Strat. Change 25: 485–500 (2016) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jsc.2075
RESEARCH ARTICLE
Financial Risk Tolerance among Indian Investors: A Multiple Discriminant Modeling of Determinants Manit Mishra International Management Institute, Bhubaneswar, Odisha, India
Sasmita Mishra Department of Business Management, CV Raman College of Engineering, Bhubaneswar, Odisha, India
The risk tolerance of individual investors is assessed by taking into consideration the personality trait of materialism and socioeconomic particulars. A multidimensional evaluation of investor profile based on materialism, age, and gender would aid practitioners in precise targeting of prospects. Segregation of investors into above‐average and below‐average risk tolerance would facilitate customization of a product mix to suit the financial temperament of prospective investors.
A
bove‐average risk tolerance is strongly associated with greater materialism, younger age, and male gender, while a variety of characteristics comprising
materialism, age, gender, and ratio of earnings to total family members discriminate between the risk tolerance levels of individual investors.
Researchers, particularly economists, have been fascinated with the construct of risk for a long time, resulting in its profound dissection. Game theorists have thrived on the concepts of risk and return in different rational decision‐making situations. In fact, John von Neumann and Oskar Morgenstern invented a theory that measured how much an individual is desirous of a return, by the size of the risk she is willing to take to get it (Binmore, 2008, p. 8). Cox and Rich (1964) identified the economic cost of acquisition as the most commonly discussed element of risk. Later, Jacoby and Kaplan (1972) broadened the horizons of the definition of risk and suggested five independent types of risk: financial, performance, physical, psychological, and social. The present study pertains to the financial risk tolerance of an individual as indicated by their preference for different investment options – from the more risky ones to the less risky ones. Grable (2000, p. 625) defined financial risk tolerance as ‘the maximum amount of uncertainty that someone is willing to accept when making a financial decision.’ The construct of risk tolerance, or an individual’s attitude toward accepting risk, has implications for financial service providers as well as consumers. For the former, a proper assessment of a prospective investor’s financial risk tolerance enables them to design a heterogeneous but appropriate product mix of investment options (Jacobs and Levy, 1996), while for the latter, it aids in offering customized asset composition in a portfolio such that it is in accordance with the risk and return expectations of the individual (Droms, 1987). The present study offers a unique perspective on financial risk tolerance, on account of two aspects. First, it has been emphasized that risk tolerance is a
Copyright © 2016 John Wiley & Sons, Ltd. Strategic Change: Briefings in Entrepreneurial Finance
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complicated process, going beyond demographic and socioeconomic factors (Grable and Lytton, 1999b), and researchers have seldom forayed beyond the demographic determinants. This study includes the individual value of materialism to investigate risk tolerance. It is pertinent to mention here that both the constructs ‘financial risk tolerance’ and ‘materialism’ are dimensions of an individual’s personality and both have a bearing on the decisions related to the end use of disposable income. Second, financial risk tolerance has mostly been investigated as an individual phenomenon. However, Indians take a chronologically long‐term view of their investment decisions, and the principal objective of investment is to do one’s duty (‘dharma’) of providing for progeny (Jain and Joy, 1996). The family has greater traction in influencing financial risk tolerance for oriental investors than occidental investors. Hence, the constitution of the family as a unit in terms of number of earning members vs. total number of members should play an important role in the risk tolerance behavior of the earning members of the family. Accordingly, the ratio of earning members to total number of members in the family (E/T) for each respondent has been included as an independent variable differentiating between above‐average and below‐average risk tolerance groups. It acts as a more appropriate substitute for marital status/number of dependents in the Indian context. This also broadens the horizons of risk tolerance research by taking it beyond the normally used socioeconomic determinants of age, gender, marital status, income, and occupation. The present study intended to fulfill two objectives: (a) to determine whether a set of socioeconomic parameters and an individual consumption value can distinguish between levels of financial risk tolerance, as individual variables and as a weighted variate; and (b) to empirically investigate the extent of the contribution of the individual value of materialism and socioeconomic determinants toward separation of above‐average risk tolerance investors from those having below‐average risk tolerance, as a variate. Copyright © 2016 John Wiley & Sons, Ltd.
Review of the literature Determinants of financial risk tolerance Demographic parameters such as gender, age, education, income, occupation, etc. have been of particular interest to researchers as independent variables shaping investors’ financial risk tolerance (see MacCrimmon and Wehrung, 1986; Riley and Chow, 1992). An individual’s ability to tolerate risk has been found to be contingent on characteristics such as age, time horizon, liquidity needs, income, investor knowledge, and attitude toward price fluctuations (Fredman, 1996). Despite a plethora of research on the relationship of various socioeconomic factors with the risk tolerance of individual investors, a brief assessment of the results indicates a clear lack of consensus on the issue. Grable and Lytton (1999b) carried out an exhaustive investigation to assess the role of demographic, socioeconomic, and attitudinal factors as determinants in shaping financial risk tolerance. The authors concluded that the two most effective risk tolerance differentiating factors are education and financial knowledge, with an investor’s education explaining the greatest amount of variance in the risk tolerance. Contrary to popular notions, the study found that gender, age, and marital status had a lesser impact on the risk tolerance of an individual. At the same time, the study challenged the belief that the age of an investor is negatively related to the investor’s risk tolerance. On the contrary, it found that the mean age of the individuals in the above‐average risk category was greater compared with the mean age in the below‐average risk category. In the Indian context, Purkayastha (2008) studied the impact of demographic factors like age, occupation, income, and number of dependents on risk tolerance. The author concluded that younger investors and those with a high income are more risk tolerant. However, in the second stage of the research, the author attempted to determine the actual investment pattern of customers having a specific demographic profile and risk tolerance level. Surprisingly, a reality check revealed that customers Strategic Change DOI: 10.1002/jsc
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prefer to invest their money in an average‐risk mutual fund, irrespective of their demographic profile and risk tolerance capability. While it is not expected that there would be a conformance in the findings of research carried out in diverse geographic regions using dissimilar research methods, nevertheless the disparity in findings is disconcerting. It brings to the fore the lack of conclusive evidence, and therefore unanimity, amongst researchers on the relationship between financial risk tolerance and socioeconomic variables. More importantly, it presents a reservoir of research opportunities to explore the unknown. The remaining portion of this literature review elucidates the findings related to the impact of individual socioeconomic determinants on financial risk tolerance. Age Investors belonging to different age groups are known to vary significantly with regard to their choice of investment. Young investors (26–35) have been found to prefer mutual funds, while middle‐aged investors (36–45) have shown an inclination toward debentures/bonds as investment options (Mittal and Vyas, 2007). Wallach and Kogan (1961) are believed to be pioneers in studying the relationship between risk tolerance and age. Their early experimental research used choice dilemmas, which indicated that older individuals were less risk tolerant than younger individuals. This can be explained inductively by the fact that older individuals have less time to recover losses from riskier investments than do younger individuals, and as such, risk tolerance decreases with age. Harlow and Brown (1990) added another supporting justification by suggesting that biological transformations due to the process of ageing may also be responsible for making individuals less risk tolerant as they grow old. An inverse relationship between risk tolerance and age is substantiated by the works of many researchers (Dahlback, 1991; Jagannathan and Kocherlakota, 1996). The findings tend to build on the argument that, as an individual grows old, her stream of future income decreases, diminishing the Copyright © 2016 John Wiley & Sons, Ltd.
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value of the human capital endowed. Therefore, individuals tend to offset this decline in the value of their human capital by reducing the risk of their financial portfolio. Riley and Chow (1992) took research into the relationship between risk tolerance and age to an altogether new level by arguing that the relationship may not necessarily be linear. They found that risk tolerance increases with age until five years prior to retirement, and then decreases with age. This contention was supported by Hallahan et al. (2004), who found evidence of a negative (but non‐linear) relationship between risk tolerance and age. However, the inverse relationship between age and risk tolerance is debatable, since there are conclusions to the contrary wherein researchers have either found no significant relationship or a positive relationship (e.g., Grable, 2000; Hanna et al., 1998; Hariharan et al., 2000). Wang and Hanna (1997) carried out a profound study on the association between risk tolerance and age. The risk tolerance of an individual was measured by calculating the ratio of risky assets to total wealth, wherein an asset was considered risky if it provided an uncertain nominal cash flow (e.g., real estate, business assets, mutual funds, corporate stocks, precious metals, and pension assets in the form of stocks, bonds, or mutual funds). The authors concluded that risk tolerance increases with age, since the present financial resources of young people are limited and future wealth cannot be used to pay for present expenses, keeping other sociodemographic variables constant. The varied findings indicate that age, all by itself, may not be contributing to risk tolerance. A better understanding of the relationship is possible through concurrent consideration of a number of socioeconomic variables and individual values, including age. Gender In comparison with age, the association between risk tolerance and gender is comparatively much less debatable. Grable and Lytton (1997) investigated the financial attitudes of adults and suggested that men have a greater Strategic Change DOI: 10.1002/jsc
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tolerance toward risk compared with their female compatriots. Sung and Hanna (1996) concluded that households headed by a female member are less risk tolerant in comparison with households headed by a male member or a married couple. An extensive study on the role of gender in financial risk tolerance, with specific reference to students, was carried out by Garrison and Gutter (2010). Using a stratified random sample of 15,797 students drawn from 15 universities across the USA, the authors concluded that female students tend to exhibit lower financial risk tolerance from the moment they are expected to manage their own finances. Hallahan et al. (2004), in a study involving multiple demographic variables, found that women displayed a lower level of financial risk tolerance compared with men. Overall, there seems to be unanimity among researchers that men tend to exhibit greater financial risk tolerance compared with women, even though independence in taking financial decisions plays a role. Marital status/number of dependents Roszkowski et al. (1993) suggested that the number of dependents is inversely proportional to risk tolerance, since individuals with greater responsibilities act with more caution. In addition, individuals having a greater number of dependents are also likely to be affected by the potential social risk associated with undertaking greater financial risk. This proposition stands supported by Sunden and Surette (1998), who assessed risk tolerance by choice of pension plan and concluded that marriage, and therefore an enhanced number of dependents, makes both men and women less risk tolerant in their choices. Daly and Wilson (2001) opined that the increased responsibilities accompanying marriage and children will make a man less tolerant of risk. Hallahan et al. (2004) too found a significant negative relationship between risk tolerance and the marital status of individual investors. For the purpose of this study, the ratio of earning members to total number of members in the respondent’s family (E/T) has been used to reflect the enhanced/diminished Copyright © 2016 John Wiley & Sons, Ltd.
responsibility bestowed by marital status or number of dependents. As argued earlier, the authors believe this variable to be more representative of the pluralistic Indian culture, wherein the financial responsibilities of the breadwinner extend beyond the immediate family. Occupation Occupation refers to the principal activity in which an individual engages for pay. McClelland’s theory of personality states that an individual’s choice of occupation depends on whether they are motivated by achievement, power, affiliation, or security. Individuals motivated by achievement choose occupations with relatively higher economic and political risks (e.g., entrepreneurial ventures). Therefore, self‐employed individuals are likely to exhibit greater risk tolerance compared with salaried individuals (MacCrimmon and Wehrung, 1986). Masters (1989) found that non‐professionals (e.g., clerks, farmers, laborers) are less risk tolerant compared with professionals (e.g., educators, doctors, lawyers, businessmen, managers). Mittal and Vyas (2007) suggested that salaried people prefer to invest their money in equities and mutual funds; meanwhile self‐employed people were more inclined to invest their money in riskier options (e.g., debentures and real estate). Income Malkiel (1996, p. 401) argues that, ‘The risks you can afford to take depend on your total financial situation, including the types and sources of your income exclusive of investment income.’ A higher level of income should encourage greater risk tolerance, because greater wealth ensures access to more resources for investment and also serves as a cushion against the vagaries of the financial market. Bajlelsmit and VanDerhai (1997) analyzed investors’ choice of pension plan (with varying degrees of risk) to find that employees with a high income were willing to take more risk compared with employees with a low income. Many other researchers (e.g., Bernheim et al., 2001; Hallahan et al., 2004; Lee and Hanna, 1991; Riley Strategic Change DOI: 10.1002/jsc
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and Chow, 1992; Schooley and Worden, 1996) have found a significant positive relationship between income and risk tolerance. The present study investigated the simultaneous impact of the socioeconomic determinants of age, gender, occupation, income, and E/T, along with the individual consumption value of materialism. Materialism The tendency toward materialism is an inherent constituent of the human condition (O’Shaughnessy and O’Shaughnessy, 2002). Over the last two and a half decades, researchers have exhibited tremendous interest in the construct of materialism (e.g., Belk, 1984; Burroughs and Rindfleisch, 2002; Richins and Dawson, 1992). Belk (1987) defined materialism as ‘the tendency to believe that consumer goods and services provide the greatest source of satisfaction and dissatisfaction in life.’ Pollay (1986) defined materialism as ‘the belief that consumption is the route to happiness, meaning, and the solution to most personal problems.’ The contribution that revolutionized the way we think about materialism is that by Richins and Dawson (1992), who conceptualized materialism as a value. Rokeach (1973) has suggested that a value is a centrally held, enduring belief which guides actions and judgments across specific situations and beyond immediate goals to more ultimate end states of existence. Richins and Dawson (1992) defined materialism as ‘a value that guides people’s choices and conduct in a variety of situations but not limited to consumption arenas.’ The construct is based on three ‘orienting values’ – acquisition centrality, acquisition as the pursuit of happiness, and possession‐defined success. Materialistic individuals, with position acquisition at the center of their lives, believe that acquisition provides happiness, and define their own success as well as the success of others on the basis of their possessions. An analysis of materialism as a value leads one to conclude that it is: (1) a centrally held and enduring belief; (2) aimed for as an end state of existence; (3) influential beyond immediate goals. Copyright © 2016 John Wiley & Sons, Ltd.
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Materialism, as a value, has been widely researched (see Kamineni, 2005; Wang and Wallendorf, 2006). This study draws its strength from the definition of materialism as a value given by Richins and Dawson (1992), wherein they categorically suggest that the influence of materialism is aimed for as an end state of existence and extends beyond the immediate goals of consumption. The present authors posit that materialistic individuals, in their quest for greater consumption and possession, would warrant higher earnings and therefore display greater financial risk tolerance. Materialism and risk tolerance Risk tolerance has often been studied in relation to socioeconomic characteristics. This study broadens the horizons of risk tolerance research by assessing the impact of the psychological construct of materialism, in addition to the effect of socioeconomic determinants. The definition of materialism as a value has enabled researchers to study the ramifications of materialistic tendencies and comprehend its causal relationship with other constructs and variables of importance to marketers. This study investigates the relationship between materialism and financial risk tolerance. Materialism, for the purpose of this research, has been treated as an individual value whose impact extends to the domain of an individual investor’s risk tolerance. Materialists give prominence to possessions and the acquisition of objects in life. This desire to acquire is expected to cajole individuals toward investments with greater reward, notwithstanding the risk, thereby enhancing their risk tolerance. Materialists believe that possessions and their consumption are essential to their satisfaction (Belk, 1984). It is this belief which not only influences the ‘type and quantity of goods purchased’ to attain an end state of existence, but also influences the ‘allocation of variety of resources, including time’ to achieve an ultimate end state of existence beyond immediate goals (Richins and Dawson, 1992). This research determines whether the desire to attain an affluent end Strategic Change DOI: 10.1002/jsc
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state of existence elevates the materialistic individual investor to a position of greater financial risk tolerance. This premise draws sustenance from the findings of several researchers who have investigated the relationship between materialism and financial decision‐making. Richins (2011) suggested that materialism simultaneously leads to a more favorable attitude toward debt and a stronger belief that life transformations will occur as a result of acquisitions, and these two forces work together to increase credit overuse. Watson (2003) investigated the relationship between materialism and spending tendencies, saving, and debt. His findings showed a negative relationship between materialism and saving, such that highly materialistic people are more likely to be spenders whereas people with low levels of materialism are more likely to save through financial investments such as stocks and mutual funds. His findings also suggest a strong inclination on the part of highly materialistic people toward borrowing money for non‐essential purposes. It establishes the proclivity of materialistic individuals toward earning more money. More materialistic consumers have been found to be willing to carry heavier debt loads (Ponchio and Aranha, 2008), and exhibit a more positive attitude toward borrowing (Watson, 1998). Tang et al. (2008) measured students’ behavioral intentions, to find that a love of money was significantly correlated with risk tolerance. Further, investor risk tolerance is considered to be a psychological determinant of financial behavior (Jacobs‐Lawson and Hershey, 2005), and therefore is likely to be influenced by a hedonic value like materialism. The present study proposes that individuals with low levels of materialism would have lower financial risk tolerance, whereas highly materialistic individuals have a strong disposition toward money and hence exhibit greater financial risk tolerance.
Research summary There has been plenty of research on the impact of socioeconomic determinants on financial risk tolerance. Copyright © 2016 John Wiley & Sons, Ltd.
However, as the literature review suggests, there is a lack of consensus on the direction of impact for most demographic determinants. The justification for assessing the effect of socioeconomic variables on financial risk tolerance is due to Samuelson (1969). He listed the four most common reasons why ‘a young businessman can take more risk in the financial market than an old widow’ as: (1) the businessman is more affluent than the widow; (2) the businessman expects higher earnings in the future; (3) the businessman can recover any current losses in the future; and (4) the businessman has a much longer investment horizon compared with the old widow. In other words, the businessman has greater risk tolerance by virtue of his socioeconomic characteristics. At the same time, the variables are largely intertwined in their effect on risk tolerance, rather than being isolated. Morin and Suarez (1983) investigated risk tolerance through an assessment of investment in risky assets such as stocks, bonds, mutual funds, real estate (other than owner‐occupied home), equity in own business, and loans. Their findings integrate the impact of age and net worth (reflecting financial stability) on risk tolerance. They suggest that for those having low net worth, risk tolerance decreases with age whereas for high net worth, risk tolerance increases with age. Another study that integrates the impact of socioeconomic determinants is by McInish et al. (1993), who concluded that the relationship between net worth and risk tolerance was statistically significant for individuals aged 35 and above, but not for those below 35 years of age. Even as there is evidence to indicate the impact of materialism on financial decision‐making, there is a dearth of research on the impact of materialistic tendencies specifically on financial risk tolerance. An exception in this regard is the work of Richins (2011), who argued that a strong desire for wealth, goods, and life transformations on the part of materialistic individuals has resulted in financially risky behavior. This study carries forward the research through investigation of the contentious issue of the degree and direction of the simultaneous impact of socioeconomic Strategic Change DOI: 10.1002/jsc
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determinants and individual consumption values on financial risk tolerance.
Methodology Data The data for the study was generated from employees working in a higher education institute of some repute in the city of Bhubaneswar, Odisha, India during the period October to December 2014. The employees included in the sample were randomly chosen from a listing of all faculty and staff. One half of all employees (N = 600) received the questionnaire, on the basis of a random selection. The survey instrument used was a self‐administered questionnaire comprising 37 questions divided across three sections. The first section sought demographic information on the variables of age, gender, marital status, occupation, number of earning and total family members, and income. The second section required a response on risk tolerance using the 13‐item Grable and Lytton (1999a) scale, whereas the third section assessed the materialistic tendencies of respondents through the 18‐item Richins and Dawson (1992) scale of materialistic values. The response rate for the survey was 95.3 percent, with 286 questionnaires returned. However, six questionnaires were unusable due to missing values. Therefore, this resulted in 280 valid responses for this analysis, or a usable response rate of 93.3 percent (n = 280). Measurement The construct ‘risk tolerance’ is the dependent variable in the study. Roszkowski et al. (1993, p. 230) noted that when it comes to measurement of risk tolerance, most of the instruments are operationalized by various financial planning organizations for in‐house use. This limits their applicability in a wide variety of situations. Further, MacCrimmon and Wehrung (1986, p. 65) suggest the use of a questionnaire over any other method of measuring risk tolerance, since it ensures a response free from decision Copyright © 2016 John Wiley & Sons, Ltd.
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analyst influence. Therefore, for this research the authors used a scale which could capture the multidimensional nature of the financial risk tolerance construct, with established reliability and validity estimations. Grable and Lytton (1999a) operationalized financial risk tolerance on the basis of the three dimensions of investment risk, risk comfort and experience, and speculative risk. The scale consists of 13 items and satisfies the reliability and validity criteria (Grable and Lytton, 2001). For the purpose of the present study, the Grable and Lytton (1999a) scale is used to measure financial risk tolerance. Materialism and five other socioeconomic variables were considered as independent variables for the study. The scale developed by Richins and Dawson (1992) is more acceptable and applicable to consumers in varied cultures owing to the sheer diversity and depth of respondent profiles. Therefore, for the present study, the materialism of individual investors has been measured using the Richins and Dawson (1992) scale. Responses were also sought on the five socioeconomic variables of age, gender, ratio of earning members to total number of members in the family (E/T), occupation, and family income. Statistical analysis Financial risk tolerance cannot be treated as a continuum on which individuals can be assigned values (Grable and Lytton, 1998). This study was based on the premise that financial risk tolerance can be treated as high (above average) or low (below average). This approach has been used by several researchers for studying financial risk tolerance (Grable and Lytton, 1999b; MacCrimmon and Wehrung, 1986; Roszkowski et al., 1993). On the basis of this assumption, multiple discriminant analysis was used to classify respondents into risk tolerance categories using respondents’ materialistic values and socioeconomic characteristics. The study demanded a multivariate analysis so that multicollinearity can be taken care of while measuring the substantive effect. The assessment of the impact of materialism on risk tolerance is an important aspect of the study, and materialism is known to be influenced Strategic Change DOI: 10.1002/jsc
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by socioeconomic variables – e.g., age (Belk, 1985) and gender (Kamineni, 2005) – which are also part of the study. Multiple discriminant analysis as the research method has an advantage over multiple regression analysis since it can be used for a non‐metric dependent variable, in this case financial risk tolerance, and metric independent variables. In other words, it’s a mirror image of the method of MANOVA (Hair et al., 2006, p. 300). Multiple discriminant analysis has a heritage of previous use in financial risk tolerance research (Grable and Lytton, 1999b).
Table 1. Variable definitions
Variable
Measurement
Risk tolerance
1 = less than or equal to 29 (median value) 2 = greater than 29 Respondent’s score on MVS (Richins and Dawson, 1992) 0 = male 1 = female Respondent’s age (21–50) 0 = single 1 = married 0 = staff 1 = faculty Respondent’s family income per month in Indian currency Ratio of number of earning members to total number of members in the family
Materialism Gender Age Marital status Occupation Family income
Analysis and discussion Demographic characteristics of the sample The sample size exceeds the suggested minimum 5:1 ratio of observations to independent variables and the minimum group size of 20 observations (Hair et al., 2006, p. 337). The sample of 280 respondents comprised 100 women and 180 men. A total of 176 respondents were married and the rest (104) unmarried. The respondents who were employed as faculty numbered 172, whereas 108 were engaged in administrative occupations but not teaching. The age of respondents ranged from a low of 21 to a high of 51. The mean age of respondents was 32, with a standard deviation of approximately 7 years. The overall ratio of earning to total members in the family (E/L) was 0.505, with a standard deviation of 0.225. The average monthly family income for the respondents was 67,500 INR. Risk tolerance The risk tolerance score for the respondents had a low of 17 and a high of 40. The mean value was 29, with a standard deviation of 4.812 and a median value of 29. The construct risk tolerance was measured dichotomously, wherein respondents obtaining a score up to the median value of 29 were categorized as 1 and those securing more than 29 were categorized as 2 (see Table 1). Copyright © 2016 John Wiley & Sons, Ltd.
E/T
Materialism The materialism index scores ranged from a low of 21 to a high of 77, with a mean of 53.53, a standard deviation of 10.175, and a median of 54. Respondents’ scores on a materialism value scale (MVS) were used to generate the discriminant function. Stepwise multiple discriminant analysis results The mean and standard deviations of each independent variable were calculated for the two levels of risk tolerance: above average and below average. The data in Table 2 gives the different values of each independent variable for the two groups, and the statistical significance of the difference. There is a significant difference in the materialism scores of the two groups, such that respondents having greater materialistic tendencies exhibit above‐average risk tolerance in their financial decision‐making (p < 0.05). The difference between the above‐average and below‐ average risk tolerance groups for the variables of age and gender are marginally significant, at the 5 percent level. However, for the variables of occupation, ratio of earning members to total members in a family (E/T), and family income, the analysis could not find the difference to be Strategic Change DOI: 10.1002/jsc
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Table 2. Group means and standard deviations of classifying variables
Variable
Materialism Age Gender Occupation E/T Family income
Below‐average risk tolerance
Above‐average risk tolerance Wilks’ lambda
Mean
SD
Mean
SD
51.2750 33.1500 0.4500 0.6750 0.4862 71634
9.72174 8.19490 0.50383 0.47434 0.18369 41176.9
56.5333 29.8333 0.2333 0.5333 0.5303 62033
10.14289 5.60224 0.43018 0.50742 0.27340 40186.9
statistically significant. Gender being a dichotomous variable (0 for males and 1 for females), the mean value indicates the proportion of cases with a value of 1 (i.e., number of females). Therefore, while the above‐average risk tolerance group comprises 23.33 percent women and 76.67 percent men, the below‐average risk tolerance group consists of 45 percent women and 55 percent men. Similarly, faculty members comprise 53.33 percent of the above‐average risk tolerance group and 67.5 percent of the below‐average risk tolerance group. The average age for the above‐average risk tolerance group is approximately 30 years, whereas for the below‐average risk tolerance group the average age is approximately 33 years. Interestingly, the average family income of the above‐average risk tolerance group (62,022 INR p.m.) is less than the average family income of the below‐average risk tolerance group (71,634 INR p.m.). The ratio of earning members to total members is higher in the above‐ average risk tolerance group (0.5303) in comparison with the below‐average risk tolerance group (0.4862). The statistical significance of the independent variables in differentiating between the two levels of risk tolerance was determined using one‐way ANOVA. The univariate F and Wilks’ lambda values represent the separate or univariate effect of each variable, not considering the multicollinearity among the independent variables. The F‐statistic results given in Table 2 indicate that only materialism, gender, and age could successfully discriminate between the two levels of risk tolerance. In contrast, occupation, Copyright © 2016 John Wiley & Sons, Ltd.
0.934 0.949 0.950 0.979 0.991 0.986
F
Sig.
4.833 3.633 3.585 1.440 0.650 0.951
0.031 0.061 0.063 0.234 0.423 0.333
ratio of earning to total members in the family (E/T), and family income failed to discriminate in a statistically significant manner between the levels of risk tolerance. The findings are corroborated by Wilks’ lambda figures for each of the independent variables. Wilks’ lambda is defined as the ratio of the within‐ group sum of squares to the total sum of squares. A small value of lambda indicates a large difference between group means. The smallest Wilks’ lambda value is that for materialism, thereby suggesting its univariate ability to discriminate between the two levels of risk tolerance. Thus, as given in Table 2, the mean responses for materialism, gender, and age were the most different for above‐ average and below‐average risk tolerance groups. Meanwhile, the variables of occupation, E/T, and family income – when considered in terms of their isolated impact – failed to discriminate significantly between the two groups of risk tolerance. This analysis is not free from the impact of multicollinearity among independent variables. In order to determine the discriminant variate that will discriminate best between the above‐average and below‐average risk tolerance groups, the appropriate linear combination of independent variables under investigation needs to be identified. Toward this objective, multiple discriminant analysis has been used in the present study. Prior to conducting discriminant analysis on the data, it was tested for meeting the assumption of equal variance– covariance matrices across groups. Box’s M test was used Strategic Change DOI: 10.1002/jsc
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to assess the similarity of the dispersion matrices of the independent variables among the groups. The analysis failed to reject the null hypothesis that the dispersion matrices are homogeneous (Box’s M = 16.9, approximate F = 1.6, p = 0.106), thereby meeting the stringent condition for being subject to discriminant analysis. A stepwise‐ discriminant analysis was considered appropriate to meet the objectives of the research, since the purpose of this research was to identify the variables which can successfully differentiate between the above‐average and below‐ average risk tolerance groups. The second stage of analysis comprised an evaluation of the discriminant loadings or structure correlations given in Table 3. The authors do not emphasize the discriminant weights and the resulting discriminant function equation, since the objective is to identify the independent variables that have a strong relationship with group membership in the categories of the dependent variable of risk tolerance rather than building a predictive model. The discriminant loadings measure the simple linear correlation between each independent variable and the discriminant function, thereby reflecting the variance that a particular independent variable shares with the discriminant function. Thus, these loadings indicate the discriminating power of each independent variable, taking into consideration the interaction between and among the independent variables. The importance of the variables in the discriminant function is indicated Table 3. Pooled within‐group correlations between independent variables and the standardized canonical discriminant function
Variable
Risk tolerance coefficient
Materialism Age Gender Occupation* E/T Family income*
0.513 0.445 0.442 0.294 0.188 0.103
*
Variable not used in the final discriminant function.
Copyright © 2016 John Wiley & Sons, Ltd.
by the relative size of the absolute value of the coefficient. The more important variables have larger coefficients. Hair et al. (2006, p. 331) suggest that variables exhibiting an absolute loading of 0.40 or higher may be considered substantive. The variable that shared most variation with the canonical discriminant function was materialism (coefficient of 0.513), followed by age (coefficient of 0.445) and gender (coefficient of 0.442). Occupation, ratio of earning members to total members in the family (E/T), and family income possess comparatively less differentiating power between different levels of risk tolerance. Hence, the loadings tend to suggest that materialism has maximum power to discriminate between above‐ average and below‐average risk tolerance groups, followed by age and gender, respectively. The stepwise multiple discriminant analysis included the variables of materialism, age, gender, and E/T in the discriminant function. The discriminant function is highly significant (p = 0.003) and displays a canonical correlation of 0.461 (p < 0.01). The canonical correlation value suggests that approximately 21 percent (0.4612) of the variance in risk tolerance is accounted for by this model, which includes four independent variables. A high canonical correlation indicates a function that discriminates well between different groups. This finding stands substantiated by Wilks’ lambda value. The value of Wilks’ lambda for this discriminant function was 0.787 (p < 0.01). Thus, the discriminant function explained 21.3 percent of the variance in the financial risk tolerance scores within the sample. The stepwise discriminant analysis was able to identify a set of variables – namely materialism, age, gender, and E/T – that can provide a rather succinct and powerful distinction between the above‐average and below‐average risk tolerance groups. The Wilks’ lambda and univariate F‐statistic values given in Table 2 suggest the ability of materialism, age, and gender to discriminate among the groups, but only separately. However, the parameters depicting the overall model fit in Table 4 suggest a discriminant function comprising materialism, age, gender, and E/T that takes into consideration Strategic Change DOI: 10.1002/jsc
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Table 4. Overall model fit – canonical discriminant functions
Eigenvalue
Canonical correlation
Wilks’ lambda
Chi‐square
Sig.
0.270
0.461
0.787
15.778
0.003
multicollinearity among the various independent variables and simultaneously maximizes the between‐group variance relative to the within‐group variance. This is reflected in the inclusion of E/T in the discriminant function. The variable E/T did not have a significant discriminating effect when considered separately, but had enough unique variance to be part of the discriminant function eventually. The analysis suggests that collectively, the combination of variables materialism, age, gender, and E/T has a statistically significant strong relationship with the group membership of respondents in the above‐average and below‐average risk tolerance groups. Thus, the discriminant variate that successfully isolates the above‐average risk tolerance groups from the below‐ average risk tolerance groups comprises materialism, age, gender, and E/T. Predicting financial risk tolerance The overall model is statistically significant and explains 21 percent of the variation between the above‐average and below‐average risk tolerance groups. Therefore, an assessment of the predictive accuracy was made. The discriminant score obtained for each respondent was used to classify each respondent into above‐average or below‐
average risk tolerance categories. As shown in Table 5, out of 120 respondents in the above‐average risk tolerance category, the model was able to correctly classify 84 (i.e., 70 percent). Similarly, out of 160 respondents in the below‐average risk tolerance category, 116 were correctly classified (i.e., 72.5 percent). Overall, the discriminant function – including the independent variables of materialism, gender, age, and ratio of earning to total members in the family – correctly classified 200 out of 280 respondents. This pertains to 71.4 percent accuracy on the part of the derived model (p < 0.05).
Conclusion A concurrent assessment of the means for the independent variables included in the discriminant function (given in Table 2) and their respective discriminant loadings (given in Table 3) was done to determine the pattern of relationships between the risk tolerance groups and the independent variables of materialism, age, gender, and E/T. The variable which contributed most to explaining the difference between levels of risk tolerance is materialism. The analysis suggests that respondents high on materialism tend to exhibit above‐average risk tolerance. This is a rev-
Table 5. Classification results
Risk tolerance
Actual group
Predicted group Below average
Above average
Above average Below average
30 40
Copyright © 2016 John Wiley & Sons, Ltd.
Number
Percentage
Number
Percentage
21 11
70.0 27.5
9 29
30.0 72.5
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Manit Mishra and Sasmita Mishra
elation. Even though the impact of materialism on various financial decisions (e.g., savings and investments) has been investigated in the past, the specific effect of materialism on financial risk tolerance – which subsequently influences saving and investment decisions – has never been explored. With regard to the independent variable of age, the conventional wisdom that risk tolerance reduces with increasing age, withstands our scrutiny. A greater number of men in the sample demonstrated above‐average risk tolerance, whereas a greater number of women belonged to the below‐average risk tolerance group. Therefore, our findings regarding the impact of gender on financial risk tolerance tend to agree with past research, wherein men have shown greater financial risk tolerance compared with women. The inclusion of the independent variable of E/T was meant to cater specifically for the Indian culture. An earning member in India not only has responsibility toward their immediate family – comprising their spouse and children – but also toward their parents and even the extended family, comprising relatives. Therefore, this variable is likely to have a greater impact on risk tolerance compared with any other independent variable representing family burden (e.g., marital status). Independently, though, E/T did not have a statistically significant contribution to risk tolerance. Overall, the analysis discovered a discriminant function comprising materialism, age, gender, and E/T, which collectively act as a good discriminator of risk tolerance levels. The discriminant function accounted for approximately 21 percent of the variance between risk tolerance groups. In other words, financial risk tolerance, as a construct, could best be described by a linear combination of respondents’ materialistic tendencies, age, gender, and ratio of earning to total members in the family. Therefore, it was concluded that above‐average risk tolerance is associated with a higher level of materialism, lower age, male gender, and higher ratio of earning to total members in the family. Occupation and family income explained a comparatively smaller percentage of the variation in financial risk tolerance. The analysis based on the values of Copyright © 2016 John Wiley & Sons, Ltd.
Wilks’ lambda and canonical correlation led to the conclusion that the linear combination of variables worked to separate the two levels of risk tolerance from each other. On the whole, materialism emerged as the best discriminating factor between above‐average and below‐ average risk tolerance. The association of materialism with various socioeconomic (e.g., age, gender, and E/T) variables is well established. Materialism has also been studied extensively in the consumer behavior literature in terms of its impact as an independent variable on various constructs (e.g., innovativeness, attitude toward advertising). This study broadens the horizons of research on materialism by discovering its hitherto unexplored relationship with the construct of financial risk tolerance. In addition to establishing the relationship of materialism with risk tolerance, another significant finding of this study is the role of family responsibility in influencing risk tolerance levels, in collaboration with other variables. The ratio E/T is not able to influence financial risk tolerance as a separate variable. However, in association with materialism, age, and gender, it determines the level of risk tolerance as part of a variate. Thus, the seminal findings of this study are: (a) the discriminant function comprising materialism, age, gender, and ratio of earning to total members in a family (E/T) as a variate discriminates significantly the above‐average level of financial risk tolerance from the below‐average level of financial risk tolerance; (b) the above‐average financial risk tolerance group demonstrates significantly greater materialistic tendencies compared with the below‐average risk tolerance group; and (c) the ratio of earning to total members in the family (E/T) explains enough unique variance between risk tolerance groups so as to discriminate between above‐average and below‐average financial risk tolerance groups, in association with materialism, age, and gender.
Implications Financial service providers, financial investment planners, and other practitioners have to assess individual financial Strategic Change DOI: 10.1002/jsc
Financial Risk Tolerance among Indian Investors
risk tolerance. Mostly, these practitioners depend on unidimensional evaluations, objective heuristics, and intuitive subjective judgments to understand financial beliefs, pecuniary needs, and hedonic aspirations that shape financial risk tolerance. Such a piecemeal approach does not do justice to the complicated psychological construct of financial risk tolerance. This article broadens the horizons of assessment of risk tolerance by taking into consideration the personality trait of materialism as well as offering a combination of variables (including psychological and socioeconomic particulars) that effectively segregate investors into above‐average and below‐average risk tolerance. An appropriate assessment would enable practitioners to customize their product mix to suit the financial temperament of investors, while simultaneously fulfilling their financial objectives to an optimum extent. The variate obtained in the present study would enable a marketer to correctly identify the financial risk tolerance based on materialism, age, gender, and E/T ratio 71.4 percent of the time. The high degree of accuracy should provide confidence in the development of a strategy based on these results. The study identified isolated variables and a variate as effective risk tolerance differentiating factors. The findings reinforce the notion that the age and gender of respondents significantly influence their financial risk tolerance perceptions. Men tend to be more tolerant toward risk compared with women, and risk tolerance reduces with increased age. Practitioners, therefore, can continue to rely on age and gender to classify prospects into above‐average or below‐average risk tolerance groups. At the same time, practitioners must note that age and gender, when combined with materialistic tendencies and E/T ratio, influence an individual’s risk tolerance. A young materialistic male with fewer mouths to feed would exhibit substantially greater financial risk tolerance compared with an aged spiritual female with great fiscal responsibility toward the family. These are two cases at opposite ends of the spectrum, but nevertheless they aid in the portrayal and subsequent discrimination of a financially risk‐tolerant investor from a non‐risk‐tolerant Copyright © 2016 John Wiley & Sons, Ltd.
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investor. The identification of materialism as a key variable discriminating between risk tolerance levels has key implications for practitioners. The assessment of this personality trait at the level of an individual investor may be difficult, due to its psychological nature compared with inferring some of the socioeconomic attributes of a prospective investor. This, however, does not in any way diminish the importance of the finding that materialistic tendencies make individuals more risk tolerant. Societies across the world are gradually moving toward greater materialism. Financial planners and other practitioners can assess the materialistic values of the target market as a whole and thereafter, deduce the financial risk tolerance. This article makes an important contribution to the investor behavior literature by taking into account both subjective and objective characteristics while assessing a person’s financial risk tolerance. It is an improvement on the assessment of risk tolerance based on investor profiling using individual socioeconomic variables separately. A linear combination of materialism, age, gender, and E/T offers optimum discrimination between above‐average and below‐average financial risk tolerance groups. Investor profiling on the basis of the variate derived would provide an improved assessment of risk tolerance and, thereby, contribute toward ameliorated financial well‐ being of individuals and families. Notwithstanding the statistical and practical significance of the findings in the present study, the fact that the variate obtained explains a little more than 21 percent of the variance between the risk tolerance levels should encourage researchers to examine other psychological, psychographic, and demographic variables that could possibly add to the variance accounted for. A greater understanding of the financial risk tolerance construct and the variables shaping it would enable superior personal financial management by practitioners, researchers, and investors. References Bajlelsmit VL, VanDerhei JL. 1997. Risk aversion and pension investment choices. In Gordon MS, Mitchell OS,
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BIOGRAPHICAL NOTES
Manit Mishra is Associate Professor of Marketing and Quantitative Techniques at the International Management Institute, Bhubaneswar, Odisha, India. His teaching expertise includes marketing research, business analytics, and consumer behavior. His areas of research interest are hedonic consumption behavior, statistical modeling, marketing instrument validation, and multivariate consumer behavior analysis.
Sasmita Mishra is Assistant Professor of Finance and Accounting in the Department of Business Management, CV Raman College of Engineering, Bhubaneswar, Odisha, India. Her teaching expertise includes financial accounting and financial services. She is currently pursuing a Ph.D. in the area of behavioral finance at Centurion University of Technology and Management, India.
Correspondence to: Manit Mishra International Management Institute IDCO Plot No. 1, Gothapatna Bhubaneswar – 751003 Odisha, India email:
[email protected];
[email protected]
Copyright © 2016 John Wiley & Sons, Ltd.
Strategic Change DOI: 10.1002/jsc
