# Risk Appetite and Hedging Strategies: The Impact of Age Cohorts in Financial Markets

M. Aravind, C. G. Manojkrishnan

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M. Aravind, C. G. Manojkrishnan. Risk Appetite and Hedging Strategies: The Impact of Age Cohorts in Financial Markets. Cogent Economics & Finance, 2024, 12 (1), pp.2382354. 10.1080/23322039.2024.2382354 . hal-04846849

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# Risk appetite and hedging strategies: the impact of age cohorts in financial markets

M. Aravind & C. G. Manojkrishnan

To cite this article: M. Aravind & C. G. Manojkrishnan (2024) Risk appetite and hedging strategies: the impact of age cohorts in financial markets, Cogent Economics & Finance, 12:1, 2382354, DOI: 10.1080/23322039.2024.2382354

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# Risk appetite and hedging strategies: the impact of age cohorts in financial markets

M. Aravindª and C. G. Manojkrishnanᵇ

## ABSTRACT

The financial instruments are subject to market risk, and the degree of risk may differ. Numerous demographic and behavioral factors determine the willingness of retail investors to face market risk. This work examines the direct effect of investors' risk appetite on their risk mitigation strategies and the indirect effect of investors' risk appetite on hedging strategies through financial intermediaries. In both situations, the age cohort is fixed as a moderator. The data consists of 612 active stock market investors across India. The analysis has established partial mediation in the indirect path. The investors aged 26–40 appear to be risk-oriented, whereas those above 60 reported being risk-averse. These results show that risk appetite decreases with age, and people prefer stable returns to volatility as their age progresses. This research points to policymakers and intermediaries to design portfolio management services that fit the investors' age group.

## IMPACT STATEMENT

The purpose of this study is to examine whether the risk appetites (RA) of retail investors can have an impact on their hedging strategies (HS) in financial markets and how the age factor is influencing the risk aspiration of the investors. The mediation role of financial intermediaries (FI) in the risk mitigation process and how the service of financial intermediaries (FI) was availed of by investors across age cohorts were duly examined. The study results show that the young investors are more risk-oriented than the older ones and they prefer to rely on their own hedging strategies rather than the tips suggested by financial intermediaries. This result suggests that stock brokers and financial intermediaries must design a balanced decision-making board on the basis of the age group of the investors.

## ARTICLE HISTORY

Received 5 December 2023

Revised 11 June 2024

Accepted 5 July 2024

## KEYWORDS

Risk appetite; financial
intermediation; hedging
strategies; mediated
moderated analysis; age
cohorts

## REVIEWING EDITOR

Gazi Salah Uddin, Linköping
University, Linkoping,
Sweden

## SUBJECTS

Anthropology - Soc Sci;
Economics; Business,
Management and
Accounting; Economic
Psychology

# 1. Introduction

The portfolio theory establishes that risk and return strictures drive an individual's decision to invest in financial markets (Markowitz, 1952). Investors should take a greater level of risk to earn higher returns. However, the amount of risk that an investor is willing to take may vary from person to person. Aggressive investors will have a higher risk tolerance level than traditional investors. Determining the risk tolerance of an investor's age is an essential factor. Younger people are believed to have a higher risk tolerance than older people (Lippi & Rossi, 2020).

Risk appetite is the quantity of risk an investor is willing to accept to pursue their investment objectives. It depends upon how much financial gain or loss they could accept from their investment portfolio. Aggressive investors are said to have more risk appetite than conservative investors, as traditional investors are more risk averse and set moderate investment goals (Aven, 2013).

Indian investors' risk appetite is satisfactory, as their average risk tolerance ranges between 52 and 81. These scores were arrived at based on four key parameters: risk tolerance, loss aversion, financial mastery, and investor overconfidence (India Investment Personality Report, 2022).

<footnote>ªAssistant Professor in Business Administration, Government Law College, Thrissur, Kerala, India; ᵇFaculty HR, EM Normandie Metis Lab, EM Normandie Business School, Dubai, United Arab Emirates</footnote>
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Investors who cannot take risks may seek advisory services from financial intermediaries. Financial planners should assess an individual's risk comfort before suggesting an investment strategy (Potti, 2019). However, in India, there needs to be a mechanism to evaluate the competence of the various financial intermediaries operating in the stock market. The Security Exchange Board of India has already cautioned that intermediaries such as advisors, brokers, and research analysts are violating all regulatory norms in pursuit of more significant returns in the form of commissions (Thareja et al., 2023). Though financial intermediation is a comfortable method for risk mitigation, this scenario calls for due diligence before associating with a financial intermediary.

The regulators in India have introduced instruments like financial derivatives from 2000 onwards for hedging investment risk. Nevertheless, trading volume from the derivative market has surpassed the cash market within a short period; the retail investors least preferred these instruments to verge their investment risk. The trading mechanism in derivatives is different from conventional investment products, and retail investors in India need more knowledge and confidence to trade with financial derivatives. This scenario limits the scope of financial derivatives in a broader spectrum, and retail investors rely on intermediaries like brokers to manage their funds (Aravind, 2021). It is observed that now, the younger population has begun to use derivatives as a medium to manage and hedge their investment risk without seeking the assistance of intermediaries. It is a positive indication that the attitude toward market risk among the young generation in India is changing, and they are willing to take on more challenges to meet their investment goals.

Traditional investors can resort to hedged mutual fund schemes to manage their portfolios, and nearly 13.13 crore new folios have been created by mutual fund companies in India since April 2017 (Baid, 2022). The Indian investors in the average age group of 35 were reported to exhibit a solid inclination to invest directly in the stock market by avoiding the service of financial intermediaries. It is revealed that with increasing age, investors tend to become more comfortable with lump sum investments than Systematic Investment Plans (SIPs). It reflects that the investors' confidence and risk tolerance levels are linked with their age (Mint, 2023).

The above scenarios will post research questions on the linkage between investors' age and risk appetite, the age and the ability to use hedging instruments and the age and dependence on financial planners/intermediaries.

This research adds to the existing literature by examining the connection between risk appetite and the age of retail investors. It would also help the fund managers and investment expert to design their fund management strategies based on the age group of the clients. The major hypothecation of the research is that the age of investors can be a significant factor in their reliance on financial intermediaries.

# 2. Literature review

## 2.1. Risk appetite

The term risk appetite refers to the amount of risk that investors are willing to accept from the financial market (González-Hermosillo, 2008). An investor's risk appetite plays a crucial role in shaping his investment decision; therefore, it is inevitable that the critical determinants of risk tolerance be identified (Bayar et al., 2020). The risk aversion level negatively affects stock market expectations (Lee et al., 2015). The key demographic factors in determining the risk tolerance of retail investors are age, gender, number of children, marital status, income, wealth, education, financial literacy, work experience, financial market experience, culture, religion, and race (Fisher & Yao, 2017; Wang & Hanna, 1998).

The portfolio theory suggests that a rational investor maximize his return by minimizing risk and achieves the best result by choosing an optimal portfolio (Markowitz, 1952). The normative theory appeals to investors to consider the natural outcome of an investment rather than prior outcomes. However, investors must frequently reframe their portfolios to address the market risk (Thaler & Johnson, 1990). Sensation-seeking has been reported to be one of the inherent risk-taking behaviors for financial activities (Zuckerman, 1994). It involves experiencing pleasure or pain by taking a certain level of risk (Higgins, 1997).
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Grable and Lytton (1999) developed a risk tolerance scale by assuming a strong correlation between socioeconomic characteristics and individuals' financial risk tolerance. An individual's risk appetite is highly associated with guaranteed and probable return, risk experience, comfort in dealing with financial instruments, knowledge, interaction with others, and socioeconomic background (Grable, 2000; Hong et al., 2004; Kuzniak et al., 2015). Subjective financial knowledge is reported to influence investors' risk tolerance positively and subjective and objective financial knowledge are positively correlated (Grable & Rabbani, 2023).

According to Higgins (2002), the self-regulation of an investor can result in three important decision values: outcome value, the value from fit, and the value from the proper mean. The financial risk appetite of an individual will decrease with an increase in household size (Van de Venter et al., 2012). It is reported that men are more open-minded about investing in sophisticated financial instruments than women (Bannier & Neubert, 2016). Subsequently, it is reported that women are less risk-taking than men, and the risk-bearing tendency is higher in the younger generation than in the older group (Lippi & Rossi, 2020).

Another study established a partial mediation between age and risk tolerance level. However, it is tough to establish that the risk tolerance level of the older generation will decrease as the age progresses (Brooks et al., 2018). Banks et al. (2020) have investigated the reason for the change in the risk appetite of people of older age in European countries. The study reported that life events such as health shocks, retirement, widowhood, or marital change may affect their risk tolerance level.

Hirshleifer (2008) explains in the psychological attraction theory of financial regulation that the social transmission bias is a critical variable in shaping an investment decision. Variables such as self-esteem, personality type, and sensation seeking are also reported to be crucial in individual choices regarding wealth accumulation, portfolio allocation, and all other investment and finance-related decisions (Kannadhasan et al., 2016). Asset allocation can be essential for mitigating risk at the individual level to overcome future financial threats (Fiksenbaum et al., 2017; Nguyen et al., 2017).

The factors affecting their risk perception of equity investors are evidence screening, investment education, fear psychosis, fundamental and technical expertise, familiarity bias, information asymmetry, etc. (Singh & Bhattacharjee, 2019).

## 2.2. Financial intermediation

Financial intermediaries are specialized agencies that assist individuals or companies in shaping their financial transactions. Financial intermediation aims to get more symmetrical information from the market (Diamond, 1984). Intermediation helps gather vital information from various embedded knowledge networks (Sinclair, 2000). With the help of financial intermediation, an investor can transform his assets professionally with minimum effort (Merton & Bodie, 1993). The financial intermediation industry is highly reliant on the transaction cost of financial commodities, whereas their service can save time for individual investors (Benston & Smith, 1976).

People in financially advanced countries are willing to take more risk because they can avail themselves of intermediation services to mitigate adverse market shocks (Maggiori, 2017). Financial intermediaries' better services correlate with their client's financial risk tolerance scores (Hartnett et al., 2019).

Important attributes such as shared confidence in recognizing and managing risk and overcoming regulatory deltaic in investment are the key motivating factors for financial intermediation (Gilbey & Purchase, 2023; Kane, 1977). Investors also consider the length of their relationship with fellow investors, the risk allocation strategies suggested, the ability to arrange financial support, brokers' skills in managing contract positions, etc. (Malinvaud, 1973; Nguyen et al., 2016; Potti, 2019).

## 2.3. Hedging strategies

Various strategies an investor may employ to mitigate their market risk can be called hedging strategies (Aravind, 2021). The investors will allocate their portfolio to various classes like Futures, hedge funds, naked options, equities, real estate, long-term bonds, cash, money market, and short-term bonds (Corter & Chen, 2006). They can also consider derivative trading strategies like call options or covered put to
<figure><img src="image_15.png" /></figure>

mitigate the risk from the equity market (Shivaprasad et al., 2022). Deposit insurance, equity futures and options, index futures and options, hedged mutual funds, etc., are considered to be another viable form of hedging strategies to protect against market risk (Agarwal et al., 2009; Aravind, 2021; Hachicha et al., 2022; Merton & Bodie, 1993). Investors with sound financial market knowledge can consider advanced mitigation measures like cross-hedging (Jose et al., 2022).

**3. The proposed model**

The willingness of investors to take risks has a direct bearing on their financial decisions. To address the risk, retail investors may attempt to follow a variety of mechanisms, including operating via financial intermediaries or institutional investors (Bauer et al., 2023). There are established models to address risk appetite and risk premia through intermediated asset pricing (Adrian & Shin, 2010). The financial intermediaries can better serve the investors through new financial product designs, providing timely communication by using the latest technology and helping reduce risk aversion through the latest investment models (Merton, 1995).

Investors prefer to use independent financial advice as an investment in the stock market, which is challenging. The financial advisors will consider their clients' risk appetite and design an investment portfolio suitable to their needs (Brooks & Williams, 2022). Financial intermediaries have numerous methods to reduce the exposure and alleviate the impact of financial market shocks. They mainly specialize in hedging and risk distribution techniques more than retail investors (Kambhu et al., 2007).

The risk appetite of retail investors directly impacts the design of their hedging strategies. More risk-averse investors can hedge their investment portfolio through financial intermediaries. In this context, many researchers have pointed out that the risk appetite of investors varies across age groups (Fisher & Yao, 2017; Lippi & Rossi, 2020; Wang & Hanna, 1998). In order to articulate the impact of risk appetite across generations, the age groups proposed in the generation theory of Mannheim (1952) were duly employed in this model as moderators. Refer to Figure 1 for the proposed model.

**4. Methodology**

A cross-sectional research design model was employed to scan whether the risk appetites (RA) of the retail investors can impact their hedging strategies (HS) and how the age factor influences the risk desire of the investors. This paper also examines the mediation role of financial intermediaries (FI) in mitigating retail investor risk and how investors accepted this role across age cohorts. From the review of the literature, a draft questionnaire was designed with 75 variables in which 26 questions were based on risk

<figure><img src="image_16.png" /><figcaption>**Figure 1.** The model.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Table 1. Study variables.</figcaption></figure>

<table><thead><tr><td>Code</td><td>Variables</td><td>Source</td></tr></thead><tbody><tr><td>RA 01</td><td>Risk-Return Parameters</td><td>Markowitz (1952)</td></tr><tr><td>RA 02</td><td>Comfort in investing with Risky financial instruments</td><td>Kuzniak et al. (2015)</td></tr><tr><td>RA 03</td><td>To seek excitement</td><td>Zuckerman (1994)</td></tr><tr><td>RA 04</td><td>To enjoy Pleasure and Pain</td><td>Higgins (1997)</td></tr><tr><td>RA 05</td><td>self-regulation and distinct values</td><td>Higgins (2002)</td></tr><tr><td>RA 06</td><td>Risk- Experience and Knowledge</td><td>Grable and Lytton (1999)</td></tr><tr><td>RA 07</td><td>Subjective Financial Knowledge</td><td>Grable and Rabbani (2023)</td></tr><tr><td>RA 08</td><td>Guaranteed vs Probable return</td><td>Grable and Lytton (1999)</td></tr><tr><td>RA 09</td><td>Socio Economic background</td><td>Grable (2000)</td></tr><tr><td>RA 10</td><td>Gender Difference</td><td>Bannier and Neubert (2016)</td></tr><tr><td>RA 11</td><td>Age Class</td><td>Lippi and Rossi (2020)</td></tr><tr><td>RA 12</td><td>self-esteem, personality type, and sensation seeking</td><td>Kannadhasan et al. (2016)</td></tr><tr><td>RA 13</td><td>Asset allocation and Risk Perception</td><td>Nguyen et al. (2017)</td></tr><tr><td>RA 14</td><td>Financial Threats</td><td>Fiksenbaum et al. (2017)</td></tr><tr><td>RA 15</td><td>Social Transmission Bias</td><td>Hirshleifer (2008)</td></tr><tr><td>RA 16</td><td>Social interaction with fellow participants</td><td>Hong et al. (2004)</td></tr><tr><td>RA 17</td><td>Total size of household</td><td>Van de Venter et al. (2012)</td></tr><tr><td>RA 18</td><td>Reframing Investment Options</td><td>Thaler and Johnson (1990)</td></tr><tr><td>FI 01</td><td>Financial Risk Tolerance score</td><td>Hartnett et al. (2019)</td></tr><tr><td>FI 02</td><td>Length of Relationship</td><td>Nguyen et al. (2016)</td></tr><tr><td>FI 03</td><td>Shared confidence in management of risk</td><td>Gilbey and Purchase (2023)</td></tr><tr><td>FI 04</td><td>Availing Expert Knowledge</td><td>Sinclair (2000)</td></tr><tr><td>FI 05</td><td>Protection from Negative Shocks</td><td>Maggiori (2017)</td></tr><tr><td>FI 06</td><td>Overcoming Asymmetric Information</td><td>Diamond (1984)</td></tr><tr><td>F1 07</td><td>Rescue Transaction time</td><td>Benston and Smith (1976)</td></tr><tr><td>FI 08</td><td>Qualitative Asset Transformation</td><td>Merton and Bodie (1993)</td></tr><tr><td>FI 09</td><td>Surpass Regulatory dialectic</td><td>Kane (1977)</td></tr><tr><td>FI 10</td><td>Optimal Allocation of Risk</td><td>Malinvaud (1973)</td></tr><tr><td>FI 11</td><td>Orientation to Various Financial Instruments</td><td>Potti (2019)</td></tr><tr><td>FI 12</td><td>Frequent monitoring of position</td><td>Potti (2019)</td></tr><tr><td>FI 13</td><td>Arranging Financial Support</td><td>Potti (2019)</td></tr><tr><td>HS 01</td><td>futures, hedge funds, naked options</td><td>Corter and Chen (2006)</td></tr><tr><td>HS 02</td><td>equities</td><td>Corter and Chen (2006)</td></tr><tr><td>HS 03</td><td>Real Estate (excluding personal residences)</td><td>Corter and Chen (2006)</td></tr><tr><td>HS 04</td><td>long-term bonds</td><td>Corter and Chen (2006)</td></tr><tr><td>HS 05</td><td>Cash, Money Market, Short-Term Bonds</td><td>Corter and Chen (2006)</td></tr><tr><td>HS 06</td><td>Deposit Insurance</td><td>Merton and Bodie (1993)</td></tr><tr><td>HS 07</td><td>Call options</td><td>Shivaprasad et al. (2022)</td></tr><tr><td>HS 08</td><td>Covered Put</td><td>Shivaprasad et al. (2022)</td></tr><tr><td>HS 09</td><td>Commodities and Financial indices</td><td>Hachicha et al. (2022)</td></tr><tr><td>HS 10</td><td>Equity Futures and Options</td><td>Aravind (2021)</td></tr><tr><td>HS 11</td><td>Index Futures and Options</td><td>Aravind (2021)</td></tr><tr><td>HS 12</td><td>Hedged Mutual Funds</td><td>Agarwal et al. (2009)</td></tr><tr><td>HS 13</td><td>Cross Hedging</td><td>Jose et al. (2022)</td></tr></tbody></table>

Source: Secondary Data.

Note. RA: Risk Appetite, FI: Financial Intermediation & HS: Hedging Strategies.

appetite (RA), 24 inquiries were based on financial intermediation (PM), and 25 queries were exclusively focused on hedging strategies (HS). The classical principle component analysis (PCA) technique was used to reduce the dimensions of the data set. The varimax rotation in the SPSS software was used, and an at-most care was employed to suppress the variables with factor loading less than 0.8. The PCA has finally retained 44 variables for the three identified constructs, viz. Risk Appetite (18), Financial Intermediation (13) and Hedging Strategies (13). The retained variables and corresponding sources were duly presented in Table 1.

## 5. Data and sample

This study was carried out in 2022 as a significant increase in retail investment was reported in the Indian stock market. The population consists of retail investors with a trading account and a dematerialization account with a broker or a financial institution in India. The share of retail investors in the Indian stock market consists of 40.7% of the total volume traded and accounts for 11.7 million retail investors in January 2022. The contribution of retail investors to total trading volume fell to 36.5% in 2023, with an active number of 8 million traders (Rukhaiyar, 2023).
<figure><img src="image_18.png" /><figcaption>**Table 2.** Sample characteristics (N = 834).</figcaption></figure>

<table><thead><tr><th>Variable</th><th>Group</th><th>Frequency</th><th>Percent</th></tr></thead><tbody><tr><td rowspan="5">Age</td><td>18-25</td><td>117</td><td>19.1</td></tr><tr><td>26-40</td><td>162</td><td>26.5</td></tr><tr><td>41-60</td><td>121</td><td>19.8</td></tr><tr><td>Above 60</td><td>212</td><td>34.6</td></tr><tr><td>Total</td><td>612</td><td>100.0</td></tr><tr><td rowspan="3">Gender</td><td>Male</td><td>345</td><td>56.4</td></tr><tr><td>Female</td><td>267</td><td>43.6</td></tr><tr><td>Total</td><td>612</td><td>100.0</td></tr><tr><td rowspan="7">Occupation</td><td>Student</td><td>93</td><td>15.2</td></tr><tr><td>Govt. Service</td><td>86</td><td>14.1</td></tr><tr><td>Private Service</td><td>64</td><td>10.4</td></tr><tr><td>Self Employed</td><td>53</td><td>8.7</td></tr><tr><td>NRI</td><td>42</td><td>6.9</td></tr><tr><td>Retired</td><td>198</td><td>32.3</td></tr><tr><td>Other</td><td>76</td><td>12.4</td></tr><tr><td rowspan="7">Education</td><td>Total</td><td>612</td><td>100.0</td></tr><tr><td>High School</td><td>105</td><td>17.2</td></tr><tr><td>Higher Secondary</td><td>115</td><td>18.8</td></tr><tr><td>Under Graduate</td><td>112</td><td>18.3</td></tr><tr><td>Post Graduate</td><td>113</td><td>18.5</td></tr><tr><td>Professional</td><td>91</td><td>14.9</td></tr><tr><td>Other</td><td>76</td><td>12.4</td></tr><tr><td></td><td>Total</td><td>612</td><td>100.0</td></tr></tbody></table>

Source: Primary Data.

<table><caption>**Table 3.** Descriptive statistics.</caption><thead><tr><th>Constructs</th><th>Mean</th><th>Std. Deviation</th><th>Variance</th><th>Skewness</th><th>Kurtosis</th><th>Cronbach's alpha<br>(α)</th><th>No. Variables</th></tr></thead><tbody><tr><td>RA</td><td>4.07</td><td>0.949</td><td>.901</td><td>-0.43</td><td>-1.02</td><td>0.99</td><td>18</td></tr><tr><td>FI</td><td>4.51</td><td>0.891</td><td>.794</td><td>-1.61</td><td>1.41</td><td>0.95</td><td>13</td></tr><tr><td>HS</td><td>4.77</td><td>0.568</td><td>.322</td><td>-3.02</td><td>10.30</td><td>0.98</td><td>13</td></tr></tbody></table>

Source: Primary Data Analysis.

Note. RA: Risk Appetite, FI: Financial Intermediation &HS: Hedging Strategies.

The survey was conducted by using an electronic questionnaire. The reactions were instantly entered on Google Forms as the respondents marked their responses using electronic gadgets like tabs and smartphones. The informed consent of the participants was obtained for this study by declaring that the information obtained through the survey will be used for academic research and that the personal information about the respondents will be kept confidential. Initially, the respondents were asked, "Do you have any trading experience with the stock market?" Those who were affirmative to the above statement were retained for further survey. The responses were rated on a scale of 5 points ranging from extremely important (5) to least important (1). Finally, 612 valid responses were obtained, and the demographic profiles of the respondents were duly presented in Table 2.

The respondents who participated in the survey hold a stock trading account and a DEMAT account either with a broking firm or with a financial institution. Table 2 shows that 34.6% of the respondents fall within the age group of 60 and above. The sample consists of the age groups of 41-60 and 26-40, with a count of 19.8 and 26.5%, respectively. The survey also covered respondents aged 18-25, which accounts for 19.1% of the total response received. The sample ensured an equal number of responses from various age cohorts to ensure the right age mix for the moderation analysis.

Regarding gender, around 56.4% of the respondents are male, and 43.6% are female. At least 51.7% of the respondents have a college or professional degree. It shows that the sample is collected from well-educated groups of respondents. About 32.3% of the traders are retired from their regular jobs.

The sufficiency of the sample size for regression analysis and further modeling was examined using the power analysis procedure proposed by Faul et al. (2009). The G*Power software has suggested a sample size 567 for linear bivariate regression models at a 5% significance level (α = 0.05). Thus, from a
<figure><img src="image_19.png" /></figure>

statistical point of view, the obtained sample size of 612 is sufficient. In addition to this, it is observed
that as per Krejcie and Morgan (1970) criterion, a sample size of 384 is adequate for a population of 30
million at a 5% level of significance. In this context, a sample of 612 is appropriate for a population of
11.7 million retail investors.

**6. Results**

The descriptive statistics of the constructs are represented in Table 3.

In this research, the responses were rated on a five-point scale ranging from strongly agree (5) to strongly disagree (1). The mean scores of RA, FI, and HS represent the weighted average scores obtained for these constructs based on the respective variables indicated in Table 1.

Hedging Strategies (HS) were reported to have the highest mean score of 4.77 with a standard devi-
ation of 0.568. It points out that the respondents already recognized the importance of hedging strat-
egies to mitigate the market risk. The lowest mean score of 4.07 was obtained for Risk Appetite (RA);
however, the standard deviation of 0.949 indicates a deviation in respondents' opinion regarding their
willingness to take risks. The constructs RA, FI, and HS are negatively skewed with scores of -0.43,
-1.61, and -3.02, respectively. A skewness score between +1 and -1 shows that the distributions are
symmetric. In this study, the negative values indicate that the distribution has a longer tail on the left
side (Hair et al., 2022). The Kurtosis value of 10.30 for HS indicates that the responses for risk mitigation
strategies are highly peaked. In contrast, a kurtosis score of -1.02 for RA indicates that the response set
for the inclination to address risk is flatly curved. Acceptable values for skewness fall between -3 and
+3, and kurtosis is appropriate in a range of -10 and +10 while utilizing SEM (Brown, 2006).

Cronbach's alpha (Cronbach, 1951) is used to measure the reliability of the data set under the
renowned constructs. The obtained values are 0.99, 0.95, and 0.98 above the threshold score 0.70
(Shemwell et al., 2015). Thus, we can confirm that the data set used for this study is reliable.

The Kaiser—Meyer—Olkin (KMO) test was performed to check the usefulness of the data for factor
analysis (Kaiser, 1974). The obtained KMO value of 0.949 is above the threshold limit of 0.5. The devi-
ation of the correlation matrix from the identity matrix was evaluated using Bartlett's sphericity meas-
ures (Bartlett, 1951). A Chi-square value of 89066.32 was significant at the 95% confidence level. Thus,
the study has statistically confirmed the patterned relationship among the variables.

The Principle Component Analysis (PCA) was performed using the thumb rule of 'eigenvalues greater
than one' proposed by Kaiser (1960). The total variance for a single factor was 40.15%, below the permit-
ted limit of 50%. Thus, the chance of a standard method of variance was also ruled out (Podsakoff &
Organ, 1986). The rotated component matrix is duly exhibited in Table 4.

If the average variance extracted (AVE) is 0.5 and above, then the convergent validity of the con-
structs can be confirmed (Anderson & Gerbing, 1988). Table 5 shows that the constructs RA, FI, and HS
reported have AVE scores of 0.942, 0.990, and 0.986, are above the benchmark level of 0.5. The con-
structs are internally consistent as the composite reliability (CR) of the factor loadings are 0.993, 0.990,
and 0.986, respectively. It is above the standard limit of 0.7 (Hair et al., 2022).

The Fornell and Larcker (1981) criterion was used to inspect discriminant validity. Discriminant validity can be established if the square root of AVE is greater than the correlation among constructs. The square root of AVE given in Table 5 (italics) was higher than the correlation values of -4.20, -0.094, and -0.133.

In this research, a mediated moderated regression model was employed. Mediation analysis will
examine in a hypothecated causal chain how the independent variable will affect the dependent vari-
able in a scenario and how it affects the dependent variable through a mediated variable. Moderation
analysis examines when, where, in what circumstances, or what type of people will affect the result in
what magnitude (Hayes & Rockwood, 2017). In this study, RA is the independent variable, HS is the
dependent variable, FI is the mediator, and age is the moderator.

Hayes and Preacher (2013) PROCESS macro 4.2 versions were duly employed to test the mediation
effect. The indirect effect of RA on HS was initially measured by positioning FI as the mediator. Table 6
shows that RA negatively influences FI (β = -0.3946, T= -11.44). This result indicates that investors
with a high-risk orientation ignore the service of financial intermediaries as their risk-taking capability is
<figure><img src="image_20.png" /><figcaption>Table 4. Rotated component matrix.</figcaption></figure>

<table><thead><tr><th>Variable</th><th>Construct 1</th><th>Construct 2</th><th>Construct 3</th></tr></thead><tbody><tr><td>RA 01</td><td>.974</td><td></td><td></td></tr><tr><td>RA 02</td><td>.972</td><td></td><td></td></tr><tr><td>RA 03</td><td>.975</td><td></td><td></td></tr><tr><td>RA 04</td><td>.965</td><td></td><td></td></tr><tr><td>RA 05</td><td>.975</td><td></td><td></td></tr><tr><td>RA 06</td><td>.972</td><td></td><td></td></tr><tr><td>RA 07</td><td>.976</td><td></td><td></td></tr><tr><td>RA 08</td><td>.973</td><td></td><td></td></tr><tr><td>RA 09</td><td>.973</td><td></td><td></td></tr><tr><td>RA 10</td><td>.966</td><td></td><td></td></tr><tr><td>RA 11</td><td>.973</td><td></td><td></td></tr><tr><td>RA 12</td><td>.954</td><td></td><td></td></tr><tr><td>RA 13</td><td>.968</td><td></td><td></td></tr><tr><td>RA 14</td><td>.972</td><td></td><td></td></tr><tr><td>RA 15</td><td>.974</td><td></td><td></td></tr><tr><td>RA 16</td><td>.970</td><td></td><td></td></tr><tr><td>RA 17</td><td>.975</td><td></td><td></td></tr><tr><td>RA 18</td><td>.967</td><td></td><td></td></tr><tr><td>FI 01</td><td></td><td>.935</td><td></td></tr><tr><td>FI 02</td><td></td><td>.936</td><td></td></tr><tr><td>FI 03</td><td></td><td>.938</td><td></td></tr><tr><td>FI 04</td><td></td><td>.942</td><td></td></tr><tr><td>FI 05</td><td></td><td>.937</td><td></td></tr><tr><td>FI 06</td><td></td><td>.949</td><td></td></tr><tr><td>FI 07</td><td></td><td>.941</td><td></td></tr><tr><td>FI 08</td><td></td><td>.949</td><td></td></tr><tr><td>FI 09</td><td></td><td>.945</td><td></td></tr><tr><td>FI 10</td><td></td><td>.949</td><td></td></tr><tr><td>FI 11</td><td></td><td>.934</td><td></td></tr><tr><td>FI 12</td><td></td><td>.948</td><td></td></tr><tr><td>FI 13</td><td></td><td>.941</td><td></td></tr><tr><td>HS 01</td><td></td><td></td><td>.960</td></tr><tr><td>HS 02</td><td></td><td></td><td>.972</td></tr><tr><td>HS 03</td><td></td><td></td><td>.952</td></tr><tr><td>HS 04</td><td></td><td></td><td>.846</td></tr><tr><td>HS 05</td><td></td><td></td><td>.946</td></tr><tr><td>HS 06</td><td></td><td></td><td>.932</td></tr><tr><td>HS 07</td><td></td><td></td><td>.928</td></tr><tr><td>HS 08</td><td></td><td></td><td>.957</td></tr><tr><td>HS 09</td><td></td><td></td><td>.913</td></tr><tr><td>HS 10</td><td></td><td></td><td>.962</td></tr><tr><td>HS 11</td><td></td><td></td><td>.939</td></tr><tr><td>HS 12</td><td></td><td></td><td>.883</td></tr><tr><td>HS 13</td><td></td><td></td><td>.758</td></tr></tbody></table>

Source: Primary Data Analysis.

Note. Construct 1: Risk Appetite, Construct 2: Financial Intermediation &Construct 3: Hedging Strategies.

<table><caption>Table 5. Convergent and discriminant validity.</caption><thead><tr><th rowspan="2">Constructs</th><th rowspan="2">CR</th><th rowspan="2">AVE</th><th colspan="3">Fornell-Larcker criterion</th></tr><tr><th>RA</th><th>FI</th><th>HS</th></tr></thead><tbody><tr><td>RA</td><td>0.993</td><td>0.942</td><td>(0.971)</td><td></td><td></td></tr><tr><td>FI</td><td>0.990</td><td>0.887</td><td>-0.420</td><td>(0.942)</td><td></td></tr><tr><td>HS</td><td>0.986</td><td>0.848</td><td>-0.094</td><td>-0.133</td><td>(0.921)</td></tr></tbody></table>

Source: Primary Data Analysis.

Note: CR: composite Reliability, AVE: Average Variance Extracted

Diagonal values (in bold -italic) are squared roots of AVE; off-diagonal values are the estimates of inter-correlation between the latent constructs.

<table><caption>Table 6. Direct, indirect and total effects.</caption><thead><tr><th>Path</th><th>β</th><th>SE</th><th>T-Stat</th><th>p.value</th><th>LLCI</th><th>ULCI</th></tr></thead><tbody><tr><td>RA→FI</td><td>-0.3946</td><td>0.0345</td><td>-11.44</td><td>0.00*</td><td>-0.4623</td><td>-0.3268</td></tr><tr><td>FI→HS</td><td>-0.1331</td><td>0.0278</td><td>-4.787</td><td>0.00*</td><td>-0.1878</td><td>-0.0785</td></tr><tr><td>Indirect Effect</td><td>0.0525</td><td>0.0138</td><td>-</td><td>-</td><td>0.0263</td><td>0.0809</td></tr><tr><td>Direct Effect</td><td>-0.1087</td><td>0.0261</td><td>-4.163</td><td>0.00*</td><td>-0.1599</td><td>-0.0574</td></tr><tr><td>Total Effect</td><td>-0.0561</td><td>0.0241</td><td>-2.329</td><td>0.02</td><td>-0.1035</td><td>-0.0088</td></tr></tbody></table>

Source: Data Analysis.

Note. LLCI: Lower-Level Confidence Interval, ULCI: Upper-Level Confidence Interval.

*Significant at 5% level.
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>**Figure 2.** The path model with coefficients.</figcaption></figure>

<table><caption>**Table 7.** Moderation effect.</caption><thead><tr><th rowspan="2">Age cohorts</th><th colspan="6">RA→ HS</th></tr><tr><th>B</th><th>SE</th><th>T-Stat</th><th>p value</th><th>LLCI</th><th>ULCI</th></tr></thead><tbody><tr><td>18-25</td><td>-0.1540</td><td>0.1401</td><td>-0.1109</td><td>0.9124</td><td>-0.2906</td><td>0.2598</td></tr><tr><td>26-40</td><td>0.2490</td><td>0.0762</td><td>3.2740</td><td>0.0011*</td><td>0.0999</td><td>0.3994</td></tr><tr><td>41-60</td><td>0.0090</td><td>0.0853</td><td>0.1052</td><td>0.9162</td><td>-0.1586</td><td>0.1765</td></tr><tr><td>Above 60</td><td>-0.1962</td><td>0.1234</td><td>-1.5900</td><td>0.1123</td><td>-0.4385</td><td>0.0461</td></tr></tbody><thead><tr><th rowspan="2">Age Cohorts</th><th colspan="6">FI→ HS</th></tr><tr><th>β</th><th>SE</th><th>T-Stat</th><th>p.value</th><th>LLCI</th><th>ULCI</th></tr></thead><tbody><tr><td>18-25</td><td>-0.0217</td><td>0.1068</td><td>-0.2033</td><td>0.8390</td><td>-0.2314</td><td>0.1880</td></tr><tr><td>26-40</td><td>-0.2210</td><td>0.0757</td><td>-2.9203</td><td>0.0030*</td><td>-0.3692</td><td>-0.0724</td></tr><tr><td>41-60</td><td>-0.0758</td><td>0.0892</td><td>-0.8508</td><td>0.3952</td><td>-0.2509</td><td>0.0992</td></tr><tr><td>Above 60</td><td>0.6902</td><td>0.1441</td><td>4.7901</td><td>0.0000*</td><td>0.4072</td><td>0.9731</td></tr></tbody></table>

Source: Data analysis.

Note. LLCI: Lower-Level Confidence Interval, ULCI: Upper-Level Confidence Interval.

*Significant at 5% level.

high. The second conjecture was between FI and HS; it also reported a negative impact ($\beta$ = -0.1331, $T$ = -4.787). The indirect effect produced a $\beta$ value of 0.0525 (-0.3946* to -0.1331).

Moreover, there is no zero between the absolute confidence interval values (LLCI= 0.0263, ULCI= 0.0809). Thus, a partial mediation of FI can be established between RA and HS. In other words, the role of financial intermediation for risk mitigation was established among investors with low-risk tolerance levels. Figure 2 exhibits an overview of path analysis.

The direct effect of RA on HS was also examined through a simple path model, and the results show an inverse relationship ($\beta$ = -0.1087, $T$ = -4.16). This output confirms that investors with a high-risk appetite are less concerned about risk mitigation measures. This result can be further confirmed as no zero positioned between the confidence intervals (LLCI= -0.1599, ULCI= -0.0574).

The total effect has produced a $\beta$ coefficient of -0.0561, indicating a competitive mediation. Here, the direct and indirect effects are significant and point in the opposite direction. The direct effect confirms that risk-oriented retail investors are less concerned about market risk mitigation measures. In contrast the indirect effect suggests financial intermediation and hedging measures for investors with low-risk tolerance.

The moderation effect of age cohorts among the established paths was duly analyzed (refer to Table 7), and the results point out that age moderates the investors' risk appetite and the selection of hedging strategies. The investors aged 26-40 are more aware of risk management tools than other age groups ($\beta$ = 0.2490, $T$ = 3.274). This result can be confirmed as there is no zero in between the confidence intervals (LLCI= -0.0999, ULCI= -0.3994).

The moderated mediated model (indirect path) has produced some exciting results. The coefficient among the age group of 26-40 is negative and significant ($\beta$ = -0.2210, $T$= -2.9203), whereas that among the age group of 60 and above is positive and significant ($\beta$ = -0.6902, $T$= 4.7901). It shows that the investors within the age cohorts of 26-40 are risk-takers capable of designing home-made risk mitigation models. Conversely, investors within the age group of 60 and above are reluctant to take market risk, and they wish to avail themselves of the services of intermediaries to hedge the market risk.
<figure><img src="image_23.png" /></figure>

# 7. Discussion

The investor's risk appetite positively impacts their choice of financial intermediation. In the Indian context, intermediaries such as stock brokers are usually available to open share trading accounts, DEMAT accounts, advisory services, portfolio management services, etc. Investors with high-risk tolerance levels can limit the service of intermediaries up to account opening and maintenance services. Risk-averse people usually opt for risk management services offered by financial intermediaries. It would provide insurance for their investment as the risk is already passed on to the shoulders of professional agencies. The intermediaries are making this possible by pooling funds from a large number of retail investors and allocating funds to a large number of capital assets. Such capital allocation will enable the intermediaries to achieve risk neutrality (Hellwig, 2000).

The study has reported a competitive mediation as the direct effect of RA on HS is negative and significant. In contrast, the indirect effect of RA on HS through FI is positive and significant. These results open an avenue for intermediaries to grab business from risk-averse groups of investors. As investors' risk aversion increases, the negative expectations from the market creep; this mentality of retail investors can be capitalized by offering more customized services at the firm level (Lee et al., 2015). The intermediaries can go for Indigenous marketing strategies like orientation programs, distributing educational materials, facilitating mock trading opportunities, creating an opportunity to interact with successful players, etc., to attract low-risk tolerant investors towards their portfolio management services (Potti, 2019).

The investors aged 26-40 are more risk-oriented and wish to manage their investment portfolio independently. They are less concerned about the asset allocation strategies offered by various financial intermediaries. However, the investors falling to the other age groups are comparatively less risk tolerant and prefer intermediated hedging strategies. This result adds to the existing literatures of Wang and Hanna (1998), Fisher and Yao (2017) and Lippi and Rossi (2020). This research found that the investors falling within the age cohorts of 26-40 are risk takers, whereas those within the age group of 60 and above are reluctant to take market risk. These findings converse with the "Kundli' Survey" observations conducted by Mint (2023), which reported that investors tend to become more comfortable with risk with increasing age. This result also questions the outcome of Brooks et al. (2018), who reported that there is no evidence to show that the risk tolerance level of the old generation is lower than that of the youth. The study strongly points out that investors' risk tolerance levels are high in the early and middle stages of adulthood, and they tend to become more risk averse as their age progresses. It adds to the existing literature by agreeing that the younger generation is more risk-tolerant than the older generation (Lippi & Rossi, 2020).

As the age progresses, investors may have more limited memory, affecting their ability to process data more accurately. As a result, they may seek the help of experts to design their portfolios (Brooks et al., 2018). Life events such as health shocks, retirement, widowhood, or marital change can influence the risk tolerance level at older age (Banks et al., 2020). From an economic angle, aged investors are more risk and ambiguity-averse than youngsters and prefer to avoid losses. They aim to minimize potential loss rather than maximize the investment gain (Kurnianingsih et al., 2015). There should be a shift from equity-linked funds to debt instrument-based funds/balanced funds for aged investors as they are more risk averse and prefer fixed income. Fund managers and financial intermediaries should consider the age of their clients as an essential criterion for designing an investment portfolio.

These results help policymakers and intermediaries design portfolio management services befitting the age group of the investors. In Western economies, portfolio management companies suggest high-risk-bearing financial instruments to people with greater risk appetite. Consequently, ill-advised portfolio management products finally led to the global financial crisis (Devereux & Sutherland, 2010). Therefore, brokers and financial intermediaries must ensure a balanced decision-making board based on the investor's risk tolerance level (Kunnanatt & Emiline, 2012).

The major limitation of this study is that the investor's risk appetite was measured only among age cohorts. Secondly, the implications and policy suggested can be applied only among retail investors, and the outcomes and recommendations of this research do not apply to institutional investors and
<figure><img src="image_24.png" /></figure>

other high-net-worth investing groups. These limitations point towards the scope of some further studies on the topics like;

a. Risk appetite and intermediated hedging strategies, the moderating role of gender.

b. Institutional Risk Appetite and B2B risk mitigation strategies: The role of financial intermediation.

## Authors' Contributions

Aravind M.: Conceptualization, Methodology. Software, Validation, Formal Analysis. Resources, DataCuration. Writing – Original Draft Preparation, Manojkrishnan C.G.: Data Collection, Instrument designing, Writing – Review & Editing, Visualization, Supervision, Project Administration.

## Disclosure Statement

No potential conflict of interest was reported by the author(s).

## Funding

The authors received no direct funding for this research.

## Ethics committee approval

Ethical approval was obtained from the ethics committee of the Center for Economy, Development and Law (CEDL) established under Government Law College, Thrissur, Kerala.

## Informed consent

The authors have obtained verbal informed consent from the respondents to use the survey details for academic research and publication.

## About the authors

**Dr Aravind M.** is presently working as Assistant professor in Business Administration with the Government Law College Thrissur, Kerala, India. He has more than fifteen years of experience in academia and industry. He holds MBA and Ph.D degrees and has cleared UGC NET in Management. He has published more than 32 research articles out of which 16 of them are listed in reputed indices such as SCOPUS, WoS, ABS, ABDCand UGC CARE. He has presented research papers in several international seminars and conferences. He has also authored one book on Financial Management and published by Viva Books, New Delhi. He is also serving as an external expert in the editorial board of two national journals and one international journal. He has carried out numerous consultancy works for the government agencies as well as for the corporate. He is a lifetime member of Indian Accounting Association. Google Scholar: https://scholar.google.com/citations?user=Uz_aJbIAAAAAJ&hl=en ORCID: 0000-0002-0013-5488 Scopus: 57195431901

**Dr. Manojkrishnan Champettil Gopalakrishna Pillai** is an Assistant Professor in Human Resources and joined EM Normandie in 2023. He holds a Ph.D. in Human Resource Management (stress management) from the University of Kanchipuram in India, earned in 2011. His research and teaching areas include critical thinking, consumer behavior, stress management, leadership mindset, and work-life balance. With a notable publication record in Scopus-indexed journals such as the International Journal of Business & Society, the University of Malaysia, and others, he is the author of four books, including "Trust & Values in Pharma Industry" and "Stress Management in I.T Sector." Recipient of the "Exceptional Contribution to Management Education 2019" award, Manoj is recognized for his networking skills and professional connections with the private sector. He is also a motivation trainer and HR consultant, making significant contributions to the management program at the University of Kerala. Engaged in consulting assignments for both private and public enterprises, Manoj actively participates in alumni associations and professional committees. He also serves as the Controller of Academic Affairs.

## ORCID
<figure><img src="image_25.png" /></figure>

# Data availability statement

The data that support the findings of this study will be made available from the corresponding author, (Aravind M), upon reasonable request.

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# Appendix

## Risk appetite and hedging strategies: the impact of age cohorts in financial markets

**Declaration:** The information obtained through the survey will be used for academic research and the personal information about the respondents will be kept confidential.

### Questionnaire

#### Age:

a. 18-25

b. 26-40

c. 41-60

d. Above 60

#### Gender:

a. Male

b. Female

#### Occupation:

a. Student

b. Govt. Service

c. Private Service

d. Self Employed

e. NRI

f. Retired

g. Others

#### Education

a. High school

b. Higher Secondary

c. Under Graduate

d. Post Graduate

e. Professional

f. Other

<table><thead><tr><th></th><th>SA</th><th>A</th><th>N</th><th>DA</th><th>SDA</th></tr><tr><th></th><th>(5)</th><th>(4)</th><th>(3)</th><th>(2)</th><th>(1)</th></tr></thead><tbody><tr><td>A. Risk Appetite</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1. My investment is subject to Risk-Return Parameters of the portfolio</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. I do find comfort in investing with Risky financial instruments</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3. I wish to seek excitement through bearing market risk</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>4. I do enjoy Pleasure and Pain associated with outcome of my investment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5. My investment decision is subject to my own self-regulation and distinct values</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6. My Risk- Experience and Knowledge always helps me to shape my portfolio</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7. I have Subjective Financial Knowledge regarding the operation of stock market instruments and associated risks</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8. I will always compareGuaranteed vs Probable return to assess the risk of my investment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>9. My risk taking ability is associated with my Socio Economic condition</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>10. My gender is factor which affects my ability to bear market risk</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>11. Age Class can affect the risk taking ability of investors</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>12. Self-esteem, personality type, and sensation seeking etc. is a factor influencing risk tolerance level.</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>13. Asset allocation and Risk Perception will influence my portfolio choice</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>14. I am hesitated to invest in stock market due to my own financial pressure</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>15. I am not tuned to use modern financial instruments</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>16. I do assess investment risk through Social interaction with fellow participants</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17. My investment goal is associated with the size of my household</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18. I do bear market risk in order to reframe my investment goals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>19. I am concerned about losses along with return</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>20. I am willing to withstand some fluctuations in my investment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>21. Protecting my portfolio is most important that short term investment goals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>22. I do always safeguarded my investment by choosing stocks of well-known companies</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>23. I wish to maintain my portfolio for a long time horizon</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>24. I will seldom react on significant loss on my investment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>25. I am comfortable with short term losses from investment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>26. I will regularly track my investment position</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_29.png" /></figure>

<table><thead><tr><th></th><th>SA</th><th>A</th><th>N</th><th>DA</th><th>SDA</th></tr></thead><tbody><tr><td>B. Financial Intermediation</td><td>(5)</td><td>(4)</td><td>(3)</td><td>(2)</td><td>(1)</td></tr></tbody></table>

27. I do select my financial planner based on my financial risk tolerance level

28. I value long run of relationship with my financial planner

29. I wish to transfer my headache in management of risk to my financial planner

30. I prefer to availing expert service from qualified persons to manage the market risk

31. I believe that financial experts can protection our investments from negative shocks

32. I seeks the support of financial experts to overcoming asymmetric information supplied from multiple sources

33. Stock brokers can help to rescue transaction time

34. Qualitative asset transformation can be ensured through financial intermediaries.

35. The regulatory dialectic can be surpassed with the help of investment experts.

36. Financial intermediaries helps to allocate my risk optimally

37. I can have orientation to various financial instruments with the help of experts

38. The experts will protect my investment by monitoring my investment position frequently

39. The experts can arrange financial support from external sources

40. The advice of expert should align with my financial goal

41. I need customized service from my financial planner

42. I consider qualification and credentials of financial planner before selecting him

43. I collect opinion about financial planners from fellow investors

44. I will consider their charge / commission/ brokerage etc. before choosing a financial planner

45. I will consider the investment philosophy and approach to risk management of my financial planner

46. The physical and virtual support offering of the financial intermediary will be assessed

47. I will check the ethical background of the financial planner

48. I will check the tools used by expert for financial planning

49. I consider my financial planner as an option for advisory service only

50. I wish to use financial intermediary only for portfolio re allocation and reviewing risk.

<table><thead><tr><th></th><th>SA</th><th>A</th><th>N</th><th>DA</th><th>SDA</th></tr></thead><tbody><tr><td>C. Hedging Strategies</td><td>(5)</td><td>(4)</td><td>(3)</td><td>(2)</td><td>(1)</td></tr></tbody></table>

51. I prefer futures, hedge funds, naked options

52. I prefer equities only

53. I prefer Real Estate (excluding personal residences)

54. I prefer long-term bonds

55. I like Cash, Money Market, Short-Term Bonds

56. I prefer Deposit Insurance

57. I like Call options

58. I like Covered Put

59. I prefer Commodities and Financial indices

60. I prefer Equity Futures and Options

61. I prefer Index Futures and Options

62. I prefer Hedged Mutual Funds

63. I opt for Cross Hedging

64. I like mix of cash market and bullion

65. I consider crypto currencies

66. I like balanced funds

67. I prefer bullion only

68. I consider cash and commodity market

69. I like commodities only for hedging

70. I like gold ETFs

71. Like mix of cash market and Gold ETFs

72. I prefer long term deposits with banks along with stock investment

73. I wish to hedge with Gold Bonds

74. I prefer to guard my investment with foreign currencies

75. I prefer a mix of cash, commodities, equities and currencies