and Op ions
(F&O) a e egula ly used as hedging ins umen s, bu
hese a e also used as specula i e ins umen s among
in es o s (Leung e al., 2016). Some pas s udies p o-
posed ha he main use o he de i a i es ma ke is o
p o ide hedging agains p ice isk (Pan e al., 2003)
while a wide pe spec i e shows ha i is also used as
specula ion, especially du ing ea ning esul s, and high
ola ili y pe iods. In o med ade s ind ading in F&O
mo e ad an ageous o e s ock ma ke s (Black, 1975).
Vola ili y in he ma ke has a di ec ela ionship wi h
he pa icipa ion o hedge s. A pe iod o high ola ili y
in he s ock ma ke leads o an inc ease in he pa ici-
pa ion o bo h hedge s and specula o s in he de i a-
i es ma ke (Chen & Tang, 2009; Pan e al., 2003).
The e a e many s udies ocused on unde s anding
whe he in es o s ade o hedging o specula ion.
Some explained ha in es o s' mo i e is specula ion
(Baue e al., 2009; Lakonishok e al., 2007; Meye e
al., 2014), and some ound hedging he main mo i e
(Chen & Tang, 2009; Lemmon & Ni, 2011; Jongadsaya-
kul, 2019). The below sec ion will del e in o he pas
s udies' unde s anding o hedging and specula ion.
The main objec i e o specula ion is o ade in he
di ec ion in which he ma ke is expec ed o mo e
(Jongadsayakul, 2019; Singh, 2016). In specula ion,
a specula o is eady o ake a high isk o gain a la ge
p o i by p edic ing he u u e p ice (Sangh i e al.,
2024). The de i a i es a e sho - e m ins umen s as
hese a e se led on ma ked- o-ma ke , which esponds
immedia ely o p o i and loss (Cox e al., 2020). Mos
o he small and e ail in es o s a e engaged in specula-
i e ading ac i i ies and a e ac i ely engaged in specu-
la i e ading du ing he announcemen s o ea ning
esul s, news e en s, and poli ical issues (Choy & Wei,
2012; Pan e al., 2003).
Lakonishok e al. (2007) illus a ed ha a majo i y
o indi idual in es o s a e mo i a ed by specula ion on
expec ed u u e p ice mo emen s. The p ima y use o
de i a i es is mo e o specula ion while ade s p e-
e ed o ade in he de i a i es ma ke on alling p ic-
es du ing he s ock ma ke c ash o 2001 and 2002 a-
he han specula ing du ing he s ock ma ke boom
ime (Baue e al., 2009).
Meye e al. (2014) ound ha indi idual in es o s
use de i a i es p ima ily o specula ion and he p o i
and loss depends on he unde lying asse s aded. Bai-
ley e al. (2008) ha e ound ha sophis ica ed in es o s
p e e o specula e on highly ola ile s ocks and use
de i a i es. I has been obse ed by he esea che s
ha in es o s en e ing o in aday index ading gi e
a loss, whe eas i hey hold a de i a i es posi ion o
s anding. Thus, inancial li e acy and pa icipa ion in
complex p oduc s like de i a i es a e posi i ely ela ed
o each o he (Hsiao & Tsai, 2018).
H3: Financial li e acy and mo i es o in es in he de i -
a i es ma ke a e posi i ely ela ed.
ins umen may be a ba ie o pa icipa ion o less
inancially knowledgeable in es o s. The impo ance o
inancial li e acy has enla ged due o new complex
p oduc s in he inancial ma ke . De i a i es a e a com-
plex inancial p oduc ha needs an in-dep h unde -
Figu e 1: P oposed concep ual model
Specula ion
Hedging
Financial Li e acy
Mo i es o in es
in de i a i es
(H1)
(H2)
(H3)
Fou i ems we e used o measu e hedging and he sam-
ple i em is “I use equi y u u es and op ions con ac s
o educe s ock p ice isk”. The scale o Baue e al.
(2009) as well as Michels e al. (2019) was adop ed o
specula ion and hedging cons uc s in he las sec ion,
inancial li e acy was measu ed using a scale o Hsiao
& Tsai (2018) as well as Van Rooij e al. (2011). The
sample i em o inancial li e acy is “I am capable o
e alua ing di e en inancial asse s”.
Conside ing he aim o he s udy, p obabili y sam-
pling has been used o collec he da a om he ele-
an esponden s. P obabili y sampling seeks o mini-
mize bias by gi ing an equal chance o e e y membe
o he popula ion selec ed. To es ima e he sample
size, he “10- imes ule” me hod has been used, which
explains ha he sample size should be 10 imes o
inne and ou e model link (Hai e al., 2011).
The a ge sample was he indi idual in es o s who
we e ading in equi y de i a i es di ec ly in India.
A s uc u ed ques ionnai e was dis ibu ed online
h ough emails and LinkedIn. The ques ionnai e was
dis ibu ed h ough b oke s o each he ele an pop-
ula ion, he ques ionnai es we e collec ed om No-
This s udy used he gene alized scales de eloped
by Ras ogi and Gup a (2020) as well as Michels e al.
(2019) o measu e he dependen a iable ‘mo i es o
in es ’, and ou i ems we e used o he same. To
measu e independen a iables ‘specula ion’,
‘hedging’, and ‘ inancial li e acy’ he scales ha e been
adop ed om he exis ing li e a u e and he same has
been men ioned in Annexu e A. Fou een i ems ha e
been used o measu e independen a iables, and he
esponses we e collec ed in a i e-poin Like scale.
This s udy used Google Fo ms o c ea e and sha e
he su ey ques ionnai e online. The ques ionnai e was
di ided in o ou sec ions: The i s sec ion collec ed
demog aphic in o ma ion like age, gende , income,
educa ion, in es ing expe ience, exchange p e e ence,
e c. The second sec ion collec ed he esponses o
measu e he dependen a iable using ou i ems.
A sample i em o mo i es is “I am clea wi h my u u e
inancial needs and mo i es”. In he hi d sec ion, we
collec ed esponses o 5 i ems o measu e specula ion
mo i es, and he sample i em o specula ion is “My
main objec i e is o ade o sho - e m specula ion”.
Sou ce: Au ho ’s own wo k.
we e conside ed o he da a analysis. The da a was
scanned and cleaned; we did no ind any missing al-
ues. The da a was la e analyzed using SPSS and PLS-
SEM. The de ails o esponden s' demog aphics a e
gi en in Table 1.
embe 2023 o Ma ch 2024. A ound 487 esponden s
we e app oached in majo ci ies (Mumbai, Pune, In-
do e, Bhopal, Delhi, Kolka a, Jaipu , Lucknow and
mo e) o ill ou he ques ionnai e. 110 esponses we e
ecei ed om he esponden s who ade in equi y
de i a i es di ec ly (22.58%). The inal 110 esponses
Table 1: Demog aphic o indi idual in es o s
Va iables Ca ego y Coun (abs) Coun (%) To al
Gende Male
Female
98
12
89.10%
10.90% 110
Occupa ion
S uden
P i a e job
Sel -employed
Go e nmen employee
26
63
20
1
23.63%
57.27%
18.20%
0.90%
110
Ma i al s a us Single
Ma ied
50
60
45.50%
54.50% 110
Educa ion
quali ica ion
Unde g adua e
Pos -g adua e(M.Sc.,M.Com., e c)
P o essional (MBA, CFA, CA, e c)
8
12
90
7.30%
10.90%
81.80%
110
Age
Less han 25 yea s
Be . 25-35 yea s
Be . 35-45 yea s
Be . 45-55 yea s
> 55 yea s
14
41
46
7
1
12.70%
37.30%
41.80%
6.40%
0.90%
110
Income
(Rs in lacs)
Ze o
Below Rs. 5,00,000
Rs. 5,00,001 – 10 L
Rs. 10,00,001 – 15 L
Rs. 15,00,001 – 20 L
Abo e Rs. 20L
7
16
13
15
13
44
6.40%
14.50%
11.80%
15.50%
11.80%
40.00%
110
In es ing
expe ience in he
de i a i es
ma ke (di ec in
yea s)
Less han 1
Abo e 1- Below 3
Abo e 3 - Below 5
Abo e 5 - Below 10
Abo e 10
19
25
42
19
5
17.30%
22.70%
38.20%
17.30%
4.50%
110
Sou ce: Au ho ’s own wo k.
The eliabili y o each i em o he p oposed con-
cep ual model has been e alua ed using ac o loading
(Hai e al., 2014) and a ac o loading below 0.5 should
no be accep ed (She lin & Miles, 1998). The i ems wi h
below 0.5 loadings we e d opped (a de ail o i ems
d opped om each a iable is men ioned in Appendix
B). We measu ed con e gen alidi y based on eliabil-
i y analysis using C onbach's alpha and Composi e Reli-
abili y (CR). As ound by Hai e al. (2020) CR is mo e
impo an and eliable han C onbach alpha because CR
is weigh ed and C onbach alpha is unweigh ed. La e ,
we analyzed he s uc u al model o check he good-
ness o i o da a o he s a is ical model o he p o-
posed concep ual model. Reliabili y and con e gen
alidi y e e o he s ong co ela ion o ac o s wi h
hei ele an cons uc s. Accep able ac o s load sub-
The da a has been analyzed using Pa ial Leas
Squa es-S uc u al Equa ion Modeling (PLS-SEM) which
is one o he mos commonly used app oaches (Hai
& Alame , 2022) used o da a wi h mul iple a iables
and is p ima ily used o examine models wi h la en
a iables (Memon e al., 2021).
Mos o he esponden s ound we e male (89.1%)
as compa ed o emale (10.9%) who aded in he de-
i a i es ma ke . Mos o he ade s a e below he age
o 45 yea s, ha e p o essional deg ees, and a e wo king
p o essionals wi h a p i a e job and wi h income abo e
20 Lakhs pe annum. Mos o he esponden s ha e
in es ing expe ience in he de i a i es ma ke o 3 o 5
yea s.
The da a was ound o be eliable as C onbach’s
Alpha (α) o he dependen a iable mo i es is epo -
ed a 0.820 and o he independen a iable specula-
ion, hedging, and inancial li e acy, he alpha was
0.824, 0.856, and 0.833 espec i ely. The da a passes
he alidi y es as he composi e eliabili y o all a ia-
bles is mo e han 0.70. The a e age a iable ex ac ed
(AVE) o all a iables is mo e han 0.50 and CR is g ea-
e han AVE. The VIF o all i ems is below 3 only o
one i em i is 3.6. These alues a e shown in Table
2 and all he cons uc s we e ound o be eliable.
s an ially on hei connec ed cons uc s. The composi e
eliabili y should be mo e han 0.6 (Bacon e al., 1995),
and he equi ed le el o C onbach’s α which should be
mo e han 0.70 (Hai e al., 1998). We measu ed
con e gen alidi y using A e age Va iance Ex ac ed
(AVE) o es he alidi y o he cons uc s. AVE should
be equal o o g ea e han he 0.5 alue (Chin, 1998;
Hai e al., 2011). The Va iance In la ion Fac o (VIF)
es is used o es mul icollinea i y among p edic o
a iables and a ange o 1<VIF<5 is mode a ely co ela-
ed (Daoud, 2017). These alues a e gi en in Table 2.
Table 2: Measu emen o eliabili y and alidi y
Cons uc I em C onbach’s
Alpha (α)
Va iance
In la ion Fac o
(VIF)
Composi e
Reliabili y (CR)
A e age
Va iance
Ex ac ed (AVE)
Mo i es o In es
in De i a i es
MD1
MD2
MD3
MD4
0.820
1.969
1.822
1.821
1.466
0.881 0.650
Specula ion
S1
S2
S3
S4
S5
0.824
1.700
2.214
1.810
1.940
2.020
0.862 0.562
Hedging
H1
H2
H3
H4
0.856
2.933
3.992
3.313
1.272
0.888 0.666
Financial Li e acy
FL1
FL2
FL3
FL4
FL5
0.833
2.281
2.706
2.342
1.228
1.769
0.885 0.613
Sou ce: Sma PLS4.
be less han 0.85 o ensu e disc iminan alidi y. I he
alue is mo e han 0.85 o 0.90, i indica es ha con-
s uc s a e no dis inc om each o he su icien ly. The
cons uc o inancial li e acy, hedging, specula ion, and
mo i e mee he c i e ia o HTMT wi h all alues below
0.85, hence we ound disc iminan alidi y sa is ac o y
as all cons uc s as shown in Table 3.
Disc iminan alidi y e e s o he dissimila i y o
he cons uc s in he model (Sa s ed e al., 2022). He -
e o ai -mono ai a io o co ela ions (HTMT) me hod
has been used o measu e he disc iminan alidi y
which assesses whe he he measu es o di e en con-
s uc s a e empi ically dis inc . The c i e ion o HTMT is
o ind he collinea i y p oblems among he la en con-
s uc s (mul icollinea i y) and he HTMT a io needs o
Table 3: Disc iminan Validi y - He e o ai -Mono ai Ra io o Co ela ions (HTMT)
Cons uc s Financial li e acy Hedging Mo i es Specula ion
Financial Li e acy 1.000
Hedging 0.113 1.000
Mo i es 0.721 0.271 1.000
Specula ion 0.275 0.244 0.411 1.000
Sou ce: Sma PLS4.
s a is ically suppo ed. H3 s a es ha inancial li e acy
is posi i ely ela ed o mo i es, he esul o H3 ( alue
is 7.016, he p- alue is 0.000) was also ound s a is ical-
ly signi ican . Thus, H3 was suppo ed. The measu e
was pe o med h ough boo s apping. Since he
p- alue o all he h ee a iables is less han 0.05. Hy-
po hesis H1, H2 and H3 a e suppo ed and accep ed.
Specula ion was ound o be posi i ely and signi i-
can ly ela ed o he mo i es o in es in he de i a-
i es ma ke . Thus, he esul o H1 ( - alue is 2.505,
p- alue < 0.012) is s a is ically signi ican ; he e o e, we
conclude ha H1 was suppo ed. Hedging was also
ound o be posi i ely and signi ican ly ela ed o mo-
i es ( alue is 2.180, p- alue < 0.0029). Thus, H2 was
Table 4: Hypo hesis Tes ing Resul s
Hypo hesis Pa h O iginal
sample (O)
Sample
mean (m)
S anda d
de ia ion
(s)
T
s a is ics p alues Resul
H1 Specula ion ->
Mo i es_De i a i es 0.224 0.238 0.089 2.505 0.012 Signi ican
H2 Hedging ->
Mo i es_De i a i es 0.155 0.165 0.071 2.180 0.029 Signi ican
H3 Financial li e acy ->
Mo i es_De i a i es 0.518 0.514 0.074 7.016 0.000 Signi ican
No e: Based on 5,000 boo s apping samples and signi ican a p < 0.05 ( wo- ailed es )
Sou ce: Au ho ’s own wo k.
Figu e 2: Concep ual amewo k using s uc u al equa ion modeling
No es: S uc u al equa ion model wi h he specula ion, hedging, and inancial li e acy, p edic ing mo i e o in es in
he de i a i es ma ke
Sou ce: Au ho ’s own wo k.
a e in line wi h S i as a a e al. (2008) in his con ex . I
is impo an o unde s and he impac o inancial li e -
acy on in es men decisions, especially wi h complex
p oduc s (Hsiao & Tsai, 2018). To es he ela ionship
be ween inancial li e acy and mo i es o in es in he
de i a i es ma ke hypo hesis, we ound ha inancial
li e acy is posi i ely ela ed o mo i es, which means
ha in es o s wi h high inancial li e acy p e e de i a-
i es. The cu en s udy suppo s he indings o Hsiao
and Tsai (2018) as well as P asad e al. (2021). Mos o
he p e ious s udies ha e used seconda y da a om
discoun houses, e y ew ha e used p ima y da a o
unde s and he in es o s’ mo i e o in es in he de i -
a i es ma ke and he numbe o s udies done in India
is qui e low (Sangh i e al., 2024). This cu en s udy
helps in unde s anding he mo i es o in es o s o in-
es in de i a i es in India using p ima y da a. The e-
sul o he s udy can be gene alized o he eme ging
economy. These esul s will also help he in es o s o
unde s and hei mo i es o in es ing in de i a i es.
The ou come o his pape can help a ious policymak-
e s and s ock b oke s, and inancial planne s o eme g-
ing economies o unde s and he mo i es o in es in
de i a i es specula ion, hedging, and he impac o
inancial li e acy.
The de i a i es ma ke has changed he s yle o
ading; his p oduc helps in es o s o educe s ock
p ice isk. Recen ly he pa icipa ion o indi idual in es-
o s in he de i a i es ma ke has gained he a en ion
o b oke s, policymake s, and o he in es o s. India has
seen a emendous pa icipa ion o indi idual in es o s
in he de i a i es ma ke and, a huge demand o s ock
ade s o a end de i a i es ading wo kshops. SEBI
(Secu i ies and Exchange Boa d o India) has ecen ly
epo ed ha 9 ou o 10 in es o s ha a e indi idual
in es o s a e making losses in India and he e is a need
o in o m he isk associa ed wi h de i a i es o he
in es o s. This has led o an unde s anding o why in-
es o s en e his ma ke . We app oached 487 in es-
o s who a e ading in he s ock ma ke and we ound
110 (22.58%) ade in he de i a i es ma ke .
This s udy has se e al implica ions, he i s s udy
p esen s he mo i e model and explains he main mo-
i es a e specula ion, hedging, and inancial li e acy
which a e ound o be signi ican while examining he
impac o he mo i e o in es in he de i a i es ma -
ke . Nex , he p oposed model has been es ed o eli-
abili y and alidi y using a sample o indi idual in es-
o s in India hus con ibu ing o he body o
knowledge. The esul s p o ide a be e unde s anding
o he mo i es o in es o s in he de i a i es ma ke .
Ou esea ch has been conduc ed in India, and India’s
exchange is he wo ld’s la ges exchange in e ms o
de i a i es ading which is ano he con ibu ion o he
s udy.
In his s udy, using PLS-SEM pa h coe icien analy-
sis has been analyzed. The pa h model was assessed,
including he in luence o specula ion, hedging, and
inancial li e acy on he mo i es o in es in equi y de-
i a i es.
Falk and Mille (1992) sugges ed 0.10 as he mini-
mum accep able R2 alue, whe eas Hai e al. (2011)
sugges ed 0.75 as he subs an ial R2 alue, 0.50 as he
mode a e alue, and 0.25 as he weak alue. R squa e
measu es he goodness-o - i o a eg ession model
and i indica es how well he independen a iables
explain he a iabili y in he dependen a iable. The
highe alue shows ha he model explains a la ge
p opo ion o he a iabili y in he dependen a iable.
In social science esea ch, an R-squa ed be ween 0.10
and 0.50 is accep able i mos o he ac o s a e s a is i-
cally signi ican (Ozili, 2023). Figu e 2 shows ha he
R squa e o he model is 0.447. I means specula ion,
hedging, and inancial li e acy explain 44.7% o he a i-
ance o he dependen a iable, ha is, he mo i es o
in es o s o in es in equi y de i a i es, and he adjus -
ed R-squa e is 0.432 (43.2%) which alida es he model.
The hypo hesized s uc u al model ep esen s i o he
cu en da a. The s uc u al model is shown in Figu e 2.
Why indi idual in es o s in es in de i a i es has
been a puzzle ha many au ho s ha e a emp ed o
sol e. Some pas s udies ha e ound ha due o an
inc ease in ola ili y in he s ock ma ke , in es o s en-
e in de i a i es ma ke o hedge hei s ock posi ion.
Few s udies ha e ound ha he e is a posi i e ela ion-
ship be ween specula o s and in es o s in he de i a-
i es ma ke . This s udy sough o examine he impac
o specula ion, hedging, and inancial li e acy on mo-
i es o in es in he de i a i es ma ke in India. In he
di ec ion o his objec i e, his s udy has used pa h
analysis o analyze he mo i es model. The model was
es ima ed based on an online esponse o 110 in es o s
ading in he de i a i es ma ke . PLS-SEM has been
used as a me hodological and analy ical app oach o
es ing he concep ual model. We es ed he i s hy-
po hesis ha specula ion and mo i es o in es in he
de i a i es ma ke a e posi i ely ela ed. We ound
ha specula ion is s a is ically posi i ely signi ican ly
ela ed o mo i es, which means he in es o 's mo i e
o en e his ma ke is o book p o i . The indings o
he cu en pape a e in line wi h he p e ious s udies
o Michels e al., 2019; Singh, 2016; S i as a a e al.,
2008. The second hypo hesis was ha he hedging and
mo i es o in es in he de i a i es ma ke a e posi i e-
ly ela ed and i was ound o be s a is ically signi ican
posi i e, which shows ha in es o s use de i a i es as
a ool o educe p ice and ola ili y isk. Ou indings
