Recently, numerous studies have documented the instances of irrational behaviour and judgemental errors of individuals. Thus, determinants of trading behaviour and the risk tolerance level of individual investors had been the main focus of the growing area of finance known as ‘behavioural finance’. Empirical research studies which identify the underlying factors of trading behaviour based on the risk tolerance level of individual investors are not adequate.

Also, research pertaining to the effect of demographic factors towards risk tolerance factors and trading behaviour of individual investors are also found to be small in number. Hence, understanding the risk tolerance of individuals is imperative in understanding individuals’ decisions. Risk is found to be strongly associated with investors’ behaviour (McInish, 1982)¹. Indeed, most economic decisions are driven by primitive individual utility functions, including particular preferences for risk (Doubleday, 2002)². The individual’s perception of risk plays a vital role in influencing individual decision making process. In turn, the perception of risk determines the investing behaviour (Mayfield Perdue and Wooten, 2008)³. The term investor risk tolerance refers to an investors’ comfort level associated with investment variability or volatility (Schaefer, 1978)⁴. In general, one can expect individuals with low risk tolerance to act differently with regard to risk than individuals with a high risk tolerance. Someone with a high level of risk tolerance would be expected to accept a higher exposure to risk in the sense of taking sole responsibility, acting with less information, and requiring less control than would someone with a low level of risk tolerance (MacCrimmon and Wehrung, 1986)⁵.

Similarly, individual investors’ trading behaviour has grown over time and has attracted the attention of academicians. Financial advisors have long been recommending individual investors to refrain from frequent trading, as individual investors pay an exorbitant price for trading actively which may erode their profits or even result in systematic and economically large losses. Previous studies on individual investors’ performance have also provided support for financial advisors suggestions. As illustrated by Schlarbaum, Lewellen and Lease (1978)⁶and Barber and Odean (2000)⁷ individual investors could participate in financial market with better performance by following a simple buy-and-hold strategy, such as holding diversified portfolios. As an alternative choice, individual investors could diversify and enjoy market rates of returns by investing in equity mutual funds.

Several studies provided evidence that demographic characteristics influence investors’ decisions. Financial risk tolerance increases with age (Morin and Suarez, 1983)⁸; Females have lower preference to risk than males (Grable, 2000)⁹; Risk tolerance increases with education (Haliassos and Bertaut, 1995)¹⁰; Unmarried investors are more risk tolerant than married (Roszkowski, Snelbecker and Leimberg, 1993)¹¹ and risk tolerance increases with income and wealth (Cohn, Lewellen, Lease and Schlarbaum, 1975)¹². Researchers have also found support for relationship between trading behaviour and a variety of demographic variables (Barber and Odean, 2001¹³; Strong and Taylor, 2001¹⁴; Vissing-Jorgensen, 2004¹⁵; Feng and Seasholes, 2008¹⁶; Christiansen, Joensen and Rangvid, 2006¹⁷). However, the most frequently investigated demographic variables that are documented as antecedents to trading behaviour are age, annual income, education, and occupation. Hence, an attempt has been made in this study to identify the demographic factors affecting risk tolerance level and trading behaviour of the investors and also to find the relationship between risk tolerance level and trading behaviour of investors.

2. REVIEW OF LITERATURE:

In this session, the past studies related to the subject of the current research are reviewed. It includes studies on demographic factors affecting risk tolerance. It has been amply documented that risk is a factor that shapes individuals’ decisions, including financial and investment decisions (Lipe, 1998¹⁸; Yang and Qiu, 2005¹⁹). Keller and Siegrist (2006²⁰) found that one's financial risk attitude had a positive influence on willingness to accept investment risk and invest in stocks in one's portfolio. Individuals differ in characteristics due to demographic factors such as socio-economic background, education level, age, gender, marital status, and alike. Also every investor has his own investment objectives, risk tolerance levels, and other constraints in decision making process. Hence, it becomes imperative to study the various demographic characteristics that affect the risk tolerance levels and trading behaviour of investors.

2.1 Gender:

A person’s gender is one of the most researched factor to determine the risk attitude and trading behaviour of individual investors (Bajtelsmit and Bernasek, 1996²¹; Lewellen, Lease and Schlarbaum, 1977²²). Barber and Odean (2001)¹³ studied the difference in investment behaviour between men and women by analyzing the behaviour of more than 35,000 investors and found that, on average, men trade 1.5 times more frequently than women. They proposed that investors who tend to trade excessively take more risk and make poor investment decisions. Further, Hallahan, Faff, and McKenzie (2003)²³ and Frijns, Koellen, and Lehnert (2008)²⁴ found that males seem to be overconfident and undertake riskier behaviour than females. Hallahan, Faff and McKenzie (2004)²⁵ concluded that among a group of explanatory variables such as age, gender, marital status, education, income and wealth, gender has the most prediction power on risk tolerance. Further, in a study conducted by Pompian and Longo (2004)²⁶, women are found to be 33% more risk averse than men. Also, it is found that men look at portfolios more often than women do; Men are more likely to cut losses immediately, while women are more likely to buy and hold. Female investors are more risk averse than their male counterparts which is demonstrated by their more conservative investment behaviour. This claim is evidenced by a smaller number of market enquiries, lower trading volume and lower frequency of transactions attributable to females (Fellner and Maciejovsky, 2007)²⁷. According to Haarala (2008)²⁸, in Finland men have been found to be more willing to take risk with their invested assets than women.

Prince (1993)²⁹ showed that overconfidence leads men to take more risk in financial matters. Lundberg, Fox and Punccohar (1994)³⁰ further documented that overconfidence has been found to be more common among men. Flynn, Slovic and Mertz (1994)³¹ found that socio-political factors such as power and status favour men, resulting in their willingness to undertake higher risk. There is also contradictory evidence. Johnson and Powell (1994)³²and Schubert, Brown, Gysler and Brachinger (1999)³³ found that in specific circumstances, women appear as risk loving as men or even more loving. Also Charness and Gneezy (2004)³⁴ showed that males and females are equally successful in making decisions under condition of risk. Schubert (2006)³⁵ further showed that women appear less sensitive to probabilities and more pessimistic about gains than men. Ronay and Kim (2006)³⁶ have pointed out that there is no difference in risk attitude between individuals of different gender. Feng and Seasholes (2008)¹⁶ used data from a brokerage firm to show that Chinese men and women show similar investment behaviour.

2.2 Age:

The evidence from most research papers on age and risk tolerance suggest that older people are on average more risk averse than younger people (Lewellen et al. 1977²², McInish, 1982¹). Further, Donkers, Melenberg and Van Soest (2001)³⁷ studied risk aversion in a large survey with Dutch households and found that age affects negatively the willingness to take risk. Dohmen et al. (2005)³⁸ studied the relation of age and willingness to take risk with a German sample group. They also found evidence for risk aversion increasing with age. However, the effect is stronger in sports and leisure than in financial matters.

Riley and Chow (1992)³⁹ suggested that risk aversion decreases with age until 65 years is reached and starts decreasing again after that. Hallahan et al. (2004)²⁵ also find support for the non-linear relationship between age and risk tolerance by adding age squared as an independent variable into their regressions. Very recent studies about Finnish investors’ risk tolerance also supported the view that risk aversion increases with age. This finding had been made by Haarala (2008)²⁸ in her study with 10,000 Finnish investors and Alanko (2009)⁴⁰ in his very extensive study including risk profiles and true asset allocations of over 85,063 Finnish bank customers. Kaustia (2003)⁴¹ also found evidence for negative correlation between age and willingness to take risk in their Finnish sample.

According to Bodie and Crane (1997)⁴² and Strong and Taylor (2001)¹⁴, people rebalance their portfolio in favour of fixed income securities as they get older. The logic is that as people age they are more risk averse and hence prefer to invest in less risky assets. Young investors, unlike older investors, can adjust their current consumption downward and use some leisure time to compensate for losses in portfolios which is not possible by aged people.

There are also some contrary evidences. Yoo (1994)⁴³ and Heaton and Lucas (2000)⁴⁴reported a positive relation between investors age and percentage of equities in portfolios which means aged people take more risk. Summers, Duxbury, Hudson and Keasey (2006)⁴⁵ found that investors become more risk seeking with age. Poterba (2001)⁴⁶, Poterba and Samwick (1997)⁴⁷ and Feng and Seasholes (2008)¹⁶ found no significant relationship between investor’s age and the percentage of equities in investor’s portfolio.

2.3 Education:

Investor’s educational level as a measure of individual earning power is considered as one of the determinants of trading behaviour. This variable is expected to be highly correlated with investors’ income. Schooley and Worden (1996)⁴⁸ reported that American investors with high-school diplomas tend to hold portfolios heavily biased towards fixed-income securities, which are seen as less risky than equities. Dorn and Huberman (2005)⁴⁹ reported that wealthier investors and those with a college degree turn over their portfolio less frequently.

Christiansen et al. (2006)¹⁷ found that investors with a higher education invest a larger fraction of asset in stocks and bonds. These findings lend further support to proposition made in several studies, which state that ‘the level of education is also of importance for whether or not an investor participates in the bond and stock market. Moreover, well-educated individuals are more likely to be financial investors’ (Mankiw and Zeldes, 1991⁵⁰; Bertaut and Haliassos, 1997⁵¹; Guiso, Haliassos and Jappelli, 2003⁵²).

Graham, Harvey and Huang (2009)⁵³ found that investors with higher income or more education are more likely to perceive themselves as knowledgeable investors than investors with lower income or less education.

2.4 Income and Wealth:

Income and wealth is another important factor determining the investment behaviour of individual investors. According to Friedman (1974)⁵⁴and Cohn et al. (1975)¹², wealth and income are expected to correlate positively with individuals’ risk taking. A study by Riley and Chow (1992)³⁹with American households found that an increase in income and wealth decreases the average risk aversion of households. Grable and Lytton (1999)⁵⁵ also found that wealth is one of the determinants of investors’ risk attitude. Their results show that there is a positive correlation between risk seeking and wealth.

Vissing-Jorgensen (2004)¹⁵analyzed the 1998 and the 2001 Survey of Consumer Finances and found that wealthier investors make much more trades than less wealthy investors. Recently, Dorn and Huberman (2005)⁴⁹analyzed a sample of online broker investors and found that wealthier investors place more trades but turn over their portfolio less frequently. In contradiction, Schooley and Worden (1996)⁴⁸ conducted a study among American families and reported that wealthy people are more conservative with their money, whereas people with low levels of personal wealth are willing to take more risk.

2.5 Other factors:

There are other factors like marital status (Lazzarone, 1996⁵⁶), Occupation (Roszkowski et al. 1993¹¹) and experience which have an influence on the trading and risk tolerance behaviour of individual investors. Barber and Odean (2001)¹³ found that investors are more likely to be overconfident when they are less experienced as they learn about their true ability through experience. This implies that overconfidence would decrease with experience which would further reflect in their trading. Dhar and Zhu (2006)⁵⁷ showed that psychological biases of investors indeed decrease with trading experience due to the learning effect.

3. RESEARCH METHODOLOGY:

In this descriptive study, questionnaire is used as an instrument to collect the data. The geographical area of Coimbatore city is chosen as the sample area for the study because the researcher is located at Coimbatore and is familiar with the place. Snowball sampling technique is used to choose the respondents. The researcher prepared a list of friends, relatives and colleagues who are active traders in stock market. Accordingly, the researcher prepared 500 questionnaires and distributed them to few of the respondents directly. The researcher also distributed the questionnaires to the employees of stock broking services firms to collect data from their customers. Finally, the researcher could collect 455 completely filled questionnaires and they are considered as the sample respondents of the study and used for further analysis.

Descriptive statistics (mean, frequency, and percentage), Chi-Square test and ANOVA are employed for analysing the collected data. Chi-Square test is used to test the goodness of fit, the independence of attributes and to combine various probabilities obtained from independent experiments to give a test of significance. Thus, in this study, the analysis pertaining to test of association are done using Chi-Square analysis. ANOVA provides a statistical test of whether or not the means of several groups are all equal. To test the significant difference in the demographic factors on the trading behaviour of investors ANOVA is used.

4. RESEARCH QUESTIONS AND OBJECTIVES OF THE STUDY:

Individual investors have started trading too often and to their detriment. Though frequent trading may be profitable for brokerage firms, it is not profitable for most individual investors and it is found more risky. It is also said that demographic profile of individual investors has an effect on risk tolerance level and the trading behaviour of the investors. The more actively investors trade, the less they earn. It is reported that 20 percent of investors who traded most actively earned an average net annual return which is 7.2 percentage points lower than that of the least active investors (Barber and Odean, 2000⁷).

4.1 Research Questions:

In this background, the study has raised the following research questions:

§ Is there any association between demographic factors and risk tolerance level of investors?

§ Is there any relationship between risk tolerance level and trading behaviour of individual investors?

§ Does the trading behaviour of individual investors differ based on their demographic profile?

4.2 Objectives:

Based on the research questions raised in the previous section, the objectives of the study have been framed as follows:

§ To study the association between demographic factors and risk tolerance levels of individual investors.

§ To determine the relationship between risk tolerance level and trading behaviour of individual investors.

§ To identify whether there is any difference in trading behaviour based on the demographic profile of investors.

5. ANALYSIS AND INTERPRETATION OF RESULTS:

This session presents the analysis of data and interpretation of results. The data collected through the questionnaire are tabulated and appropriate statistical tools mentioned in the methodology have been applied in the process.

5.1 Demographic Characteristics of the Respondents:

The analysis of the demographic characteristics of the respondents is presented in this section. Demographic factors discussed in earlier research studies such as gender (Bajtelsmit and Bernasek, 1996²¹; Lewellen et al. 1977²²), age (Lewellen et al. 1977²², education (Mankiw and Zeldes, 1991⁵⁰; Haliassos and Bertaut, 1995¹⁰; Guiso et al. 2003⁵²), income and wealth (Grable and Lytton, 1999⁵⁵; Cohn et al. 1975¹²), occupation (Roszkowski et al. 1993¹¹) and marital status (Lazzarone, 1996⁵⁶) are considered for the study. Table 5.1 gives a comprehensive presentation on the demographic characteristics of the respondents.

Table 5.1 Demographic Characteristics of the Respondents

S.No.	Variables	Categories	Mean Value	Frequency	Percentage
1	Gender	Male	-	288	63.3
		Female		167	36.7
		Total		455	100
2	Age (in yrs)	Below 30	53.5	36	13.8
		31-40		93	20.4
		41-50		103	22.6
		Above 50		196	43.2
		Total		455	100.0
3	Marital Status	Married	-	385	84.6
		Unmarried		70	15.4
		Total		455	100.0
4	Annual Income (in Rs.)	< 3,00,000	6,50,000	58	12.7
		3,00,001-6,00,000		35	7.7
		> 6,00,000		362	79.6
		Total		455	100.0
5	Education	Upto School Level	-	98	21.5
		Diploma		18	4.0
		Under Graduate		100	22.0
		Post Graduate		211	46.3
		Professional Course		28	6.2
		Total		455	100.0
6	Occupation	Salaried	-	265	58.2
		Business		22	4.8
		Professional		9	2.0
		Not Employed (full time trading)		159	35.0
		Total		455	100.0

Table 5.2 Trading Characteristics of the Respondents

S.No.	Variables	Categories	Mean Value	Frequency	Percentage
1	Primary Mode of Trading	Offline	-	198	43.5
		Online		257	56.5
		Total		455	100.0
2	Place of Trading	Home	-	320	70.3
		Workplace		69	15.2
		Brokers office		51	11.2
		Others		15	3.3
		Total		455	100.0
3	Trading Capital (in Rs.)	< 1,00,000	5,75,000	62	13.6
		1,00,001-3,00,000		62	13.6
		3,00,001-5,00,000		75	16.5
		> 5,00,001		256	56.3
		Total		455	100.0
4	Trading Experience (in yrs)	Below 2	1.91	121	26.6
		2-4		252	55.4
		Above 4		82	18.0
		Total		455	100.0
5	Type of Trading	Long-term	-	159	34.9
		Day trading		212	46.6
		Both		84	18.5
		Total		455	100.0
6	Formal Course in Trading	Participated	-	141	31.0
		Not participated		314	69.0
		Total		455	100.0

Table 5.4.2 Age, Education, Occupation, Annual Income and Trading Behaviour-ANOVA Results

Demographic Factors		N	Mean	S.D	F Value	Sig. At 5% Level
Age (in yrs)	< 30	63	1.30	0.463	39.102	0.000
	31-40	93	1.22	0.413
	41-50	103	1.65	0.479
	> 50	196	1.75	0.434
	Total	455	1.56	0.497
Education	Upto School Level	98	1.33	0.471	37.951	0.000
	Diploma	18	1.00	0.000
	Under Graduate	100	1.35	0.479
	Post Graduate	28	2.00	0.000
	Professional Courses	211	1.75	0.435
	Total	455	1.56	0.497
Occupation	Salaried	265	1.41	0.492	46.393	0.000
	Business	22	1.27	0.456
	Professionals	9	1.00	0.000
	Full time trading	159	1.87	0.333
	Total	455	1.56	0.497
Annual Income	<3,00,000	58	1.00	0.000	107.220	0.000
	3,00,001 to 6,00,000	35	1.00	0.000
	>6,00,000	362	1.70	0.459
	Total	455	1.56	0.497

It can be interpreted from Table 5.4.2 that the mean value is higher for the respondents in the age group of above 50 years. So, it is evident that older age people trade frequently than younger age people. Further, the F value (39.102) suggests that there is a significant difference in trading behaviour based on age. Hence, hypothesis H_3a is rejected. The mean value is greater for respondents with higher educational qualification such as post graduation and professional education. Further, the F value (37.951) is significant which shows that there is a difference in trading behaviour based on education. Hence, hypothesis H_3b is rejected. Regarding the occupation, it can be seen that the mean value is higher for full time traders. Also, the F value (46.393) is significant which shows that there is significant difference in trading behaviour based on occupation. Hence, hypothesis H_3c is rejected.

It can be interpreted from the above table that the mean value is greater for higher income respondents. This is accordance to the findings of Vissing-Jorgensen (2004)¹⁵ and Dorn and Huberman (2005)⁴⁹ who found that wealthier investors placed more trades than less wealthy investors. Also, the F value (107.220) is significant which shows that there is significant difference in trading behaviour based on annual income. Hence, hypothesis H_3d is rejected. To summarise, there is significant difference between the select demographic variables (age, education, occupation and annual income) and trading behaviour of the investors.

6. CONCLUSION:

The study focused on the demographic factors of investors and its association on investors’ risk tolerance level. The papers also aimed at identifying the relationship between risk tolerance level and trading behaviour of the investors. Majority of the respondents are found to be aggressive risk takers. It is found that males have more risk tolerance when compared to female respondents. Also, respondents aged above 50 years and are found to be more risk tolerant and aggressive. With regard to the marital status, it is found that married respondents have high risk tolerance than unmarried respondents. Further, respondents with high educational qualification are more risk tolerant. Respondents who are engaged in full time trading are more risk taking in nature than those who are in the category of business, salaried or professionals. Also, respondents in high income category have high risk tolerance than respondents in lower income category. With respect to trading behaviour, it is found that the correlation coefficient between trading behaviour and intention towards trading is highly positive. Also, a strong positive relationship exists between risk tolerance and trading behaviour which depicts that as the risk tolerance increases in an individual, his trading frequency also increases. Aggressive investors are found to trade more frequently. To conclude, from this study it is evident that there exist a significant association between demographic factors and risk tolerance levels of investors and significant difference in the trading behaviour of the respondents based on demographic factors.

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Received on 15.05.2017 Modified on 17.06.2017

Asian J. Management; 2017; 8(3):901-911.

DOI: 10.5958/2321-5763.2017.00139.1

S. No.	Category	Number of Respondents	Percentage	Cumulative Percentage
1	Aggressive	222	48.8	48.8
2	Moderate	150	33.0	81.8
3	Conservative	83	18.2	100.0
	Total	455	100.0

Demographic Factors		Risk Tolerance			Total	Chi-Square Value	Sig. at 5% Level
Demographic Factors		Conservative	Moderate	Aggressive	Total	Chi-Square Value	Sig. at 5% Level
Gender	Male	11	69	208	288	266.23	0.000
	Female	72	81	14	167
	Total	83	150	222	455
Age (in yrs)	< 30	26	14	23	63	467.38	0.000
	31-50	29	121	46	196
	> 50	28	15	153	196
	Total	83	150	222	455
Marital Status	Married	50	122	213	385	87.70	0.000
	Unmarried	33	28	9	70
	Total	83	150	222	455