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Does a Financial Education change Gender Risk Aversion?

Michael Naylor*, Wendy Hsu and Brenda Allen-Browne

School of Economics & Finance

Massey University,

Palmerston North,

Private Bag 11 222

Palmerston North

New Zealand

* Corresponding Author: m.j.naylor@massey.ac.nz

Version 1.1: 25th August 2016

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Does a Financial Education change Gender Risk Aversion?

Abstract We are the first paper to test if the well-established gender difference in optimism and lower risk

aversion applies to financially educated individuals. We find that educated investors and uneducated

investors perceive risk differently, and that female uneducated investors are likely to perceive the

risk of a range of financial assets as less risky than the overall financial advisers while the male

uneducated investors are likely to perceive the risk as higher.

Demographic characteristics such as age, non-financial education level, overall wealth, and

ethnicity were also found to be significant. However neither group consistently used solely objective

measures. We also show that during the GFC both genders and education levels acted in a similar

fashion, although those who described themselves as ‘aggressive investors’ took the crisis as an

opportunity while those who were the most highly educated and individuals who worried about

their financial assets, took action based on concern.

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Introduction Risk, like beauty, could be said to be in the eye of the beholder.

It is well established behavioural finance heuristic that male retail investors are more optimist

about potential investment outcomes than female retail investors, as well as having a lower risk

aversion. Eckel and Grossman (2007) and Croson and Gneezy (2004) show that the views of males

and females differ on a range of topics, and that most of these differences can be attributed to

gender differences in risk-aversion.

However, risk tolerance differs from risk perception. Risk tolerance can be described as an

investor’s willingness to accept a given level of risk for the achievement of a goal or objective. ‘Risk

perception’ is however unique to the individual and is the risk the individual believes is contained

within (and the consequences of) a certain situation or decision – whether real or not (Ricciardi,

2008). This is a function of context – both of setting and time. Weber, Blais and Betz (2002) found

differences in risk taking are highly domain-specific and relate to perception of the risk rather than a

consistent attitude toward risk. Roszkowski and Davey’s (2010) found that while risk tolerance was

generally a stable characteristic of an individual’s personality, is it able to be influenced by situations

or by a change in circumstances. They report individual’s risk tolerance scores were surprisingly

largely unchanged pre- and post-GFC 2008, but that 74% of survey respondent’s perceived the stock

market as having become riskier.

If risk-perception can be changed by increased financial knowledge then there should be a

differentiation between the risk perception of retail investors and financial advisers. Shapira and

Venezia (2001) found that while both professionals and independent investors exhibited the

disposition effect, the professionals’ training and experience reduced this bias.

Understanding if financial advisers and investors think about risk in a similar way is critical to

ensuring communication about the often complex financial choices consumers face. For example;

do both financial advisers and clients discuss ‘risk tolerance’ do they share a common

understanding? A professional may think of risk as standard deviation while a client may think of

‘loss’ and ‘risk’ as being interchangeable terms.

This study seeks to understand if the New Zealand Authorised Financial Advisers (AFA) and retail

investors perceive risk differently and if there is a gender disparity in their risk perception; if any

demographic characteristics are related to the risk perception of both/either group; if both groups

implicitly use objective risk measures; if the behaviour of both/either during and after the GFC is

related to any demographic characteristics; if the groups differ in their views on the influences upon

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investor risk tolerance; and if the groups differ in their views on the influences upon investor’s

acceptance of financial advice.

Literature Review Ricciardi (2004) notes an individual’s risk perception can be changed by changing their knowledge

level, and that experts and novices differ in their assessment of risk across a range of activities. He

also notes that more credibility is given to information obtained from trusted sources. By implication

as a novice moves to become an expert, their perception of risk is likely to change, becoming more

objective than subjective. The acquisition of knowledge and change in risk perception should be

gender neutral as perception becomes more objective.

Research is, however, limited on the comparison of risk perception between experts and novices,

and whilst there are many studies on gender differences in risk perception and adversity, results are

mixed. Both have implications for communication between financial advisers and their clients.

Olsen (1997) surveyed American Chartered Financial Analysts and clients and suggested that

individuals are loss-averse rather than risk-averse. He categorised the first survey results into four

attributes of risk; the potential for capital loss, returns below expectations, controllability of loss,

and level of knowledge. He found that the level of knowledge was not significantly related to any of

the risk attributes.

Diacon (2004) compared licensed financial advisers with individual investors and found that

financial advisers reported lower average risk scores than lay investors. The most important factors

for experts were dislike of volatility and losses and lack of trust in product/provider. Comparatively,

for the layperson uncertainty and loss adversity remain distinct factors, and their third factor is lack

of knowledge. From this analysis Diacon finds significant differences in how financial advisers and

the layperson construct their risk perception and suggests advisers may be prone to trusting product

providers, consider products as less complex, and are have more trust in regulators.

Jansen, Fischer and Hackethal (2008) found that the majority of advisers surveyed underestimated

their clients’ risk aversion when compared to the client’s self-reported risk aversion rating. Clark-

Murphy and Soutar (2008) found the finance professionals underestimated those clients self-

categorised as risk averse and that there was also a clear mismatch in the importance ranking of

most attributes of possible investments. Roszkowski and Grable (2005) found no significant

difference in the standardised risk tolerance between expert and lay groups. However male clients’

score being marginally overestimated by advisers while female clients’ scores were significantly

underestimated.

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Most gender risk-aversion research shows that women are consistently more risk averse than men

(e.g.; Barber and Odean, (2001), Croson & Gneezy, (2009), Charness and Gneezy (2012)). Nelson

(2013) argues, however, these studies often do not allow for within-group variation and mistakenly

treated gender differences in risk aversion as an individual categorical variable. She argues that the

data is reconsidered on this basis many of the previous significant results were found to be the result

of within-group variations rather than there being a significant difference in average results.

The evidence therefore remains mixed and many studies use data collected from students rather

than actual retail investors. Others like Haigh and List (2005) used brokers, clerks and exchange

employees rather than financial advisers. The relevance of these studies is therefore limited.

Olsen and Cox (2001) surveyed Chartered Financial Analysts and 274 Chartered Financial Planners

and found that while both men and women ranked the most important risk attribute as the risk of

large losses, women’s score showed a higher level of concern. Compared to Men, Women ranked

uncertainty second and were less concerned with the risk of earning less than expected. Woman

also viewed the lowest risk and highest risk assets (insured savings account and long term Treasury

bonds, and new firm IPO’s) as riskier than men, with a significant difference in perception. Olsen

and Cox suggest this is consistent with women’s concern over downside risk as the low-risk assets

have limited upside opportunity, and women may consider IPO’s have both higher potential for loss

combined with greater uncertainty. Women also allocated a higher portion of funds to growth assets

than men. Olsen and Cox note while this appears at odds with their earlier findings they suggest the

result is related to the higher risk women ascribe to low risk assets. They conclude their study by

suggesting gender differences may only become important when considering portfolios at each end

of the risk spectrum.

Bliss and Potter (2002) surveyed professional fund managers and found female managers held a

slightly riskier portfolio of assets, with the result consistent across three separate objective risk

measures so were less risk averse than their male colleagues. There was no gender difference in the

trading turnover of domestic equity funds, but a large difference in international funds where

women trade less frequently. It is noted however that the average international fund size for women

is one third of their male counterparts which may influence the fund turnover. While female

managers showed outperformance over male managers, this disappears on a risk-adjusted basis.

Atkinson, Boyce-Baird and Frye’s (2003) found no gender difference for fixed-income fund

managers. Niessen and Ruenzi (2006) studied equity fund managers and found that while female

managers women take less unsystematic and small firm risk, their overall risk is not significantly

different to men. They also found women pursue significantly less extreme investment styles and

their style is more consistent over time.

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Becknamm and Menkhoff (2008) argue that fund managers’ familiarity with risk mitigates any

initial gender-specific bias. They surveyed fund managers across four countries and found mixed

results. Italian and Thai female managers were significantly more risk averse in two of the three

behaviours, while German women managers only showed a significantly lower score in the

disposition effect. US managers exhibited no gender differences.

Overall two key points arise. First, there is limited research on the potential risk perception

differences between advisers and the layperson. Second, while there have been numerous studies

exploring potential gender differences, most relate to risk tolerance and behaviour rather than risk

perception. The purpose of this study is to investigate; if differences in risk perception exist

between professionals and lay-person investors, and if gender differences are apparent within

and/or between the two groups under study.

Methodology and Hypotheses Data in this study was collected by way of an online survey distributed to Financial Advisers (AFA)

and AFA clients. The survey link was widely distributed to advisers nationwide and via industry

groups. There were a total of 514 responses over July to August 2014. After eliminating incomplete

responses from both groups (n=104), the sample size was reduced to 244 AFA responses (12.8% of

total AFA population) and 166 non-AFA responses. This compares favourably with Diacon’s study

which comprised 41 experts and 123 lay investors.

The survey questions provided to the two groups had small differences: the non-AFA survey

included a set of financial literacy questions, while AFA’s were asked about professional

qualifications. The survey consisted of four sections:

Section 1: Respondents’ demographic characteristics, financial literacy score, and perception of the

risk of 20 different asset classes.

Section 2: Experimental section - using methodology per Veld & Veld-Merkoulova (2008). Each

respondent was asked to select a random symbol and from this was allocated a set of questions.

There were a minimum four and maximum of eight questions depending on answers given. The

alternatives presented all had the same expected returns but varied in their outcomes and risks.

The first set of questions sought to understand the respondents’ implicit preference for risk

measure, while the second set of questions sought to understand which benchmark was preferred

for respondents who choose an asymmetric risk measure.

Section 3: Respondents’ feelings and behaviour in response to the Global Financial Crisis of 2008,

following methodology as suggested by Soderberg & Wester (2012). If risk perception is a function

of context, the severity of the GFC provides a unique opportunity to understand how an

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individual’s risk perception influences their behaviour in such a situation. Survey participants were

asked about their actual behaviour following this event.

Section 4: Respondents’ perception of how influential various characteristics are on, (a) an

investor’s risk tolerance, and (b) the acceptance of advice from a financial adviser.

Hypotheses H1: The average self-assessed risk tolerance rating of an AFA is equal to that of the layperson.

Whilst the first hypothesis relates to the individual’s risk tolerance rather than perception rating,

this will be used as a baseline to understand how the individual views themselves. The self-

assessed risk rating was on a four-point scale from 1 = conservative to 4 = aggressive.

H2: There is no difference between the AFA and layperson’s perception of the risk of 20 different

asset classes.

For each of the financial assets, respondents were asked to rate the ‘riskiness’ of the asset using a

scale ranging from 1 = very low risk to 5 = very high risk, or 0 = I am not familiar with this. One

difference of this study to Diacon’s is here all respondents were asked their perception of all 20

financial assets.

H3: a) There is no difference between the male and female AFA’s perception of the risk of 20

different asset classes

b) There is no difference between the male and female layperson’s perception of the risk of 20

different asset classes.

H4: The demographic characteristics of the AFA and layperson are unrelated to their risk perception

score.

Two separate regressions are run using different dependent variables:

Dependent variable 1: The respondents own self-assessed risk score.

Dependent variable 2: The respondents Z-score, following methodology of Sachse, et al (2012).

Respondents’ Z-scores were calculated in order to identify differences based on individual

perception of risk of the 20 asset classes. The Z-scores were then averaged for an overall score per

respondent. Where a response was noted as “I am not familiar with this”, it was excluded from the

Z-score calculation. A positive Z-score indicates respondent is more risk averse than the group

average, a negative Z-score indicates respondent is less risk averse than the group average.

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Independent variables: Demographic variables of interest are those commonly tested for their

potential relationship to risk perception:

• Gender: male = 1, female = 0

• Age: separated into the following age bands – 20-29, 30-39, 40-49, 50-59 and 60 and over,

respectively Age_1 to Age_5

• Marital status: Married/de facto = 1, single, divorced, widowed = 0

• Dependents: financial dependents including children, partner or parents, yes = 1, no = 0

• Ethnicity: white = 1, all other ethnicities = 0

• Education, highest level completed, from Ed_1 - Secondary School qualification to Ed_5 – Postgraduate, Masters or PhD

• Net household income – annual household income from all sources, separated into the following

bands – less than $50,000, $50,000 – 149,999, and over $150,000, respectively HH_Inc 1-3.

• Household financial assets excluding equity in property – separated into the following bands – less

than $50,000, $50,000 - $149,999, $150,000 - $299,999, $300,000 - $499,999 and over $500,000,

respectively HH_Fin_Assets 1 – 5.

• Home ownership: yes = 1, no = 0

In addition, the following variables will be tested for potential relationship:

• Investment experience: proxy will be the number of different asset classes owned in the past 5

years. A greater exposure and experience of a range of investment assets may influence an

investor’s perception of risk as suggested in Hilgert, Hogarth & Beverly (2003).

• Investment review period: The investment review period favoured by the investor could potentially

indicate their level of concern for their investment performance or simply their investment style.

• Financial literacy score (non-AFA’s only); Using standard questions. Lusardi and Mitchell (2011)

found individuals who are financially literate are more likely to have successful retirement

outcomes, and Deaves, Veit, Bhandari & Cheney (2007) study that found college employees who

had a propensity to plan for their retirement were also more risk tolerant.

H5: There is no difference between the AFA and the layperson in their implicit use of objective risk

measures.

The experimental section of the survey effectively provides the survey respondent three choices for

each question, the objective risk measure for that question, an alternative objective risk measure, or

a subjective risk measure. In selecting one of three random symbols, the survey participant is

allocated a theoretical sum of one of $1,500, $15,000 or $150,000 to invest in various options. All of

the questions are designed that the mean return for every A/B choice is 8% while their standard

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deviation lies between 10% and 26%. These figures are in line with long run real returns and

volatility for the New Zealand stock marketA t-test is used to check for any significant difference in

AFA vs layperson mean results.

For those survey respondents who implicitly select specific objective risk measures, they were then

directed to questions that sought to uncover their implicit benchmark.

H6: There was no difference between the AFA and layperson’s behaviour following the Global

Financial Crisis (GFC) in 2008.

The two sets of survey responses (AFA and layperson) were grouped into two subsets – participants

who made a choice to take action following the GFC, and those who did not. A binary logistic

regression will be used to understand the variables that impacted on the decision to make a choice.

H7: AFA’s and laypeople do not differ in their view of the importance of factors influencing an

investor’s risk tolerance.

For each of the factors that may influence an investor’s risk tolerance, both AFA and laypeople

respondents were asked to rate the importance of the factor using a scale ranging from 1 = no

influence to 5 = large influence. Factors tested are investor’s: age, gender, ethnicity, net wealth,

net income or income needs, proposed investment timeframe, familiarity with a range of different

asset classes, ethical considerations or constraints, prior positive or negative experience with shares

or managed funds, and prior loss of investment capital.

H8: AFA’s and laypeople do not differ in their view of the importance of factors influencing an

investor’s acceptance of financial adviser’s advice.

Both AFA’s and laypeople respondents were asked to rate to what extent various characteristics

influence an investor’s acceptance of a financial adviser’s advice using a similar scale 1 = not at all to

5 = to a large extent. Characteristics tested are: adviser’s age, adviser’s gender, adviser’s ethnicity,

the investor’s perception of the adviser’s personal net wealth, advisers’ years of industry experience,

advisor’s highest qualification and the advisor’s membership of a Professional Association.

Results Descriptive statistics of the survey respondents are in Table 1 in the appendix. The ‘average’ AFA is

white, male, aged over 50 and married. This is somewhat different to the layperson group, and

significantly different to the general NZ population in terms of ethnic diversity and gender. The

income and asset levels of the AFAs surveyed were significantly higher than the layperson group.

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Table 2 shows the self-assessed risk scores and perceived riskiness of assets. The mean AFA score

of 3.37 is significantly higher than the mean layperson score of 2.48 (t=11.42, p<.0001). This

indicates the AFA sample view themselves as more risk tolerant, with 43.4% of AFA respondents

answering they see themselves as ‘Aggressive’ investors compared with 10.3% from the layperson

group. The null hypothesis 1 is therefore rejected. This does not necessarily mean AFAs are likely to

recommend riskier investments to clients, but rather supports earlier research that found experts

are generally more comfortable with risks in their area of expertise than a lay individual.

Table 2 also shows that in 15 of the 20 asset classes the AFAs and laypeople differ significantly in

the perceived riskiness of assets. This provides strong evidence the null hypothesis 2 can also be

rejected.

Table 2. Self-assessed risk score and perceived riskiness of financial assets: AFA vs Layperson.

The direction of the mean difference is important to note. In 12 of the 15 significantly different

results the AFA means are higher, that is AFAs perceive the assets as riskier than the layperson. Only

AFA Layperson

No. of responses Mean Std Dev

No. of respons

es Mean Std Dev t Value Pr > |t| Self-assessed risk score 244 3.37 0.625 166 2.48 0.858 11.42*** <.0001 (range 1= Conservative, 4 = Aggressive)

Perceived riskiness of various Asset classes

(range 1= very low risk, 5 = very high risk)

Savings account 244 1.19 0.477 166 1.10 0.316 2.35* 0.019 Bank Fixed Term Deposit 244 1.30 0.543 166 1.19 0.477 2.24* 0.026 Finance Company Fixed Term Deposit 242 3.83 0.919 156 3.42 1.180 3.75*** 0.000 Bonus Bonds 243 1.53 0.840 162 1.52 0.836 0.10 0.923 Diversified Managed Fund 243 2.87 0.542 153 2.91 0.789 -0.50 0.620 Kiwisaver or Superannuation Fund 242 2.69 0.618 166 2.28 0.912 5.04*** <.0001 Employer-contribution Superannuation scheme 238 2.67 0.638 162 2.30 0.946 4.35*** <.0001 NZ Corporate Bond rated BBB or lower 240 3.68 0.924 121 3.59 1.078 0.84 0.401

NZ Corporate Bond rated BBB+ or higher 241 2.51 0.837 122 2.88 0.941 -3.68*** 0.000 NZ Government Stock 244 1.24 0.540 146 1.95 0.999 -7.93*** <.0001 Shares in a NZ listed company 242 3.60 0.631 156 3.67 0.694 -0.92 0.358 IPO of a Company 241 4.22 0.764 114 4.03 0.814 2.18* 0.030 Private Equity investment 238 4.55 0.672 123 4.10 0.872 5.04*** <.0001 Shares in an overseas listed company 242 3.71 0.711 155 3.90 0.745 -2.56** 0.011 Precious metals (eg Gold) 240 4.00 1.043 154 3.16 1.117 7.45*** <.0001 Foreign currency 241 4.14 0.902 161 3.67 0.954 4.95*** <.0001 Investment-linked insurance policy 235 2.61 0.947 126 2.78 1.138 -1.39 0.166 Art or Antiques 228 3.96 1.066 154 3.60 1.196 3.02*** 0.003 Residential investment property 243 3.25 0.816 164 2.70 0.968 6.00*** <.0001 Non-residential investment property 243 3.42 0.796 156 3.12 0.964 3.29*** 0.001

Note: * statistically significant at the 0.05 level (2-tailed)

** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-

tailed)

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for Corporate Bonds rated BBB+ or higher, Government Stock and shares in an overseas listed

company were the layperson means higher than that of the AFA.

This is the opposite result to that found in Diacon’s (2004) study where the expert group

consistently reported lower scores than lay investors. This is also at odds with the view that experts

are more comfortable with the risks in their particular field of expertise, leading to an expectation is

their mean riskiness scores would be lower. Possible explanations could be that the AFA sample is

more risk averse than the layperson sample or that the lack of knowledge by layperson leads them

to under-estimate risk.

The first explanation is unlikely given the earlier results noted of the significantly higher AFA self-

assessed risk tolerance scores. Evidence the knowledge of the lay sample is lower of some assets in

the layperson group is seen by 27-31% of lay respondents answering “I am not familiar with this”

when asked about their perception of some assets1. The second possibility will be explored in

Hypothesis 4.

Of particular interest is the difference in the perceived risk of residential property investment.

Results show this asset type displays one of the largest differences in perceived risk (t=6.000,

p<.001). Interestingly this may help explain New Zealand investors ‘love-affair’ with property as

financial experts who may view the risk of residential property investment as significantly higher

than the layperson investor.

The implications of a mismatch in understanding the risk level of financial assets are considerable.

If AFAs and lay investors do not have common perception of risk, how can an adviser ensure they

have matched the investor to a financial product that best suits their needs and risk profile? If the

layperson does not consider the assets to be particularly risky, will they be prepared to pay for

advice about such products?

Risk Perception by Gender

Table 3a shows the self-assessed risk scores of both the AFA and layperson samples, separated by

gender. Whilst the mean self-assessed risk score for female AFAs is higher than that of male AFAs,

the difference is only marginally significant. In the layperson sample, the mean female score is

lower than the male score, but it is not significant.

Table 3b in the appendix shows male vs female AFA view of the riskiness of the same 20 financial

assets. There are only two of the 20 assets showing a significant difference (to 5%) in the mean

scores when results are separated by gender. Given the vast majority of results show no significant

1 this was in spite of there being explanatory information provided in the survey

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difference and the two that do are significant to 5% only, it is reasonable to conclude the null

hypothesis H3(a) is supported.

Table 3a. Self-assessed risk score, comparison of Female and Male scores.

Female Male

(range 1= Conservative, 4 = Aggressive)

No. of responses Mean

Std Dev

No. of responses Mean

Std Dev t Value Pr > |t|

AFA 49 3.51 0.681 195 3.33 0.606 1.66 0.101 Layperson 107 2.40 0.845 59 2.63 0.869 -1.61 0.109

Table 3c shows the male vs female layperson results of the mean comparison for the same 20

financial assets. These results are completely different to the male/female AFA outcome. In all bar

two of the asset classes there is a significant difference between the mean responses of males and

females. Further, in 16 of the 18 significantly different results, the differences the female mean score

was less the male score, indicating the female layperson considers the riskiness of the asset to be

lower than that of the male layperson. Given the similarity of the male and female self-assessed risk

scores (Table 3a), this difference is unlikely to be attributed to women being more risk averse.

Table 3c. Perceived riskiness of financial assets: Layperson Female vs Male

Female Layperson Male Layperson

(range 1= very low risk, 5 = very high risk) No. of

responses Mean Std Dev

No. of responses Mean Std Dev t Value

Pr > |t|

Savings account 107 1.07 0.284 59 1.15 0.363 -1.60 0.114 Bank Fixed Term Deposit 107 1.09 0.292 57 1.37 0.672 -2.95** 0.004 Finance Company Fixed Term Deposit 101 3.36 1.171 55 3.53 1.200 -0.86 0.393 Bonus Bonds 105 1.30 0.634 57 1.93 0.998 -4.35*** <.0001 Diversified Managed Fund 99 2.71 0.718 54 3.28 0.787 -4.42*** <.0001 Kiwisaver or Superannuation Fund 107 1.94 0.775 59 2.88 0.832 -7.12*** <.0001

Employer-contribution Superannuation scheme 106 1.96 0.839 56 2.95 0.796 -7.35*** <.0001 NZ Corporate Bond rated BBB or lower 77 3.31 1.091 44 4.07 0.873 -4.18*** <.0001 NZ Corporate Bond rated BBB+ or higher 77 2.60 0.799 45 3.36 0.981 -4.40*** <.0001 NZ Government Stock 95 1.74 0.853 51 2.35 1.128 -3.41*** 0.001 Shares in a NZ listed company 100 3.43 0.624 56 4.09 0.611 -6.41*** <.0001 IPO of a Company 67 3.85 0.821 47 4.28 0.743 -2.88** 0.005 Private Equity investment 76 3.92 0.891 47 4.38 0.768 -3.05** 0.003 Shares in an overseas listed company 101 3.66 0.725 54 4.35 0.555 -6.59*** <.0001 Precious metals (eg Gold) 102 2.82 1.038 52 3.83 0.965 -5.95*** <.0001 Foreign currency 105 3.36 0.900 56 4.25 0.769 -6.57*** <.0001 Investment-linked insurance policy 80 2.45 1.066 46 3.35 1.038 -4.63*** <.0001 Art or Antiques 101 3.26 1.155 53 4.26 0.984 -5.68*** <.0001 Residential investment property 107 2.37 0.819 57 3.30 0.944 -6.25*** <.0001 Non-residential investment property 103 2.84 0.864 53 3.66 0.919 -5.42*** <.0001

Note: * statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed)

*** statistically significant at the 0.001 level (2-tailed)

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Table 3d shows differences between male laypersons and all AFAs. This shows that there are

significant differences in the mean results for 10 of the 20 financial assets, in that male laypeople

perceive these financial assets as significantly riskier than the AFA sample. Our conclusion is that the

female layperson scores have considerably reduced the overall layperson mean results to the extent

that the distinct male responses are no longer discernible.

Table 3d. Perceived riskiness of financial assets: AFA vs Male Layperson

Note: * statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

Table 3e shows that the results of a regression on the layperson dataset using the financial literacy

score as the dependent variable and Gender as the independent variable. This was statistically

insignificant. Regression was also run adding the alternative proxies for financial knowledge; which

show that Education and Investment are significant. Gender also becomes marginally statistically

significant at 10% level. Since the male gender beta is negative females have more knowledge than

males. This result does not support the notion that the female layperson has lower financial

knowledge so may perceive financial assets to be less risky than the male layperson. Past research

has generally found women are more risk averse than men and this extends to both finance

professionals and the layperson. This study found this to be not the case for the New Zealand AFA.

Male and female risk tolerance scores were only marginally significantly different. Our expectation

AFA Male Layperson

(range 1= very low risk, 5 = very high risk)

No. of responses Mean

Std Dev

No. of responses Mean

Std Dev t Value

Pr > |t|

Savings account 244 1.19 0.477 59 1.15 0.363 0.64 0.524 Bank Fixed Term Deposit 244 1.30 0.543 57 1.37 0.672 -0.68 0.497 Finance Company Fixed Term Deposit 242 3.83 0.919 55 3.53 1.200 1.79 0.079 Bonus Bonds 243 1.53 0.840 57 1.93 0.998 -3.14** 0.002 Diversified Managed Fund 243 2.87 0.542 54 3.28 0.787 -3.60*** 0.000 Kiwisaver or Superannuation Fund 242 2.69 0.618 59 2.88 0.832 -1.69 0.095

Employer-contribution Superannuation scheme 238 2.67 0.638 56 2.95 0.796 -2.40* 0.019 NZ Corporate Bond rated BBB or lower 240 3.68 0.924 44 4.07 0.873 -2.56** 0.011 NZ Corporate Bond rated BBB+ or higher 241 2.51 0.837 45 3.36 0.981 -6.07*** <.0001 NZ Government Stock 244 1.24 0.540 51 2.35 1.128 -6.87*** <.0001 Shares in a NZ listed company 242 3.60 0.631 56 4.09 0.611 -5.22*** <.0001 IPO of a Company 241 4.22 0.764 47 4.28 0.743 -0.43 0.665 Private Equity investment 238 4.55 0.672 47 4.38 0.768 1.52 0.129 Shares in an overseas listed company 242 3.71 0.711 54 4.35 0.555 -7.26*** <.0001 Precious metals (eg Gold) 240 4.00 1.043 52 3.83 0.965 1.10 0.273 Foreign currency 241 4.14 0.902 56 4.25 0.769 -0.84 0.404 Investment-linked insurance policy 235 2.61 0.947 46 3.35 1.038 -4.74*** <.0001 Art or Antiques 228 3.96 1.066 53 4.26 0.984 -1.87 0.063 Residential investment property 243 3.25 0.816 57 3.30 0.944 -0.41 0.679 Non-residential investment property 243 3.42 0.796 53 3.66 0.919 -1.94** 0.054

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that risk perception, particularly among financial experts is gender neutral and results are in line

with this.

Table 3e. Multiple regression of financial knowledge proxies on Financial literacy score (n = 166)

*** statistically significant at the 0.001 level (2-tailed)

A different picture emerged when comparing male and female layperson scores. The result found

the somewhat surprising result that women had significantly lower scores than men indicating the

female layperson from this sample considered most financial assets to be less risky than their male

peers. This is an unexpected result considering past research has found women to be either more

risk averse. As significant differences in AFA and male layperson perception scores were found this

may indicate further research is required to understand if the disparity in layperson risk perception

scores is due a gender mismatch in financial knowledge or other underlying factors.

Multivariate Regression Analysis: Self-Assessed risk score

Table 4a presents a multiple regression of the self-assessed risk score regressed on three models of

explanatory variables described earlier. Model 1 includes all 18 independent variables and produced

a statistically significant R2 of 0.315. Of the five variables that were significant only the Layperson

variable is negative. This is in line with the earlier finding that the layperson mean risk score is

significantly lower than the AFA’s.

The other variables; Age, Education, Investment count and Benchmark, all have a positive sign

which indicates at their value increases, so will the expected risk score. The Education variable may

also be a proxy for AFA status as AFA’s in this sample have a higher education level that the

layperson sample. The Investment count variable is used as a proxy for investment experience so is

expected to be positively related to risk score. The positive relationship with the Benchmark variable

indicates that those measuring their investment performance against either a risk-free or market

Dependent Variable: Fin_Lit_Score

Beta SE Pr > |t|

Intercept 2.11*** 0.142 <.0001

Gender -0.15 0.093 0.101

Inv_cnt 0.03 0.021 0.099

Edu 0.13*** 0.029 <.0001

R-Square 0.122

Adj R-Sq 0.106

13

rate are expected to have a higher risk score than those who use the original investment value as

their benchmark.

The positive relationship between the Age variable and SA risk score indicates as age increases, so

does the individual’s risk score. This would appear at odds with both past literature (e.g.; Sachse et

al. 2012) and the commonly held belief that individuals become more conservative as they age.

However, the dataset includes both AFA and laypeople and further analysis of the data reveals 35%

of the AFAs aged 50+ noted their risk score as 3 or 4 (Balanced or Aggressive). The basis of this

result may therefore have been influenced by the subset of AFA data. A further regression run

using just the layperson dataset (Table 4b. Appendix B) shows that whilst layperson Age also has a

positive relationship to SA risk score, the variable is not significant (p=0.169). The only significant

independent variables in the amended model are Education and Benchmark.

Table 4a. Multiple regression of demographic characteristics on SA Risk (n = 410)

Note: * statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

Dependent Variable: SA_Risk

MODEL1 MODEL2 MODEL3

Beta SE Pr > |t| Beta SE Pr > |t| Beta SE Pr > |t|

Intercept 2.29*** 0.485 <.0001 2.32*** 0.431 <.0001 2.38*** 0.218 <.0001

Layprsn -0.70* 0.288 0.015 -0.78*** 0.157 <.0001 -0.69*** 0.083 <.0001

Gender -0.06 0.089 0.487 -0.07 0.089 0.451

Age 0.06* 0.032 0.048 0.06* 0.032 0.042 0.06* 0.031 0.051

Married -0.09 0.089 0.331

Dep 0.01 0.079 0.889

Ethnicity 0.05 0.110 0.659 0.05 0.110 0.646 Edu 0.11** 0.033 0.002 0.10** 0.033 0.002 0.11*** 0.030 0.000

CFP 0.03 0.095 0.779

Inv_cnt 0.04* 0.017 0.019 0.04** 0.016 0.005 0.05** 0.016 0.003

B_mark 0.10* 0.045 0.020 0.11** 0.045 0.015 0.12** 0.044 0.008

Review 0.03 0.045 0.519 0.04 0.044 0.410

Z_Score 0.08 0.092 0.367 0.10 0.090 0.261

HH_Fn_As 0.03 0.029 0.314

Home_own -0.17 0.112 0.140 -0.18 0.104 0.086 -0.16 0.101 0.113

HH_Income 0.09 0.072 0.190 0.12 0.068 0.077

Yrs_workd -0.12 0.115 0.314

Yrs_advice 0.16 0.086 0.067

Fin_lit_score -0.06 0.100 0.539 -0.07 0.099 0.503

R-Square 0.345

0.335

0.325

Adj R-Sq 0.315 0.315 0.315

14

In Model 2 the number of independent variables was reduced to nine following a backward

selection process, eliminating highly correlated variables, eg HH Financial Assets and HH income

(correlation coefficient 0.393, p<.001). Variables that related to AFAs only (CFP, Years worked and

Years advice) were also removed as the first model showed these had no significance. Model 2 also

produced a statistically significant R2 of 0.315 showing removal of some of the insignificant and/or

correlated variables did not change the model strength. The five significant variables from Model 1

remained so, but the Layperson variable became significant to the 0.1% level, while the Investment

count and Benchmark variables became significant to the 1% level.

In Model 3 the number of independent variables was reduced further, eliminating those that

weren’t significant except Home Owner as this had p=0.077 in Model 2 so may have become

significant once other variables were removed. The original five variables remained significant to

the same level and the R2 remained 0.315 with Home Owner variable remaining insignificant. The

null hypothesis that demographic variables are unrelated to an individual’s self-assessed risk score is

therefore rejected.

Multivariate Regression Analysis: Z-score

Table 4c presents a multiple regression with the individual’s Z-score regressed on three further

models of explanatory variables. Using the Z-score enables the combined dataset of AFA and

layperson responses to be used as the standardised Z-score provides comparison of the individual to

their wider peer group. Model 4 again uses all 18 independent variables and produced a statistically

significant R2 of 0.195, but this is considerably lower than that of the earlier regression. In this model

the only statistically significant variables are Gender, Ethnicity and Benchmark. The earlier significant

variables of Age and Investment count no longer feature and the Education variable is marginally

significant (p=0.056).

The Gender and Benchmark betas are positively signed indicating as these increase so does the Z

score. The positive Gender beta indicates as the individual variable changes from female to male

their Z-score increases. A higher Z-score indicates the survey respondent viewed the range of 20

financial assets as more risky than their average peer group. Whilst this is not in line with the results

of Table 3a it does perhaps help to explain the results from Table 3d. From Model 4 we are unable

to see if this applies to all males or just to the male lay investor as identified in Table 3d.

15

Table 4c. Multiple regression of demographic characteristics on Z_Score (n= 410)

The Ethnicity variable is negatively signed indicating as the individual variable changes from non-

white to white their Z-score decreases. This implies those of non-white ethnicity consider the 20

financial assets less risky than their white peers. This is at odds with the so-called ‘white male effect’

(Finucane, Slovic, Mertz, & Satterfield, 2000) which found white males perceive risks to be lower

than women and non-white males.

In Model 5 the number of independent variables was reduced to 10 following a backward selection

process, eliminating highly correlated variables together with those that were insignificant in Model

4. Model 5 increased the statistically significant adjusted R2 to 0.200 showing removal of some of

the insignificant and/or correlated variables improved the model strength. The change in model

increased the number of significantly related independent variables from three to six, with

Layperson, Education and CFP becoming significant.

Dependent Variable: Z_Score

MODEL4 MODEL5 MODEL6

Beta SE Pr > |t| Beta SE Pr > |t| Beta SE Pr > |t|

Intercept -0.16 0.275 0.565 -0.26* 0.116 0.028 -0.30** 0.106 0.006

Layprsn 0.14 0.160 0.371 0.19*** 0.053 0.001 0.20*** 0.053 0.000

Gender 0.36*** 0.046 <.0001 0.36*** 0.045 <.0001 0.37*** 0.045 <.0001

Age -0.01 0.018 0.725

Married -0.09 0.049 0.075 -0.08 0.046 0.098

Dep 0.04 0.043 0.351

Ethnicity -0.15** 0.060 0.011 -0.17** 0.059 0.005 -0.17** 0.058 0.005

Edu 0.04 0.018 0.056 0.04* 0.017 0.038 0.03* 0.017 0.048

CFP 0.08 0.052 0.121 0.11* 0.050 0.034 0.10* 0.050 0.040

SA_Risk 0.03 0.028 0.367

Inv_cnt -0.01 0.009 0.294 -0.01 0.009 0.288

B_mark 0.05* 0.025 0.050 0.05* 0.024 0.044 0.05* 0.024 0.052

Review 0.01 0.025 0.809

HH_Fn_As -0.03 0.016 0.112 -0.02 0.015 0.120 -0.03* 0.013 0.040

Home_own 0.07 0.062 0.265 0.06 0.061 0.289

HH_Income 0.00 0.040 0.981

Yrs_workd -0.06 0.063 0.346

Yrs_advice 0.07 0.047 0.115

Fin_lit_score -0.05 0.055 0.384

R-Square 0.230

0.220

0.212

Adj R-Sq 0.195 0.200 0.198

Note:

* statistically significant at the 0.05 level (2-tailed)

** statistically significant at the 0.01 level (2-tailed)

*** statistically significant at the 0.001 level (2-tailed)

16

The significance in the Layperson variable in combination with Gender variable (both positively

signed) indicates a male layperson considers the 20 financial assets as being riskier than do all other

sub-groups of the sample. This clearly supports the results summarised in Table 3d.

The newly significant CFP variable, positively-signed, indicates those among the AFA group who are

among the industry’s most qualified, consider the financial assets listed to be riskier than their non-

CFP AFA peers. Whilst the beta is small at 0.11 it is significant none-the-less and raises the question

as to why this experienced group of financial advisers hold this view. Further data analysis reveals

CFPs comprise nearly half (48.8%) of the AFA respondents and are spread across age and income

bands. However, nearly 60% of CFPs have household financial assets of over $500,000 so their

implied increased risk adversity may be driven by a desire to take less risk with their accumulated

wealth.

In Model 6 the number of independent variables was reduced further, eliminating those that

weren’t significant from Model 5 except HH Financial Assets. The six variables from Model 5

remained significant to the same level and HH Financial Assets was found to have a positive

statistically significant relationship to Z-score. The model’s adjusted R2 reduced only slightly to

0.198. The null hypothesis that demographic characteristics are unrelated to an individual’s Z-score

is therefore rejected.

This section sought to understand if an individual’s characteristics, beyond gender, were related to

their risk tolerance. This may assist advisers’ understand of which client’s characteristics link to risk

sensitivity.

Experimental section analysis

The experimental part of the survey ascertained respondents actual risk behaviour from

randomised investment trial questions. The summarised results of the first part of the experimental

questions are presented in Table 5a. The number of responses is important as it shows what

proportion of respondents made a definite choice based on the limited information provided. The

four possible answers provided are: ‘1.Fund A’, ‘2.Fund B’, ‘3.Both funds are equally

attractive/unattractive to me’ or ‘4.I do not have enough information to make a choice’.

Four objective risk measures are implied in the choices provided in questions 1 – 4. These are

probability of loss, semi-variance, expected value of loss and total variance respectively. For each of

these initial questions, one objective risk measure has been calculated as offering a lower risk than

the other three risk measures, whilst holding return to 8% and standard deviation to a range

17

between 15 and 26. The questions are based on the assumption the rational investor will seek to

minimise their risk. In Question 1 for example, Fund A has a probability of loss of 10% whilst Fund B

has a probability of loss of 40%. A rational investor solely using objective risk measures is expected

to therefore choose Fund A.

Table 5a. Summary of responses to experimental questions, AFA and Layperson

AFA Layperson

Fund A Fund B

Both funds equal

Insuf. Info

Total resp. Fund A Fund B

Both funds equal

Insuf. Info

Total resp.

Q1. 1 = probability of loss of most concern, 2 = other risk measures

35 (14.3%)

96 (39.3%)

32 (13.1%)

81 (33.2%)

244

35

(21.1%) 63

(38.0%) 25

(15.1%) 43

(25.9%) 166

Q2. 1= semi-variance is of most concern, 2= other risk measures

55 (26.3%)

56 (26.8%)

25 (12.0%)

73 (34.9%)

209

37 (31.6%)

37 (31.6%)

17 (14.5%)

26 (22.2%)

117

Q3. 1= expected value of loss of most concern, 2= other risk measures

20 (12.9%)

34 (21.9%)

30 (19.4%)

71 (45.8%) 155

11 (13.9%)

24 (30.4%)

7 (8.9%)

37 (46.8%) 79

Q4. 1= total variance of most concern, 2= other risk measures

11 (8.1%)

39 (28.9%)

21 (15.6%)

64 (47.4%)

135 7 (8.4%)

29 (34.9%)

9 (10.8%

38 (45.8%)

83

Q5. Benchmark: 1= market, 2= risk free rate or zero

33 (58.9%)

19 (33.9%)

3 (5.4%)

1 (1.8%)

56

28 (60.9%)

16 (34.8%)

1 (2.2%)

1 (2.2%)

46

Q6. Benchmark: 1= market or risk free, 2= zero

30 (54.5%)

21 (38.2%)

4 (7.3%)

0 (0%)

55

16 (34.8%)

25 (54.3%) 5 (10.9%) 0 (0%)

46

Q7. Benchmark: 1= semi-variance of returns relative to the market, 2= risk free rate or zero

30 (54.5%)

20 (36.4%)

4 (7.3%)

1 (1.8%)

55

23 (60.5%)

13 (34.2%)

1 (2.6%)

1 (2.6%)

38

Q8. Benchmark: 1= semi-variance of returns relative to the market or risk free rate, 2= zero

23 (41.8%)

27 (49.1%)

5 (9.1%)

0 (0%)

55

16 (44.4%)

19 (52.8%)

0 (0%)

1 (2.8%)

36

Table 5a shows for the first two questions approximately 53% of the AFA survey respondents are

willing to make a definite investment choice between Fund A and Fund B, with the balance replying

that either ‘both funds are equally attractive/unattractive’ or that ‘they did not have enough

information to make a choice’. For Questions 3 and 4, those AFA willing to make a definite choice

drops to 34.8% and 37% respectively. The layperson results are somewhat similar to the AFA results

for the first two questions, with 59.1% (Q.1) and 63.2% (Q.2) of respondents making a choice

between Fund A and Fund B. For Questions 3 and 4, the proportion of lay investors making a

definite choice drops to 44.3% and 43.3% respectively but these are considerably higher than the

proportion of AFA responses. This would imply that the layperson is more comfortable making a

definite investment choice based on implicitly objective risk measures than the AFA in this survey.

This is an interesting result when the AFA is expected to be more objective in their consideration of

risk compared to the layperson.

Both the AFA and layperson responses however are significantly different to the response rates

seen in the Veld and Veld-Merkoulova (2008), who found that between 66.5% - 80.4% of

respondents made a definite choice of either Fund A or Fund B for Questions 1-4. Further, less than

18

5% of respondents answered Questions 1-4 with option 4. This compares to between 22.2% - 47.4%

of respondents in this survey. This level of non-choice indicates a significant proportion of this

sample do not base their investment choices on purely objective risk measures.

For those respondents who chose an objective risk measure, follow up questions to discover their

implicit preferred benchmark were then provided. For those who chose Fund A in either Question 1

or 3, they were given Questions 5 and 6. These questions sought to understand if the benchmark

preferred is the market return, the risk-free rate or zero Table 5a shows that more than 50% of the

AFA respondents implicitly prefer the market return as a benchmark (Q5.) instead of the original

value of the investment. While the layperson sample indicates a preference for the market return

benchmark (Q.5 60.9%), in Q.6 respondents show 54.3% prefer the original value of the investment

as a benchmark. Their preferred implicit benchmark is therefore inconclusive.

For those respondents who chose semi-variance (Fund A, Q.2) or total variance (Fund A, Q.4) as

their objective risk measure, follow up questions 7 and 8 were provided. The results in Table 5a

show again more than 50% of AFA respondents implicitly preferred the market return as their

benchmark (Q.7). For Q.8 however the choice between the implicit benchmarks slightly favoured

the original investment value. In the layperson sample, the choice in Q.7 was clear with 60.5% of

respondents implicitly preferring the market benchmark. The responses from Q.8 however do not

reflect the same result with 52.8% of respondents implicitly preferring the original investment value

as their benchmark. These results are similar to those of Veld and Veld-Merkoulova (2007).

For the proportion of survey respondents who made definite choice between Fund A and Fund B,

further analysis has been undertaken to see if the respondents favour particular objective risk

measures. In this analysis if Fund A = 1 and Fund B = 2, then the expected mean of the random

choice between the two is expected to be 1.5. Table 5b summarises these results.

For Question 1, the mean response is statistically significantly different to 1.5 indicating the

respondents consider other risk measures to be more important than the probability of loss. This

result applies to both the AFA and layperson respondents. The same result is seen for both groups

for Question 4 indicating total variance is less important than other risk measures. Question 2 mean

is not significantly different to 1.5 for either group and the mean in Question 3 is only significantly

different to 1.5 for the layperson group. Results for Questions 5 – 8 show only one significant result

across both the AFA and layperson groups so supports the hypothesis that the observed mean is not

significantly different to 1.5 for these four questions regarding implicit benchmark.

19

The results regarding the implicit benchmark used by the AFA or layperson can be compared to the

responses from the survey which directly asked which benchmark the individual used to assess the

performance of their investments. The responses are shown in Table 5c.

Table 5b. Summary of results for the experimental questions to AFA and Laypeople

Table 5c. Responses to survey question: When you assess the performance of your investment, what do you use as your benchmark?

Interestingly a similar proportion of AFAs chose zero or the original value of their investment as

their explicit preferred benchmark when compared with the implicit measure. The implicit AFA

choice of the market benchmark (Q.5) was however considerably higher than their explicit choice.

For the layperson, the explicit preferred benchmark is clearly the original value of the investment

but Q.5 shows 60.9% implicitly chose the market return as a benchmark. Q.6 however shows 54.3%

of those lay investors who made a definite investment choice implicitly chose the original

investment value as their benchmark which is very similar to the result in Table 5c.

AFA Layperson

Number of resp. Mean

Std Dev Pr > |t|

Number of resp. Mean

Std Dev Pr > |t|

Q1. 1 = probability of loss of most concern, 2 = other risk measures 131

1.73***

0.444

<.0001

98

1.64**

0.482

0.004

Q2. 1= semi-variance is of most concern, 2= other risk measures

111

1.50

0.502

0.925

74

1.50

0.503

1.000

Q3. 1= expected value of loss of most concern, 2= other risk measures 54

1.63

0.487

0.056

35

1.69*

0.471

0.026

Q4. 1= total variance of most concern, 2= other risk measures 50

1.78***

0.418

<.0001

36

1.81***

0.401

<.0001

Q5. Benchmark: 1= market, 2= risk free rate or zero 52 1.37* 0.486 0.051 44 1.36 0.487 0.070

Q6. Benchmark: 1= market or risk free, 2= zero 51 1.41 0.497 0.211 41 1.61 0.494 0.163

Q7. Benchmark: 1= semi-variance of returns relative to the market, 2= risk free rate or zero 50

1.40

0.495

0.159

36

1.36

0.487

0.096

Q8. Benchmark: 1= semi-variance of returns relative to the market or risk free rate, 2= zero

50

1.54

0.503

0.577

35

1.54

0.505

0.619

Note: * statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

AFA Layperson

No. of responses

Percent of responses

No. of responses

Percent of responses

1 = Zero 84 34.4% 88 53.0%

2 = RF rate 65 26.6% 57 34.3%

3 = Market 95 38.9% 21 12.7%

20

The overall results from the experimental section of the survey do not appear to support the

hypothesis that both AFAs and the layperson implicitly use objective risk measures and the null

hypothesis is therefore rejected.

The results show that both layperson and AFA do not implicitly use objective risk measures when

making an investment choice within the constrained choices provided. The level of non-choice by

both the AFA and layperson was an interesting incongruity to the research by Veld and Veld-

Merkoulova and may suggest respondents in fact use subjective risk factors in their decision-making.

It may also have been due to what Schwartz (2004) describes as decision avoidance, who posits

individuals may avoid making a decision when presented with two attractive alternatives particularly

in a world of information overload. It may be that in this experimental section, survey respondents

simply did not have the cognitive energy to make a considered choice as the funds were

hypothetical and there were no consequences for a non-choice. This finding may be particularly

relevant to those financial institutions that use a hypothetical scenario test to assist with assessing

client risk tolerance.

The results regarding the implicit benchmark used were inconclusive with inconsistencies in the

survey responses for both the AFA and layperson. The explicit survey question on performance

measure used by the individual revealed that a significantly higher proportion of lay investors used

zero, or the original investment value as their benchmark. However, just over one third of AFAs also

used a zero return as their performance measure. This may identify individuals that are either loss

averse or that attach more significance to losses than gains. Loss aversion is a central assumption of

prospect theory which proposes value is defined in terms of losses and gains from a particular

reference point – in this case the original investment value (Ricciardi, 2004).

Given traditional financial theory is commonly based on total variance, which treats gains and

losses equally, the expression of this benchmark preference is in line with the finding that

respondents preferred to not use total variance as a risk measure (significant to a 0.0001 level). It is

interesting that this finding was consistent in both the AFA and layperson groups when an AFA,

educated in traditional financial theory might be expected to use an objective risk measure. This has

implications for client adviser communication if the adviser does not fundamentally believe the

‘message’ of the advice being given. This could be particularly highlighted when the client asks “if

you were me, what would you do?”

21

Logistic regression analysis: Response to the GFC

Inherent within the decision of whether to take action as a response to the global financial crisis is

the individual’s feeling of how much control they have over their personal situation, as occurred

during the 2007-09 Global Financial Crisis (GFC). The global financial crisis provides researchers an

opportunity to study individual’s actual rather than theoretical financial behaviour during a

significant worldwide event affecting a wide range of financial assets. Of the AFA sample 55% of

respondents took action compared with 31% of the layperson sample. The question was initially

about choosing to take action or not, the distinction of what type of action was taken was addressed

in a separate question. In order to understand if any demographic characteristics influenced either

the AFA or layperson in their decision to take action or not, a logistic regression was performed.

The initial model includes the same 20 independent variables used in the earlier multivariate

regression plus the additional variables Affected, Worry and Protect. These three variables are

defined as follows:

Affected – a binary variable indicating whether the individual felt their household’s financial

assets had been affected by the GFC or not. Yes = 1, No = 0.

Worry – a variable indicating the extent to which the individual worried about their financial

assets during the following the GFC. Scale 0 = not to a great extent, 100 = to a great extent.

Protect – a variable indicating the extent to which the individual considered other people

undertook actions to protect their financial assets during and following the GFC. Scale 0 = not to

a great extent, 100 = to a great extent.

Results are summarised in Table 6a. The 20 original independent variables have been converted

where necessary to binary variables by splitting the variables into levels. Model 1 was statistically

significant, which indicates the model was able to distinguish between those respondents who chose

to take an action and those who chose not to act. As shown in the table, only six of the

independent variables were statistically significant in the full regression. These were Affected,

Worry, Married, Education level4, Education level5 and Investment count.

The strongest predictor of taking some action in response to the GFC is Education level5 (those

individuals who are the most highly educated with a Post Graduate, Masters or PhD qualification)

with an odds ratio (OR) of 4.195. These individuals are more than four times more likely to have

chosen to take an action than individuals who have a secondary school education qualification only.

The OR for individuals who felt their household financial assets were affected by the GFC was 2.348

indicating these individuals were over twice as likely to take action compared to those who felt their

assets were unaffected.

22

Table 6a. Logistic regression of demographic characteristics on the choice to take action as a response to the GFC

Dependent Variable: Action

MODEL1 MODEL2

Beta SE

Pr > ChiSq

Odds ratio Beta SE

Pr > ChiSq

Odds ratio

Intercept -2.96* 1.501 0.048 -2.74** 0.801 0.001 Affected 0.85** 0.278 0.002 2.348 0.90** 0.272 0.001 2.459 Protect 0.00 0.005 0.714 0.998

Worry 0.03*** 0.005 <.0001 1.032 0.03*** 0.005 <.0001 1.032 Layprsn -0.23 1.881 0.901 0.791

Gender -0.43 0.312 0.165 0.648 -0.48 0.283 0.090 0.619 Age_2 0.72 0.584 0.215 2.064 0.58 0.550 0.292 1.785 Age_3 0.39 0.587 0.508 1.474 0.28 0.552 0.613 1.322 Age_4 0.46 0.611 0.450 1.587 0.31 0.563 0.579 1.367 Age_5 0.11 0.648 0.860 1.121 -0.13 0.586 0.822 0.876 Married -0.70* 0.311 0.024 0.496 -0.67* 0.278 0.016 0.511 Dep 0.25 0.283 0.383 1.280

Ethnicity 0.03 0.384 0.941 1.029

Ed_2 0.48 0.588 0.413 1.618 0.48 0.580 0.409 1.614 Ed_3 0.81 0.516 0.119 2.238 0.81 0.504 0.107 2.249 Ed_4 1.05* 0.514 0.041 2.856 1.11* 0.496 0.026 3.025 Ed_5 1.43** 0.572 0.012 4.195 1.52** 0.553 0.006 4.589 CFP -0.09 0.320 0.781 0.915

SA_Risk 0.08 0.179 0.647 1.085

Inv_cnt 0.13* 0.058 0.019 1.144 0.15** 0.053 0.006 1.156 B_mark 0.11 0.152 0.476 1.115

Rvw_Mod -0.03 0.310 0.925 0.971

Rvw_Inf -0.04 0.310 0.899 0.961

Z_Score -0.19 0.313 0.549 0.829

Assts_2 0.28 0.455 0.542 1.320

Assts_3 0.39 0.493 0.434 1.472

Assts_4 -0.12 0.576 0.834 0.886

Assts_5 0.23 0.486 0.635 1.259

Home_own -0.45 0.403 0.267 0.639

HH_Inc_2 -0.24 0.850 0.780 0.789

HH_Inc_3 0.16 0.866 0.851 1.177

Yrs_workd -0.08 0.379 0.836 0.925

Yrs_advice 0.02 0.294 0.953 1.018

Fin_lit1 -0.61 2.012 0.761 0.542 -0.93 1.170 0.428 0.396 Fin_lit2 -0.91 1.718 0.598 0.404 -1.28** 0.467 0.006 0.278 Fin_lit3 -1.16 1.718 0.498 0.312 -1.38*** 0.367 0.000 0.252 Likelihood ratio 120.86 <.0001 118.03 <.0001 Note:

* statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-

tailed) *** statistically significant at the 0.001 level (2-

tailed) Whilst the Worry variable was highly significant, the predictive power was weak with worried

individuals only 3.2% more likely to take action than those who were less worried about their assets.

In contrast, those with a higher number of investment types were 14.4% more likely to take action,

and Married individuals around 50% less likely to take action than their unmarried peers.

23

These results are similar to Soderberg and Wester’s (2012) who found Affected, Worry, Protect,

Gender, Age, Education, and Ethnicity significant, together with an additional variable not measured

in this study (IMPofFinKn).

Model2 was created to reduce the number of independent variables and followed a backward

selection process, particularly eliminating highly correlated variables, together with those variables

that were insignificant in Model 1. The change in model increased the number of significantly

related independent variables from six to eight and the X2 Likelihood ratio only slightly reduced

The six initial significant variables remained significant, but Investment count and OR increased in

significance. The new significant variables were Fin_Lit2 and Fin_Lit3. These variables identified

those laypeople who had answered two or three of the three financial literacy questions correctly.

Both variables had negative betas, which indicates that lay investors with higher financial literacy

were significantly less likely to take action than those individuals with low financial literacy.

Whilst Models 1 and 2 looked at the influences on the decision to take action, understanding the

type of action taken and the potential influences may identify any differences between the AFA and

layperson samples. Their actions were then categorised into whether they took action to take

advantage of what they saw as an opportunity or whether they acted with concern. The opportunity

actions taken included: adopted a more aggressive investment strategy or purchased additional

risky assets. The concern actions taken included: made changes to investment strategy to reduce

risk; reduced, renegotiated or paid off debt; opened new bank accounts in different banks to spread

risk (as a result of the establishment of the NZ Government Retail Deposit Guarantee); reduced

household spending, and actively sold off many/most non-cash investments in order to hold cash.

Table 6b presents the logistic regression results. Model 3 was statistically insignificant, which

indicates the full model was unable to distinguish between those respondents who chose to take an

opportunity action and those who acted with concern. As shown in the table, only three of the

independent variables were statistically significant in the full regression. These were Layperson, SA

Risk score, and Review-Infrequent. As this model was insignificant, Model 4 was created to reduce

the number of independent variables and used a backward selection process. This eliminated any

highly correlated variables and insignificant variables from Model 3. The change in model increased

the number of significantly related independent variables from three to six and the X2 Likelihood

ratio increased considerably to statistically significant.

Several notable changes emerged; the Worry variable became significant whilst the Layperson beta

reduced considerably and became only marginally significant. The Education level5, CFP and

24

Investment count all became significant following the removal of other variables, while the Review-

Infrequent variable became marginally significant.

Table 6b. Logistic regression of demographic characteristics on the choice to take "Opportunity" action as a response to

the GFC 2008

The strongest predictor of taking an opportunity action in response to the GFC is the Self-assessed

risk score. This indicates those individuals who describe themselves as being more risk tolerant were

Dependent Variable: Action

MODEL3 MODEL4

Beta SE

Pr > ChiSq Odds ratio Beta SE

Pr > ChiSq

Odds ratio

Intercept -1.17 3.463 0.735 -4.82 1.474 0.001 Affected 0.04 0.581 0.944 1.042

Protect 1.47 3.660 0.689 4.337

Worry 0.00 0.009 0.885 1.001 -0.04*** 0.008 <.0001 0.964 Layprsn -0.04*** 0.010 <.0001 0.960 -1.11 0.666 0.097 0.331 Gender -0.48 0.546 0.376 0.617

Age_2 2.66 1.956 0.174 14.282

Age_3 2.17 1.935 0.261 8.802

Age_4 2.71 1.945 0.163 15.039

Age_5 1.24 1.998 0.534 3.462

Married 0.54 0.525 0.302 1.720

Dep -0.05 0.492 0.915 0.949

Ethnicity -1.03 0.885 0.244 0.356

Ed_2 0.11 1.750 0.949 1.120

Ed_3 -0.34 1.630 0.834 0.711

Ed_4 -0.48 1.635 0.771 0.621

Ed_5 -1.62 1.709 0.342 0.197 -1.61* 0.766 0.036 0.200 CFP -0.74 0.507 0.142 0.475 -0.90* 0.447 0.044 0.406 SA_Risk 1.18** 0.385 0.002 3.269 1.56*** 0.392 <.0001 4.756 Inv_cnt 0.12 0.092 0.192 1.128 0.20* 0.083 0.018 1.217 B_mark -0.04 0.262 0.883 0.962

Rvw_Mod -0.89 0.556 0.111 0.413

Rvw_Inf -1.35* 0.657 0.041 0.260 -0.95 0.563 0.093 0.388 Z_Score 0.53 0.516 0.304 1.699

Assts_2 1.58 1.406 0.261 4.856

Assts_3 1.88 1.407 0.181 6.568

Assts_4 3.01 1.671 0.072 20.194

Assts_5 2.53 1.449 0.081 12.574

Home_own -0.57 0.684 0.407 0.567

HH_Inc_2 -2.00 1.977 0.313 0.136

HH_Inc_3 -1.77 1.970 0.370 0.171

Yrs_workd -0.92 0.639 0.150 0.399

Yrs_advice 0.17 0.489 0.731 1.183

Fin_lit1 28.84 1283154 1.000 >999.999

Fin_lit2 -4.86 3.457 0.160 0.008

Fin_lit3 -4.94 3.447 0.152 0.007

Likelihood ratio 26.52 0.848 79.439 <.0001 Note:

* statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

25

nearly five times more likely than those with a low SA risk score to have considered the crisis as an

opportunity. Individuals who held a larger number of asset classes, as measured by the Investment

count variable, were 15.6% more likely to take an action in Model 2. From the OR of 1.217 in Model

4 we can see this predicts a 21.7% greater likelihood to take an opportunity action. The SA risk score

and Investment count variables are slightly correlated.

All of the remaining significant variables had negative beta indicating these were predictors of

being less likely to take an opportunity action. The negative beta variables included: Worry,

Education Level5, CFP, and Investment count. We can now see that where Worry was predictive to

taking an action in Model 1 and 2, Model 4 shows the action taken was one of concern rather than

opportunity because of the negative beta. Whilst this is intuitively correct the OR of 0.964 indicates

those with a higher Worry score are predicted to be only approximately 5% less likely to take an

opportunity action.

The negative beta of Education Level5 and CFP indicates the more educated AFA and layperson are

both significantly less likely to take an opportunity action. In Model 1 and 2 we found the Education

Level5 variable the highest predictor of taking action and Model 4 now shows that this was ‘to take a

concern’, not ‘opportunity’, action. The OR of 0.200 indicates the highly educated are predicted to

be 80% less likely to take an opportunity action than less educated individuals. Whilst the CFP

variable was not significant in Models 1 or 2, in Model 4 the negative beta and OR of 0.406 reveal

highly qualified AFAs react in a similar way to those with high education levels – that is they are less

likely to take an opportunity action (approximately 60% less likely). Given these two variables could

be correlated, a CFP may be likely to have higher qualifications in addition to their CFP, the

correlation between these two variables was checked. The correlation coefficient was -0.136 so we

can be comfortable these two variables are not measuring the same information.

The insignificance of the Layperson variable in Model 1 together with the only marginally

significant result in Model 4 means there is insufficient evidence to reject the null hypothesis that

there was no difference between the AFA and layperson’s behaviour following the GFC. Whilst the

significant CFP variable relates to AFAs only, the significant Education Level5 variable could reflect a

similar predictor from the layperson sample.

Results showed most lay investors took no action during and following the GFC, but around half

the AFA sample took action. The AFA, by virtue of their training and exposure to detailed market

commentary and research, may reasonably be expected to make objective investment decisions

during such a time. As expected the regression results showed the Worry and Affected variables

were positively related to the decision to take action rather than perhaps wait the crisis out.

26

The analysis also revealed while the Layperson variable was not related, the variables identifying

the layperson with higher financial literacy had a significantly negative relationship as did the highest

education variable. Whilst the initial regression sought to understand the influences on the decision

to take action, this section looked at the direction of that action – to either view this as an

opportunity or to act with concern. Unsurprisingly a significant positive relationship was found

between self-assessed risk tolerance score, financial experience (using an investment type count as a

proxy) the decision to take opportunity action. This is intuitively correct as the higher risk tolerance

score relates to those individuals who described themselves as aggressive investors.

The comfort provided by diversification may have prompted those with a larger range of

investments to look for additional opportunities within the crisis. The predictive power of high

education was consistent across both regressions but the second set of results revealed the action

type was one of concern rather than opportunity. This is inconsistent with the theory that becoming

more expert is synonymous with both a reduction in risk perception and a shift to objectivity. The

implication for adviser client relationships is that those with higher education or financial

understanding cannot be assumed to be more risk tolerant than those with lower education levels.

Hypothesis six was confirmed as AFAs acted similarly to the lay group during and after the GFC, but

of note is that neither sample group appeared to act entirely objectively.

Influences on investor’s risk tolerance

Given studies have revealed advisers often underestimate client’s risk tolerance (e.g.; Jansen, et al.,

(2008), Clark-Murphy and Soutar (2008)) this section sought to understand if New Zealand AFAs had

a similar view to the layperson as to the influences upon an investors risk tolerance. Table 7a

summarises these results.

The table notes the t-statistic after testing the null hypothesis that the means of the AFA and

layperson sample are equal. The results show that in six of the ten influences provided, the AFAs

and layperson differ significantly at the 5% level with three results significant to the 0.01% level.

This provides strong evidence the null hypothesis can be rejected.

The direction of the mean difference is important to note. In all of the significantly different

results the AFA means are lower, that is AFAs perceive these variables to have less influence on risk

tolerance than the layperson. The variables the AFA consider to be less influential: the investor’s net

wealth, the investor’s net income or income needs, the investor’s ethical considerations or

constraints, investor’s ethnicity, the investor’s familiarity with a range of different asset classes and

an investor’s prior loss of investment capital. Of these, the first three are significantly different to a

0.1% level.

27

Table 7a. T-test for perception of influences on an Investor's risk tolerance - AFAs vs layperson

AFA Layperson

(range 1= no influence, 5 = large influence) Mean

Std Dev Mean

Std Dev t Value Pr > |t|

Age 4.08 0.922 4.10 1.082 -0.20 0.8423 Gender 2.57 0.964 2.48 1.179 0.81 0.4173 Ethnicity 2.37 1.012 2.60 1.175 -2.05* 0.0411 Net Wealth 3.71 0.925 4.12 0.920 -4.39*** <.0001 Net Income or Income needs 3.91 0.863 4.30 0.734 -4.98*** <.0001 Proposed investment timeframe 4.27 0.790 4.14 0.869 1.45 0.1493 Familiarity with a range of different asset classes 3.80 0.886 4.03 0.937 -2.46** 0.0144 Ethical considerations/constraints 2.24 0.909 2.86 1.029 -6.21*** <.0001 Prior positive or negative experience with shares or managed funds 4.01 0.909 4.17 0.864 -1.76 0.0792 Prior loss of investment capital, eg through a Finance Company collapse, business failure, etc 4.21 0.876 4.42 0.810 -2.45** 0.0146 Note:

* statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

It is of concern to note that the investor’s net wealth and income considerations are considered

less influential by the adviser than the layperson, when dealing with client’s wealth and income

needs is a fundamental part of the advisory role and is a common starting point for financial advice.

That is not to say the AFA does not consider it influential, but rather the layperson puts more weight

on its influence. Further research may be warranted to investigate this apparent mismatch in views.

The considerably larger weighting given to ethical considerations by the layperson may be

reflective of rising societal interest in socially responsible investment and may indicate the older AFA

group has not yet fully appreciated this trend.

Tables 7b and 7c detail the male vs female views on the influences on investor risk tolerance for

both the layperson and the AFA, see Appendix C. There are no significant differences between the

means of the genders in either the layperson or the AFA sample. The differences between the AFA

and layperson, in Table 7a, are therefore not due to gender.

The results of the testing of hypothesis seven are of direct relevance to adviser client interactions

as they reveal the difference in opinion the two groups have regarding the influences on an investors

risk tolerance. Understanding a client’s risk tolerance is at the heart of the AFA role and forms the

basis for personalised advice. That there is a significant difference in view of what is influential on

investor risk tolerance is of concern, particularly as the layperson weighted the importance of six of

ten influencers significantly higher than the AFA. Gender was rated by both groups as having limited

influence on an investors risk tolerance. This is interesting when considering the earlier results of a

significant gender difference in risk perception across almost the full set of financial assets. This

implies the gender disparity in risk perception is unknown by both AFAs and the layperson.

28

Influences on an investor’s acceptance of a financial adviser’s advice

In this final section of the survey, participants were asked to consider what influenced an investor

in accepting financial advice. Research has shown individuals have improved retirement outcomes

when using a financial adviser (Martin and Finke, 2008) however a plan is of no use unless it is

implemented. If AFAs were able understand what the layperson felt influenced their decision to

accept advice, they may better be able to address any underlying barriers to financial plan

implementation. The influences provided in the survey are deliberately centred on the perception

the investor has of the adviser, rather than on factors contributing to the quality or appropriateness

of the advice. This focus is due in part to research by Soderberg (2013) which found adviser gender

and mood affected consumers’ willingness to follow advice.

Table 8a summarises these results. The table notes the t-statistic after testing the null hypothesis

that the means of the AFA and layperson sample are equal. The results show that in five of the eight

influences provided, the AFAs and layperson differ significantly at the 0.01% level. This provides

strong evidence the null hypothesis can be rejected.

Table 8a. T-test for perception of influences on an investor's acceptance of a financial adviser's advice - AFAs vs

layperson

The direction of the mean difference is important to note. In all of the significantly different results the AFA means are lower, that is AFAs perceive these variables to have less influence on the acceptance of advice than the layperson.

The largest difference in perception is the influence of the adviser’s membership of a professional

association, but this is probably the easiest variable for the adviser to change. This result is likely to

be welcomed by industry groups seeking to increase their membership. It is encouraging for the

future diversity of the financial advice industry that the layperson considers the gender and ethnicity

of the adviser to be only moderately influential. Of more concern is the influence the adviser’s age

AFA Layperson

(range 1= no influence, 5 = large influence) Mean Std Dev Mean

Std Dev t Value Pr > |t|

Adviser's age 3.41 0.877 3.38 1.115 0.29 0.769 Adviser's gender 2.54 0.940 2.39 1.174 1.33 0.184 Adviser's ethnicity 2.61 0.961 2.51 1.148 0.93 0.354 The investor's perception of the adviser's personal net wealth 2.86 0.921 3.28 1.142 -3.95*** <.0001 The number of years the adviser has been in their current role 3.52 0.858 3.89 0.901 -4.13*** <.0001 How many years industry experience the adviser has 3.64 0.823 4.07 0.818 -5.23*** <.0001

The adviser's highest qualification 2.86 0.894 3.43 0.987 -6.04*** <.0001 The adviser's membership of a Professional Association 2.57 1.018 3.87 0.967 -13.16*** <.0001 Note:

* statistically significant at the 0.05 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed) *** statistically significant at the 0.001 level (2-tailed)

29

has on the acceptance of financial advice. Whilst it cannot be assumed investors prefer an older

adviser, the importance the investor places on the time the adviser has been in the industry and the

number of years industry experience the adviser has, this would be reasonable conclusion to draw.

Tables 8b and 8c (see Appendix D) detail the male vs female views on the influences on the

acceptance of financial advice for both the layperson and the AFA. There are no significant

differences between the means of the genders in either the layperson or the AFA sample. The

differences noted between the AFA and layperson in Table 8a are therefore not gender-related.

It is apparent that the Advisers are unaware of the importance placed on adviser characteristics by

the layperson. The lay investor places significantly more weight than the AFA on the influences of an

adviser’s experience, qualifications and professional associations. By rating these influences as

moderately to very important, the layperson has identified some of the non-financial factors they

subconsciously consider in their rating of adviser proposals. Those advisers that ‘score’ well in these

trust and respect factors are more likely to have their advice followed. Taking account of, and

demonstrating, these influencers clearly provides an opportunity for advisers to differentiate

themselves in the market.

Summary of Hypotheses

Eight formal null hypotheses were proposed and tested. The first four related to potential

differences in risk perception between the AFA and the layperson, including possible differences due

to gender. The fifth hypothesis related to the experimental section of the survey, while the sixth to

the behaviour of the AFA and layperson following the GFC. The final hypotheses related to potential

differences in views held by the AFA or layperson regarding the influences on risk tolerance and the

acceptance of financial advice. Table 9 summarises the results from the different tests and their

correspondent hypotheses.

The overall results of testing the null hypotheses is that only two of the eight were confirmed. This

means the AFA and layperson samples consistently displayed significant differences in risk

perception, had significant relationships to various demographic characteristics and significantly

differed in their view of the importance of influencing factors on risk tolerance and advice

acceptance.

30

Table 9. Summary of hypotheses and results

Hypothesis Confirmed/Rejected H1 Means of self-assessed risk tolerance rating of AFAs and layperson are equal. Rejected

H2 There is no difference between the AFA and layperson’s perception of the risk of 20 different asset classes. Rejected

H3a There is no difference between the male and female AFA’s perception of the risk of 20 different asset classes

Confirmed

H3b There is no difference between the male and female layperson’s perception of the risk of 20 different asset classes.

Rejected

H4 The demographic characteristics of the AFA and layperson are unrelated to their risk perception score (testing two different dependent variables).

Rejected

H5 There is no difference between the AFA and the layperson in their implicit use of objective risk measures. Rejected

H6 There was no difference between the AFA and layperson’s behaviour following the Global Financial Crisis (GFC) in 2008.

Confirmed

H7 AFA’s and laypeople do not differ in their view of the importance of factors influencing an investor’s risk tolerance.

Rejected

H8 AFA’s and laypeople do not differ in their view of the importance of factors influencing an investor’s acceptance of financial adviser’s advice.

Rejected

Summary and Conclusion This makes it clear that Advisers do not think about risk in the same way as a layperson, but that

this difference did not necessarily lead to significant differences in the way they acted with both

hypothetical and their own personal investments.

If the ideal client adviser relationship is based upon trust and clear communication, there is

potential opportunity to strengthen this relationship by increased communication and clarity about

risk. It is fundamental that advisers and the layperson speak the same ‘language’ when discussing

risk in financial investments as a risk perception gap will be quickly exposed when investments fail to

perform as the investor expects.

The usual response to an identified disparity between the risk perceptions of the lay investor and

the financial expert is to provide the layperson with financial literacy education - which has its

grounding in traditional financial theory. In light of the results of this research we comment:

• Traditional financial literacy education of the layperson may not resolve the disparity in risk

perception. The influence of subjective risk factors, such a loss aversion, may be so fundamental

to the lay investor that an updated strategy in financial literacy education may be required.

• The onus of education should also rest upon the AFA and the advisory industry. Rather than

focussing primarily on what risk is and how it can be measured or objectively mitigated, AFAs

may also require education on understanding behavioural finance concepts and how to

communicate about risk to a layperson – a two-way conversation with information sharing on

both sides.

31

• While the industry regulator may provide good resources via their website many lay investors

seem unaware of their role and the resources available. We suggest promotion of the Authority

resources should be compulsory for all AFAs.

There are a few limitations to this study. Whist the AFA sample size was robust and in general

representative of the industry, the layperson sample was not equal to the AFA sample size and may

not have been representative of the wider New Zealand public. This is evident in the gender

distribution, lack of ethnic diversity and dominance of employment in white collar roles by

respondents. The study be usefully repeated with official funding so a wider sample of both AFAs

and lay investors can be obtained. However, the sample size compares well with prior studies.

32

Appendices

Appendix

Table 3b. Perceived riskiness of various Asset classes

(range 1= very low risk, 5 = very high risk) Female AFA Male AFA

No. of responses Mean Std Dev

No. of responses Mean Std Dev t Value Pr > |t|

Savings account 49 1.20 0.499 195 1.18 0.473 0.25 0.806 Bank Fixed Term Deposit 49 1.27 0.531 195 1.31 0.546 -0.56 0.580 Finance Company Fixed Term Deposit 49 3.80 0.912 193 3.84 0.922 -0.33 0.740 Bonus Bonds 48 1.50 0.851 195 1.53 0.839 -0.24 0.808 Diversified Managed Fund 49 2.86 0.577 194 2.88 0.534 -0.21 0.834 Kiwisaver or Superannuation Fund 49 2.71 0.577 193 2.68 0.630 0.38 0.707 Employer-contribution Superannuation scheme (not Kiwisaver) 47 2.68 0.594 191 2.67 0.650 0.11 0.914 NZ Corporate Bond rated BBB or lower 49 3.43 0.957 191 3.75 0.906 -2.11* 0.038

NZ Corporate Bond rated BBB+ or higher 49 2.63 0.883 192 2.47 0.825 1.14 0.259 NZ Government Stock 49 1.24 0.522 195 1.24 0.545 0.05 0.963 Shares in a NZ listed company 49 3.63 0.602 193 3.60 0.639 0.38 0.707 IPO of a Company 48 4.25 0.758 193 4.22 0.767 0.26 0.792 Private Equity investment 47 4.64 0.605 191 4.53 0.687 1.08 0.283 Shares in an overseas listed company 49 3.65 0.751 193 3.73 0.701 -0.61 0.544 Precious metals (eg Gold) 49 3.98 0.968 191 4.01 1.064 -0.16 0.872 Foreign currency 48 4.08 0.942 193 4.16 0.894 -0.48 0.633 Endowment or Investment-linked insurance policy 47 2.36 0.987 188 2.68 0.929 -1.97* 0.053 Art or Antiques 45 3.93 1.053 183 3.97 1.071 -0.22 0.824 Residential investment property 49 3.08 0.812 194 3.29 0.814 -1.59 0.115 Non-residential investment property 49 3.24 0.723 194 3.46 0.809 -1.85 0.068 Note:

* statistically significant at the 0.05 level (2-tailed)

Table 4b. Multiple regression of demographic characteristics on Layperson SA Risk (n = 166)

Dependent Variable: SA_Risk

MODEL1a Beta SE Pr > |t| Intercept 0.81 0.422 0.056

Gender 0.29 0.236 0.219 Age 0.07 0.051 0.169 Married -0.05 0.153 0.740 Dep -0.07 0.149 0.636 Ethnicity 0.22 0.171 0.190 Edu 0.09* 0.046 0.048 Inv_cnt 0.05 0.033 0.173 B_mark 0.25** 0.094 0.009 Review 0.09 0.071 0.223 Z_Score -0.15 0.249 0.541 HH_Fn_As 0.06 0.050 0.219 Home_own -0.29 0.174 0.101 HH_Income 0.23 0.127 0.071 Fin_lit_score -0.11 0.119 0.372

33

R-Square 0.220

Adj R-Sq 0.147 Note:

* statistically significant at the 0.05 level (2-tailed) ** statistically significant at the 0.01 level (2-tailed) *** statistically significant at the 0.001 level (2-

tailed)

Table 1. Descriptive Statistics

AFA (n=244) Layperson (n=165)

Percentage Mean Std Dev Percentage Mean Std Dev

Gender: Male 79.9 0.799 0.026 35.5 0.355 0.037

Age 3.750 0.064 2.813 0.097

Age_1: 20 – 29 years 1.6

17.0

Age_2: 30 – 39 years 10.2

25.5

Age_3: 40 – 49 years 24.6

29.1

Age_4: 50 – 59 years 38.5

15.8

Age_5: 60+ years 25.0

12.7

Married 89.4

78.8

Financial dependents 65.2

52.7

Ethnicity: White 91.4

78.3

Education

Edu_1: Secondary school qualification

30.3

Edu_2: Tech/Trade qualification or Level 5 Cert. (min req. for AFA) 12.7

4.2

Edu_3: Other tertiary qualification

18.1

Edu_4: Degree 37.3

27.9

Edu_5: Post Grad, Masters or PhD 50.0

19.4

Fin_Lit

2.608 0.045

Fin_Lit0: 0 correct

0.6

Fin_Lit1: 1 correct

3.0

Fin_Lit2: 2 correct

31.3

Fin_Lit3: 3 correct

65.4

Fin_Lit4: AFA

Home_owner 90.6

76.4

HH_Income 2.586 0.033 2.355 0.044

HH_Inc1: Less than $50,000 0.8

4.8

HH_Inc2: $50,000 - 149,999 39.8

54.8

HH_Inc3: $150,000 and over 59.4

40.4

HH_Fin_Assets 3.918 0.088 2.825 0.117

Assets1: Less than $50,000 5.3

23.6

Assets2: $50,000 - $149,999 18.0

27.3

Assets3: $150,000 - $299,999 11.9

16.4

Assets4: $300,000 - $499,999 9.0

7.9

Assets5: $500,000 and over 55.7

24.8

Review period 1.459 0.043 1.783 0.064

Rvw_Freq: cycle less than 12 months 64.3

47.3

34

Rvw_Mod: cycle 1 - 3 years 25.4

27.7

Rvw_Inf: cycle 3 years + or infrequently 10.2

25.4

Invest count: No. of asset classes held 6.484 0.154 5.120 0.162

SA_Risk_profile 3.369 0.040 2.482 0.067

Z_Score 0.000 0.027 0.004 0.035

Table 7b. T-tests for perception of influences on an Investor's risk tolerance, separated by gender: Layperson

Female layperson Male layperson

(range 1= no influence, 5 = large influence) Mean Std Dev Mean Std Dev t Value Pr > |t| Age 4.22 1.037 3.90 1.140 1.77 0.080 Gender 2.54 1.135 2.36 1.256 0.95 0.347 Ethnicity 2.66 1.132 2.49 1.251 0.88 0.383 Net Wealth 4.07 0.866 4.20 1.013 -0.82 0.412 Net Income or Income needs 4.27 0.734 4.36 0.737 -0.71 0.478 Proposed investment timeframe 4.20 0.841 4.05 0.918 1.01 0.316 Familiarity with a range of different asset classes 3.99 0.957 4.10 0.904 -0.74 0.460 Ethical considerations/constraints 2.88 1.025 2.81 1.042 0.39 0.700 Prior positive or negative experience with shares or managed funds 4.22 0.836 4.08 0.915 0.90 0.368 Prior loss of investment capital, eg through a Finance Company collapse, business failure, etc 4.46 0.780 4.34 0.863 0.88 0.381

Table 7c. T-tests for perception of influences on an Investor's risk tolerance, separated by gender: AFA

Female AFA Male AFA

(range 1= no influence, 5 = large influence) Mean Std Dev Mean Std Dev t Value Pr > |t| Age 4.02 0.901 4.10 0.928 -0.53 0.597 Gender 2.43 0.957 2.60 0.965 -1.12 0.267 Ethnicity 2.53 2.249 2.33 2.190 1.25 0.216 Net Wealth 3.67 0.774 3.72 0.961 -0.38 0.704 Net Income or Income needs 3.88 0.726 3.91 0.895 -0.29 0.773 Proposed investment timeframe 4.35 0.723 4.25 0.807 0.85 0.397 Familiarity with a range of different asset classes 3.71 0.791 3.83 0.908 -0.85 0.395 Ethical considerations/constraints 2.33 0.966 2.22 0.895 0.70 0.488 Prior positive or negative experience with shares or managed funds 4.06 0.966 4.00 0.897 0.40 0.689 Prior loss of investment capital, eg through a Finance Company collapse, business failure, etc 4.33 0.747 4.18 0.905 1.18 0.242

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Table 8b. T-test for perception of influences on an investor's acceptance of a financial adviser's advice – female vs male layperson

Table 8c. T-test for perception of influences on an investor's acceptance of a financial adviser's advice – female vs male AFA

Female AFA Male AFA

(range 1= no influence, 5 = large influence) Mean

Std Dev Mean

Std Dev t Value Pr > |t|

Adviser's age 3.33 0.922 3.43 0.867 -0.72 0.476 Adviser's gender 2.59 0.977 2.52 0.932 0.44 0.658 Adviser's ethnicity 2.51 0.845 2.63 0.988 -0.86 0.391 The investor's perception of the adviser's personal net wealth 2.96 0.816 2.83 0.946 0.95 0.343 The number of years the adviser has been in their current role 3.45 0.792 3.54 0.875 -0.73 0.467 How many years industry experience the adviser has 3.65 0.830 3.63 0.823 0.17 0.867 The adviser's highest qualification 3.00 0.866 2.82 0.899 1.29 0.202 The adviser's membership of a Professional Association 2.67 1.008 2.54 1.022 0.84 0.406

Female layperson Male layperson

(range 1= no influence, 5 = large influence) Mean

Std Dev Mean

Std Dev t Value Pr > |t|

Adviser's age 3.3645 1.1525 3.4068 1.0524 -0.24 0.8111 Adviser's gender 2.3738 1.2326 2.4237 1.07 -0.27 0.7859 Adviser's ethnicity 2.4579 1.1597 2.5932 1.1314 -0.73 0.4663 The investor's perception of the adviser's personal net wealth 3.1963 1.1528 3.4237 1.1173 -1.24 0.2169 The number of years the adviser has been in their current role 3.9252 0.8763 3.8305 0.9496 0.63 0.5287 How many years industry experience the adviser has 4.0467 0.7817 4.1017 0.8846 -0.40 0.6907 The adviser's highest qualification 3.3364 0.9311 3.6102 1.067 -1.65 0.1011 The adviser's membership of a Professional Association 3.9159 0.9529 3.7966 0.9962 0.75 0.4549

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References

Atkinson, S.M. Boyce Baird, S. & Frye, M.B. (2003). Do Female Mutual Fund Managers Manage Differently? The Journal of Financial Research, Vol. XXVI (1), 1-18. Barber, B.M. & Odean, T. (2001). Boys will be boys: Gender, overconfidence and common stock Investment. Quarterly Journal of Economics, Vol. 116 (1), 261-292. Beckmann, D. & Menkhoff, L. (2008). Will Women be Women? Analyzing the Gender Differences Among Financial Experts. KYKLOS, Vol. 61 (3), 264-384. Bliss, R.T. & Potter, M.E. (2002). Mutual Fund Managers: Does Gender Matter? Journal of Business & Economic Studies, Vol. 8 (1), 1-15. Charness, G. & Gneezy, U. (2012). Strong Evidence for Gender Differences in Risk Taking. Journal of Economic Behavior & Organisation, 80, 50-58. Clark-Murphy, M. & Soutar, G.N. (2008). Do retail stockbrokers understand clients’ investment

preferences? Journal of Financial Services Marketing, 13, 135-149. Commerce Commission (2010). Commerce Commission settles with ANZ and ING over investment funds. Retrieved from: http://www.comcom.govt.nz/the-commission/media-centre/media-

releases/detail/2010/commerce-commission-settles-with-anz-and-ing-over-investment-funds Croson R. & Gneezy,U. (2009). Gender differences in Preferences. Journal of Economic Literature, 47(2), 448-474. Diacon, S. (2004). Investment Risk perceptions: Do consumers and advisers agree? The International Journal of Bank Marketing, 22 (3), 180-198. Edmonds, S. (2014). Changing face of adviser force. New Zealand Asset, 131, 14-18. Eckel, C & Grossman, P. (2007) Men, Women and Risk aversion: Experimental Evidence, Handbook of

Experimental Economic Results, ed C. Plott & V. Smith, New York, Elsevier Finucane, M. L., Slovic, P., Mertz, C. K., Flynn, J., & Satterfield, T. A. (2000). Gender, race, and perceived risk: The ‘white male’ effect. Health, Risk & Society, 2(2), 159-172. Hilgert, M.A. Hogarth, J.M. & Beverly. S.G. (2003). Household financial management: the connection

between knowledge and behaviour. Fed. Reserve Bulletin, 89(7), 309-322. Jansen, C., Fishcer, R. & Hackethal, A. (2008). The influence of financial advice on the asset allocation of individual investors. Working paper. Retrieved from http://ssrn.com/abstract=1102092. Jacobsen, B, Lee, J.B. & Marquering , W. (2008), Are men more optimistic?, SSRN 1030478 Lusardi, A. & Mitchell, O.S. (2011). Financial literacy and retirement planning in the United States. Journal of Pension Economics and Finance, 10, 509-525. Martin, T.K. & Finke, M. (2008). Planning for Retirement. Working paper. Retrieved from http://ssrn.com/abstract=2195138 Nelson, J.A. (2013). Not-So-Strong Evidence for Gender Differences in Risk Taking. Working Paper 2013-06. Retrieved from http://repec.umb.edu/RePEc/files/2013_06.pdf Niessen, A. & Ruenzi, S. (2006). Sex Matters: Gender and Mutual Funds. Working paper. Retrieved from http:://ssrn.com/abstract= Olsen, R.A. (1997). Investment Risk: The Experts’ Perspective. Financial Analysts Journal, March/April, 62-66. Olsen, R.A & Cox, C.M. The Influence of Gender on the Perception and Response to Investment Risk: The Case of Professional Investors. The Journal of Psychology and Financial Markets, Vol. 2(1), 29-36. Ricciardi, V. (2004, July). A Risk Perception Primer: A Narrative Research Review of the Risk Perception Literature in Behavioral Accounting and Behavioral Finance. Paper presented at the

Society for the Advancement of Behavioral Economics/ International Association for Research in Economic Psychology conference, Philadelphia, U.S.A.

Ricciardi, V. (2008). The Psychology of Risk: The Behavioral Finance Perspective. In F J Fabozzi (Ed).

37

Handbook of Finance. Hoboken, NJ: John Wiley & Sons. Roszkowski, M.J. & Davey, G. (2010). Risk Perception and Risk Tolerance Changes Attributable to the 2008 Economic Crisis: A Subtle but Critical Difference. Journal of Financial Service Professionals, July,

42-53 Roszkowski, M.J. & Grable, J. (2005). Gender Stereotypes in Advisors’ Clinical Judgments of Financial Risk Tolerance: Objects in the Mirror Are Closer than They Appear to Be. Journal of Behavioral Finance, Vol 6 (4), 181-191. Sache, K. Jungermann, H. & Belting, J.M. (2012). Investment Risk – The perspective of individual

investors. Journal of Economic Psychology, 33, 437-447. Schwartz, B. (2004). The Paradox of Choice. New York, NJ: HarperCollins. Shapira, Z. & Venezia, I. (2001). Patterns of behvior of professionally managed and independent Investors. Journal of Banking & Finance, Vol 25 (8), 1573-1587. Söderberg, I. (2013). Relationships between advisor characteristics and consumer perceptions. International Journal of Bank Marketing, Vol. 31 (3), 147-166. Söderberg, I. & Wester, M. (2012). Lay actions in the face of crisis – Swedish citizens’ actions in Response to the global financial crisis of 2008. The Journal of Socio-Economics, 41, 796-805. Veld, C. & Veld-Merkoulova, Y.V. (2007). The risk perceptions of individual investors. Journal of Economic Psychology, 29, 226-252. Weber, E.U., Blais, A. & Betz, N. E. (2002). A Domain-specific Risk-attitude Scale: Measuring Risk Perceptions and Risk Behaviors. Journal of Behavioral Decision Making, 15, 263-290.