INTRODUCTION

Traditionally, risk has been defined as something negative or undesirable that may happen. Webster’s dictionary defines risk as “exposure to danger or hazard”. Knight (1921) differentiates between risk and uncertainty. Markowitz (1952) Modern Portfolio Theory defines risk in terms of standard deviation of the actual returns from the expected returns.

A step ahead to it, Sharpe (1963) opined that the only relevant risk to an investor whose portfolio is fully diversified is beta. In context of investment, risk is the probability that the actual return on an investment may be different from the expected return. Practically investors tend to depict varied levels of tolerance for risk when faced with uncertainty. “Individual risk tolerance is assumed to be a primary determinant of asset allocation choices, security choices and goal planning strategies”, Grable and Lytton (2001). Grable (2000) defined risk tolerance as “the maximum amount of uncertainty that an investor is willing to accept while making a financial decision”. Therefore, a realistic assessment of investors’ ability and willingness to digest the turbulence in the market is must. In general, risk tolerance may be defined as the amount of risk that an investor can handle comfortably. Therefore, researchers in the area of finance have long been interested in understanding: what determines the risk tolerance of an investor, how to measure risk tolerance and how it influences the financial decision making of individual and institutional investors. Financial risk tolerance is a subjective concept, because of which the investment managers find it difficult to understand the risk tolerance level of their clients or prospects (Roszkowski, 1995). Equities are considered as risky assets and their riskiness varies across companies, industries and economies. Thus, stock market investments are risky prepositions and an investor must be aware of his/her financial risk tolerance before entering into any commitment. Individuals largely vary in their ability to take risk depending on their demographics, socio-economic and attitudinal factors (Sharma, 2006). Measuring financial risk tolerance is not an easy job; therefore both objective and subjective methods may be employed to assess the risk tolerance behaviour of an investor.

Risk tolerance of individuals varies with age, income, gender, marital status, education and experience. Empirical studies have shown that risk tolerance or willingness to take risk decreases with age. Wallach and Kogan (1961), Cohn et al. ( 1975), Mclnish (1982), Brown (1990), Riley and Chow (1992), Bakshi and Chen (1994), Palsson (1996), Dohmen et al. (2005) and many more researchers reinforced that risk tolerance has negative relation with age. Research has proved empirically that men have greater tendency to take risk when compared to women. Prince (1993), Lunberg et al. (1994), Bajtelsmit and Bernasek (1996), Barber and Odean (2001), Hallahan et al. (2004), Roszkowski and Grable (2005) and Halko et al. (2009) concluded that gender influences the risk tolerance.

Marital status is yet another factor which influences risk tolerance as married couples have more responsibilities towards spouse and dependent family members. Roszkowski and Snelbecker (1993), Cohn and Koger (1975), Hallahan et al. (2004) found statistically significant relationship between risk tolerance and marital status. Majority findings confirm that singles are more risk tolerant. Education level of investors significantly influences their risk tolerance. It has been proved that educated individuals are willing to take more risk. Baker and Haslem (1974), Riley and Chow (1992), and Sung and Hanna (1996) found positive relation between education and risk tolerance. Yet, others argued that in the financial markets, the financial literacy is more important than the general education level of the investor. To the contrary, Shaw (1996), and Halko and Kaustia (2009) did not find any significant relation between the two.

Investment experience broadens the exposure of an investor for day to day market conditions. It enhances his comfort level for taking risk resulting in greater risk tolerance. Researchers have proved that more experienced people are willing to take more risk. Sung and Hanna (1996), Grable and Lytton (1998) and Haarala (2008) found a positive relation between experience and tendency to take risk. Income refers to the regular inflow of money in the form of salary and consultancy fee as profits. Wealth is the assets accumulated over a period of time. Income and wealth plays a vital role in determining the risk tolerance of an individual. Wealthier investors are more aggressive as they can afford greater loss. Friedman (1974), Shaw (1996), Hallahan et al. (2004) and Halko and Kaustia (2009) empirically found that individuals with higher income and wealth are more tolerant to risk and vice-versa.

Thus, it may be said that a combination of demographic and socioeconomic factors influences risk tolerance of individual investors. Further, the risk tolerance tends to bias the investment behaviour of the market participants and vice-versa. Hence, the main theme of the present study is explained as:

1. to assess the levels of financial risk tolerance among the individual equity investors in India; and

2. to study the influence of demographics on financial risk tolerance of individual equity investors in India.

REVIEW OF LITERATURE:

An accurate understanding and subsequent estimation of risk tolerance capacity of investors is of utmost importance to the market participants, investment advisors and financial product designers. This section presents a brief review of literature on risk tolerance and various issues associated with them.

Different risk tolerance classes may exhibit different behaviour while making an investment decision. Investors’ risk tolerance is generally categorized based on their demographic profile (Roszkowski and Snelbecker, 1993). Females, married, aged and salaried people are considered less tolerant to risk. On the other hand, males, unmarried, young, professionals, self-employed, more educated and people with higher income have higher risk tolerance capacity. Sung and Hanna (1996) found that the households headed by females are less risk tolerant compared to those headed by males or a married couple. Gender, marital status, ethnic group and education explain the difference in risk tolerance of households.

Wang and Hanna (1997) examined the effect of age on risk tolerance. It was found that risk tolerance increases with age keeping other variables constant. Cultural differences in risk tolerance were investigated by Weber (2013). The results show that the cultural background as represented by religion and nationality has influence on risk tolerance. Protestants and atheists are more risk tolerant than people from other religions. Foreigners were found to be risk averse. Grable and Lytton (1998) tested the relevance of demographics in differentiating the investors based on their tolerance for risk. It was found that gender, marital status, occupation (professional), employment status (self employed), income, racial background and education significantly determined the risk tolerance of the investors. Grable and Lytton (1999) presented a Financial Risk Tolerance Assessment Instrument (FRTAI). The paper provided adequate background through review of literature and then discussed the various stages of instrument development and testing. Finally, they developed a 13-item instrument used for assessing financial risk tolerance.

Hallahal et al. (2004) examined the association between demographic factors and financial risk tolerance score of individual investors. The results suggested that male investors are more tolerant to risk than females. Marriage decreases the risk tolerance scores. Sharma (2006) examined the financial risk tolerance among Indian investors by studying a combination of demographic, socio-economic and psychological factors. The results exhibit that male investors are more tolerant to risk. Younger and unmarried singles have higher appetite for risk. Similarly, those having more income and superior knowledge of personal finance are more tolerant towards risk. Yuen and Chen (2008) surveyed the risk tolerance among 2994 investors in Hong Kong. The results depicted that the risk tolerance of respondents who invested or participated in the Stock Market last year is much higher when compared to those who did not. Risk tolerance of family and friends significantly influences the risk tolerance of the investors. Income increases the risk tolerance of the investors.

Ajmi and Jasim (2008) investigated the individual investors’ risk tolerance in an emerging market, namely, Bahrain. The results indicated that women are less tolerant to risk when compared to men. Higher level of education and wealth enhances the risk tolerance of the investors. Retired investors are less tolerant to risk. Gilliam, Chatterjee and Grable (2010) compared the Survey of Consumer Finance’s (SCF) single question measure and Grable and Lytton (GL-RTS) 13-item measure as a measure of an investor’s financial risk tolerance. The study concludes that the SCF measure precisely measures the investment risk tolerance but fails to cover financial risk tolerance which is a broader concept. Therefore, GL-RTS provides a better estimate of risk tolerance. The influence of age, further decomposing it into generation, period and aging, on risk tolerance was examined by Yao, Sharpe and Wang (2011). The results portrayed that the effect of aging on risk tolerance is negative. Period effect was found to have significantly effect on risk tolerance. Education has positive effect.

The effect of demographic factors on subjective financial risk tolerance in South Africa was investigated by Strydom and Metherell (2012). Age, gender and income were found to be significantly related to financial risk tolerance, whereas race and religion did not significantly influence financial risk tolerance. Gender differences in financial decisions among university students were examined by Brokesova (2013). It was found that women are less tolerant to financial risk. These differences are further explained by their level of financial knowledge and experience. The effect of demographics on choice of investment in Pakistan was investigated by Sadiq and Ishaq (2014). The results indicate that education, income, financial knowledge and experience in market affect the risk tolerance of investors. Dhiman, Babu and Raheja (2015) examined the influence of demographics on risk tolerance among academicians in India. Age and marital status significantly influence the risk tolerance. Age has positive relation whereas marital status has negative association with risk tolerance. Thanki and Jadeja (2015) conducted a survey of individual investors in India and found that women take lesser risk than married counter parts. Investors in age group of 25-45 years are less tolerant to risk. Income is found to have a positive relation with risk tolerance but education has no association with it. Kannadhasan (2015) examined if demographic variables could be used to categorise the retail investors in terms of financial risk tolerance and risk taking behaviour in India; Four out of six demographic factors were found to be significant namely; gender, education, occupation and age. On the basis of above fact, following hypotheses may be outlined as:

Hypothesis 1: The levels of financial risk tolerance among the individual equity investors vary significantly.

Hypothesis 2: Financial risk tolerance of individual equity investors varies significantly across demographic variables.

RESEARCH METHODOLOGY:

The present study employs Grable and Lytton (published in Financial Services Review in the year 1999) thirteen item instrument for assessing Financial Risk Tolerance (FRT) of the respondents. This instrument measures investors’ financial risk tolerance on three constructs, namely, Investment Risk (IR), Risk Comfort (RCE) and Experience and Speculative Risk (SR). With a usable sample of 802 respondents, whose responses to the questionnaire have been summed up to form an individual investor’s risk tolerance index. Different scorings have been assigned to each question depending on how risky the question or statement is. Higher scores have been given to riskier choices and lower scores to less risky options. According to the Grable and Lytton, the maximum score indicating highest financial risk tolerance is forty seven (47) and the least score that may be attained by a respondent at any given time is thirteen (13). On the basis of this fact, two categories of FRT have been constructed viz; above average FRT and below average FRT. The reliability of the instrument has been tested using Cronbach’s Alpha as presented in Table 1.

Table 1: Reliability Statistics of Financial Risk Tolerance Instrument

Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	No. of Items
0.775	0.777	13

Source: Primary Data

The calculated value of Cronbach’s Alpha is 0.775 which is more than acceptable level of 0.70 (Strydom and Metherell, 2012; Grable and Lytton, 2001 and Pedhazur and Schmelkin, 1991). The satisfactory reliability results ensure the Financial Risk Tolerance instrument may be used without any apprehension and adequately represents the reliable response from the investors.

Descriptive statistics like mean, standard deviation, range, minimum and maximum have been calculated for FRT as well as for three constructs; IR, RCE and SR. In order to test the significant difference in the opinion of respondents for FRT and its’ two categories (above average FRT and below average FRT) across general and investment related demographics, independent t-test, one way ANOVA and Bonferroni post hoc analysis have been employed. The detailed results are explained below:

RESULTS AND DISCUSSION:

Table 2 represents the Financial Risk Tolerance statistics of the sample equity investors. The mean financial risk tolerance score is 28.612 with a standard deviation of 5.656. In line with Grable and Lytton (2001), the calculated risk tolerance range is 31 with a maximum financial risk tolerance value of 44 and minimum value of 13 points. These findings are also in conformity with results of Strydom and Metherell (2012). Grable and Lyton (1999b) instrument is a standardised instrument with thirteen statements to assess the Financial risk tolerance. These statements have further been divided in three constructs, namely Investment risk, risk comfort and experience and speculative risk. Investment risk measures the risk-return preferences of an investor in terms of choice of an investment option. It comprises of statements like: how comfortable you are while investing in stocks? It also poses choice to the investor of investing in Bank deposits (a safer heaven) or in a quality bend or in stocks. Investment risk construct comprises of five statements with a minimum possible score of 5 and maximum possible score of 17. The mean investment risk score of the respondents is 10.549 with a standard deviation of 2.373. It has a range of 11 points with a maximum score of 16 points and minimum score of 5.

Table 2: Descriptive Statistics of Financial Risk Tolerance of Equity Investors

Descriptive Statistics	FRT	IR	RCE	SR
Mean	28.612	10.549	11.298	6.764
Std. Deviation	5.656	2.373	2.709	1.807
Range	31	11	15	7
Minimum	13	5	5	3
Maximum	44	16	20	10
No. of Observations	802	802	802	802

Source: Primary Data

The second construct measures risk comfort and experience and are popularly referred to as financial risk also. It measures the respondents ability to take risk in daily life situations with statements like “You have just finished saving for a “once -in-a- lifetime” vacation. Three weeks before you plan to leave you lose your job. What would you do?” It also tries to capture the respondents’ general perception towards risk. This construct also comprises of five statements with minimum and maximum possible score of 5 and 20 respectively. The mean risk comfort and experience score of sample respondents is 11.298 with variation of 2.709.

The last construct, namely; speculative risk tries to capture respondents’ preference for guaranteed gain or probable gain. The investors who opt for a sure choice are less risk averters whereas those who go for gamble are risk takers. This construct comprises of questions with a maximum speculative risk score of 10 and minimum score of 3. The mean speculative risk score is 6.764 with a standard deviation of 1.807. The results exhibit moderate tolerance of investors towards risk.

The respondents have been further classified into two categories based on their overall mean FRT score of 28.612. the investors having FRT score of 28 or less have been classified as Below Average FRT group and those with FRT score of 29 or more have been classified as Above Average FRT group.

Table 3: Descriptive Statistics of Financial Risk Tolerance Categories

Descriptive Statistics	FRT Categories
Descriptive Statistics	Below Average FRT	Above Average FRT
Mean	24.01	33.08
Std. Deviation	3.33	3.42
N	395	407
Minimum	13	29
Maximum	28	44
Range	15	15
t-Value	-38.11
Sig. (2-tailed)	0.000

Source: Primary Data

Below Average FRT represents lower willingness and ability of investors to tolerate financial risk. Hence, they may be called as conservative investors. On the other hand, the Above Average FRT group comprises of investors who are ready to subsume more financial risk indicating towards their aggressive behaviour. The descriptive statistics of the two FRT groups have been presented in Table 3. The Below Average FRT group comprises of 395 investors. The minimum FRT score of this group is 13 and the maximum score is 28 thus defining a range of 15 points. For low risk seekers the mean FRT score is 24.01 with a standard deviation of 3.33 points.

The Above Average FRT group has 407 respondents with minimum FRT score of 29 and a maximum score of 44 points. The range for both the groups is 15 points. For high risk seeking group the mean FRT score and standard deviation are 33.08 and 3.42 respectively. The t-statistics representing the difference of mean across the two groups is -38.11 which is significant at 1 per cent level. It may be inferred from the results that the two FRT groups are significantly different from each other in their risk tolerance and the below average group is tolerant to significantly lower levels of risk when compared with above average FRT group (Hypothesis 1).

INFLUENCE OF DEMOGRAPHICS ON FINANCIAL RISK TOLERANCE:

Financial risk tolerance is not a static concept. It may vary across demographics. It also varies with time due to changes in the environmental and demographical factors. Numerous studies have examined the influence of demographics on FRT of varied group of people but the results are inconclusive as they are time varying, sample varying and methodology dependent.

The present study re-examines the influence of various demographic variables, namely, age, gender, marital status, education, occupation, income, investment experience and investment in equities on financial risk tolerance of the equity investors in India using a large sample size in the latest time frame. The data has been analysed using t-test and ANOVA (F test) to explore if there is any significant variation in FRT across various demographic variables.

Financial Risk Tolerance across Age:

Age is used as time tested measure to gauge an individual’s appetite for bearing losses or assuming risk. Conventionally, it is believed that as one gets older one’s propensity to tolerate risk reduces making him/her risk averse. The results of the study in Table 4 depict that age doesn’t have any significant influence on financial risk tolerance of the respondents.

Table 9: Influence of Monthly Income on Financial Risk Tolerance Categories

Risk Categories	Monthly Income	N	Mean	Std. Deviation	F-value	Sig. (2-tailed)	Bonferroni Post-hoc Test
FRT	Up to Rs. 25,000	221	27.79	5.12	27.678	0.000	Rs. 50.001- 75,000	Up to Rs. 25,000 (3.869 at 0.000 sig. level)
	Up to Rs. 25,000	221	27.79	5.12				Up to Rs. 25,000 (3.869 at 0.000 sig. level)
	Rs. 25,001- 50,000	373	27.47	5.32				Rs. 25,001- 50,000 (4.191 at 0.000 sig. level)
	Rs. 50.001- 75,000	106	31.66	5.82			Rs. 75,001 and above	Up to Rs. 25,000 (3.61 at 0.000 sig. level)
	Rs. 50.001- 75,000	106	31.66	5.82				Up to Rs. 25,000 (3.61 at 0.000 sig. level)
	Rs. 75,001 and above	102	31.4	5.76				Rs. 25,001- 50,000 (3.933 at 0.000 sig. level)
Below Average FRT	Up to Rs. 25,000	121	24.07	3.25	1.127	0.340
	Rs. 25,001- 50,000	211	23.79	3.43
	Rs. 50.001- 75,000	33	24.85	2.87
	Rs. 75,001 and above	30	24.33	3.29
Above Average FRT	Up to Rs. 25,000	100	32.29	2.86	15.35	0.000	Rs. 50.001- 75,000	Up to Rs. 25,000 (2.45 at 0.000 sig. level)
	Rs. 25,001- 50,000	162	32.27	3.02			Rs. 50.001- 75,000	Rs. 25,001- 50,000 (2.474 at 0.000 sig. level)
	Rs. 50.001- 75,000	73	34.74	3.86			Rs. 75,001 and above	Up to Rs. 25,000 (2.057 at 0.000 sig. level)
	Rs. 75,001 and above	72	34.35	3.58			Rs. 75,001 and above	Rs. 25,001- 50,000 (2.082 at 0.000 sig. level)

Source: Primary Data

It is evident from Table 9 that FRT is higher in income groups with monthly income more than Rs. 50001. The computed F-value (27.678) is highly significant at 1 per cent level. The findings support the general assertion that people with lower income are risk averse. The results are in conformity with results of Schooley and Worden (1996), Lee and Hanna (1991) and Grable and Lyton (1998). Thus, income may be used as a differentiating variable while classifying the investors into risk tolerance groups. The results of post-hoc test confirm that Income blocks up to Rs. 25,000 and Rs. 25,001-50,000 depicted statistically significant lower risk tolerance when compared with the remaining two higher income groups.

It is worth noting that investors who are basically conservative and lie in Below Average FRT group, their FRT does not get influenced by their monthly income. To the contrary, the Above Average FRT group depicts the behavior similar to overall FRT group. Further, Bonferroni Post-hoc test confirms that the two lower income brackets have significantly lower FRT than the two higher income brackets. It may be concluded that income of investors serves as differentiating variable for FRT of the investors and this exists primarily in the high risk tolerance group, cascading it to the overall results as well.

Thus, it may be inferred that demographic variables play an important role in deciding the financial risk tolerance levels of individual equity investors in India (Hypothesis 2).

CONCLUSION:

Measuring financial risk tolerance of individual investors is indeed a challenging task that needs multi-dimensional inputs. Use of mere qualitative judgment based techniques does not offer a complete answer or a comprehensive measure of one’s financial risk tolerance. The present study has employed multi-dimensional risk assessment instrument suggested by Grabble and Lytton (1999). The mean risk tolerance score of the investors is 28.612 with a standard deviation of 5.656. The two risk tolerance categories, namely; Below Average Risk Tolerance Category and Above Average Risk Tolerance Category vary significantly in their attitude and tolerance towards risk with mean FRT scores of 24.01and 33.08 respectively. Age and marital status do not seem to influence the financial risk tolerance of the investors. Gender depicts that females are less tolerant to risk primarily in Below Average FRT category. Risk tolerance is also influences by education level. The lower income groups depict lower tolerance towards financial risk supporting the assertion the low income people get naturally risk averse. Risk tolerance substantially increases with increase in investors’ investment experience and amount invested in stock markets. It may be inferred that demographic variables play an important role in deciding the financial risk tolerance levels of individual equity investors in India. Investors’ risk tolerance though appears to be a personal and static concept, is a highly dynamic and complicated phenomenon which might get influence by factors beyond demographics. Attitude, socio-economic background, family, culture, nationality, etc. may also help to get a deeper insight into investors’ financial risk tolerance behaviour.

ACKNOWLEDGEMENT:

The research has been conducted from the Major Research Project funded by University Grants Commission, New Delhi.

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Received on 23.03.2018 Modified on 05.04.2018

Asian Journal of Management. 2018; 9(2):990-998.

DOI: 10.5958/2321-5763.2018.00156.7

Risk Categories	Age	N	Mean	Std. Deviation	F-value	Sig. (2-tailed)
FRT	18-30 Years	345	28.59	5.93	0.944	0.42
	31-40 Years	275	28.37	5.22
	41-50 Years	131	29.33	5.98
	51 Years and above	51	28.24	5.09
Below Average FRT	18-30 Years	170	23.84	3.36	0.722	0.54
	31-40 Years	141	24.28	3.42
	41-50 Years	57	23.68	3.29
	51 Years and above	27	24.30	2.63
Above Average FRT	18-30 Years	175	33.21	3.89	1.589	0.19
	31-40 Years	134	32.67	2.75
	41-50 Years	74	33.68	3.37
	51 Years and above	24	32.67	3.13

Risk Categories	Gender	N	Mean	Std. Deviation	t-value	Sig. (2-tailed)
FRT	Male	688	29.03	5.54	5.248	0.00
FRT	Female	114	26.08	5.69	5.248	0.00
Below Average FRT	Male	315	24.22	3.18	2.556	0.01
Below Average FRT	Female	80	23.16	3.74	2.556	0.01
Above Average FRT	Male	373	33.10	3.47	0.25	0.80
Above Average FRT	Female	34	32.94	2.87	0.25	0.80

Risk Categories	Marital Status	N	Mean	Std. Deviation	t-value	Sig. (2-tailed)
FRT	Married	605	28.65	5.54	0.31	0.75
FRT	Single	197	28.50	6.02	0.31	0.75
Below Average FRT	Married	299	24.12	3.23	1.217	0.22
Below Average FRT	Single	96	23.65	3.60	1.217	0.22
Above Average FRT	Married	306	33.07	3.28	-0.12	0.90
Above Average FRT	Single	101	33.12	3.81	-0.12	0.90

Risk Categories	Education	N	Mean	Std. Deviation	F-value	Sig. (2-tailed)	Bonferroni Post-hoc Test
FRT	10th	26	29.46	4.81	2.152	0.09
	12th	75	27.99	5.18
	Graduate	318	28.13	5.78
	Post Graduate	383	29.08	5.67
Below Average FRT	10th	9	24.22	3.15	3.647	0.01	Post Graduate	Graduate (1.070 at 0.02 significant level)
	12th	45	24.64	3.22
	Graduate	157	23.34	3.63
	Post Graduate	184	24.41	2.99
Above Average FRT	10th	17	32.24	2.75	1.281	0.28
	12th	30	33.00	3.05
	Graduate	161	32.80	2.97
	Post Graduate	199	33.40	3.83

Risk Categories	Investment Experience	N	Mean	Std. Deviation	F-value	Sig. (2-tailed)	Bonferroni Post-hoc Test
FRT	Up to 2 Years	239	26.73	5.44	13.111	0.000	Up to 2 Years	3-5 Years (2.695 at 0.000 sig. level)
	3-5 Years	283	29.43	5.39				6-10 Years (2.653 at 0.000 sig. level)
	6-10 Years	236	29.39	5.99				11 Years and above (2.7 at 0.02 sig. level)
	11 Years and above	44	29.43	4.15				11 Years and above (2.7 at 0.02 sig. level)
Below Average FRT	Up to 2 Years	143	23.08	3.3	7.245	0.000	Up to 2 Years	3-5 Years (1.477 at 0.000 sig. level)
	3-5 Years	121	24.55	3.4				6-10 Years (1.205 at 0.000 sig. level)
	6-10 Years	110	24.28	3.25				11 Years and above (2.637 at 0.000 sig. level)
	11 Years and above	21	25.71	1.45				11 Years and above (2.637 at 0.000 sig. level)
Above Average FRT	Up to 2 Years	96	32.18	2.8	4.469	0.000	Up to 2 Years	6-10 Years (1.664 at 0.000 sig. level)
	3-5 Years	162	33.07	3.34
	6-10 Years	126	33.84	3.91
	11 Years and above	23	32.83	2.53