Investigation of Factors Influencing Risk Tolerance among Investors using Ordinal Logistic Regression: A case from Nepal.

Figures & data

Table 1. Risk Score and Risk Category level

Risk Score	Risk Category
7–10	Very Conservative
11–17	Conservative
18–24	Moderate
25–31	Aggressive
32–35	Very Aggressive

Table 2. Variables of Study

Variables	Symbol	Operational Definition	Literature Support
Independent Variable
Education	$X_{education}$	This is a categorical demographic variable that assess the education level. It is has following level 1: No Formal Education 2: School Leaving Certificate 3: College 4: University 5: Phd	Literature shows mixed impact of education on risk level. Several studies (Gutter and Fontes, Gilliam & Chatterjee, 2011) have shown education as a factor influencing risky investment while Yao et al. (2011) shows education is not that a factor
Gender	$X_{Gender}$	Dummy variable to denote gender. It assumes following values 0: Female 1: Male	Hanna et al. (2011), Friedberg and Webb (2006) and Aren & Zengin, (2016) didn’t find any role of gender in risk. Meanwhile Neelakantan (2010) found women to be less risk tolerant than their male counterparts.
Financial Literacy	$X_{Financial\mathrm{\_}Literacy}$	Dummy variable to denote whether the respondent is formally financially literate. Thoserespondent who has studied finance in college is considered as financially literate. It assumes following values 0: Not financially literate 1: Financially literate	Hallahan et al. (2003) and Aren & Zengin, (2016) have shown Financial Literacy has major role in shaping the risk tolerance of the investor. On the other hand Cooper et al. (2014) wan’t able to establish any such connection
Years In Trading	$X_{Years\mathrm{\_}in\mathrm{\_}trading}$	This is another categorical variable that assesses respondent’s number of years in trading. It can assume following values 1: less than six months 2: 6 month to year 3: year to 5 years 4: 5 to 10 years 5: more than 10 years	Grable et al. (2006) and Malmendier and Nagel (2011) posit that period effect has effect on risk tolerance. Especially those who have experienced boom in stock are more likely to be risk tolerant
Prior Loss	$X_{Prior\mathrm{\_}loss}$	This is dummy variable to denote whether respondent had experienced prior loss or not. It can assume following value 0: No prior loss 1: Prior Loss	Thaler and Johnson (1990) has shown that prior loss results in disutility among investors resulting in fall in risk tolerance
Margin Lending	$X_{Loan}$	This is dummy variable to denote whether investor has taken loan against securities. It can assume following values 0: No loan taken 1: Loan taken	Fortune (2001) has shown that margin lending provision increases the risk taking ability of investors
Dependent Variables
Risk Tolerance	RiskTolerance	Risk Tolerance of the customer is measured using the MorningStar (2017) survey using the formula

Table 3. Respondent’s information

Variables	Respondents (%)	Mean	S.D
Risk Tolerance
Very Conservative	0
Conservative	10.1
Moderate	49.5	3.30303	6,460,957
Aggressive	38.4
Very Aggressive	2.0
Education
No formal education	8.1
School Leaving Certificate (SLC)	24.2	3.101	1.156128
College	30.3
University	24.2
PHD	13.1
Financial Literacy
No Financial Knowledge	52.5
Financial Knowledge	47.5
Years in trading
less than six months	15.2
6 month to year	20.2
year to 5 years	21.2	3.070707	1.295617
5 to 10 years	29.3
more than 10 years	14.1
Loan
No loan	48.5
Loan	51.5
Gender
Female	40.4
Male	59.6
Prior Loss
No	45.5
Yes	54.5

Table 4. Cronbach Alpha

Risk Dimensions	Cronbach Alpha
Time Horizon	0.7
Long Term Goals and Expectation	0.89
Short Term Risk Attitude	0.859

Table 5. Odds ratio of risk tolerance parameters

Parameter	Odds Ratio
Education	0.884191
Financial Literacy	3.926763*
Gender	1.305212
Years in trading	0.9784
Loan	2.3692
Prior Loss	0.0195409**
Chi-Square	66.64***
McFadden R2	0.3559

*and ** denotes significant at 5% and 1% significance level, respectively

Table 6. Testing parallelism

Parameter	Chi square
Education	0.04
Financial Literacy	0.13
Gender	1.42
Years in trading	1.86
Loan	3.40*
Prior Loss	4.86**

**and*denotes significant at 5% and 10% significance level respectively

Table 7. Generalized order logit in terms of parameter coefficient

Parameter	Coefficient (Conservative)	Coefficient (Moderate)
Education	−0.19047	−0.19047
Financial Literacy	1.59664**	1.59664**
Years in trading	−.040289	−.040289
Loan	.9710278*	.9710278*
Prior loss	−1.330857	−4.482927***
Gender	.306432	.306432
Cons	2.692354**	0.8739
Chi-Square	73.77***
McFadden R2	0.3952

*, ** and *** denotes significant at 10%, 5% and 1% significance level respectively

Table 8. Generalized ordered logit in terms of odds ratio

Parameter	Odds Ratio (Conservative)	Odds Ratio (Moderate)
Education	.8265739	.8265739
Financial Literacy	4.93642**	4.93642**
Years in trading	.9605118	.9605118
Loan	2.640657 *	2.640657*
Prior loss	.2642506	.0113003***
Gender	1.358569	1.358569
Cons	14.7664	2.93612

*, ** and *** denotes significant at 10%, 5% and 1% significance level respectively

Table 9. Predictive margins analysis for prior loss, loan financing and financial literacy

	Conservative	Moderate	High
No prior loss	.0467232	.1613041**	.7919727**
Prior loss	.1456654**	.776112**	.0782226*
No Financial Literacy	0.1536082***	0.5162253***	0.3301665***
Financial Literacy	.037986*	0.4768826***	0.4851313 ***
No Loan	0.1424387***	0.4962641***	0.3612972 ***
Loan	0.0632603**	0.4841045***	0.4526352***

*and ** denotes significant at 5% and 1% significance level, respectively

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