How do consumers evaluate risk in
financial products?

Received (in revised form): 20th January, 2005

Kathleen Byrne

has over 20 years’ experience in the insurance industry, spanning life insurance, investment and general insurance. Her

current focus is on lump sum investment products including guaranteed bonds and structured products. She joined Cardif

Pinnacle in 1994, with responsibility for Actuarial, and was Group Actuarial Director prior to her appointment as Managing

Director – Investments in October 2002. She is currently responsible for all aspects of Pinnacle’s investment business from the

design of new products through to service delivery.

Abstract

Decision-making processes consumers use in investing lump sums are

reviewed, focusing on how investment risk is perceived and assessed. Primary research

was undertaken with investment customers to explore the role played in evaluation of

investment risk by risk perceptions and risk propensity. Both the literature review and

the research findings indicate the central role risk perceptions play in financial

decisions. Sitkin and Weingart’s risk model is used as a research framework. Risk

propensity and risk perception were found to be negatively correlated, however, deposit

accounts were selected for investment irrespective of how risky a respondent considered

them to be.

Risk perceptions and expected return were positively correlated for all asset types

apart from property. Further investigation revealed that experts exhibited positive

correlation in risk return judgments but novices showed no correlation. There was no

correlation between risk and return for either novices or experts for property.

Return expectations were positively correlated with investment allocation. Provision of

past performance information appears to create an expectation for future returns around

the same level as past returns. Research findings suggest that outcome history is a

predictor variable, with a Positive outcome history leading to higher risk Propensity. The

level of risk customers are assuming shows a significantly increasing trend.

Keywords Decision making, risk perceptions, financial products, consumer behaviour,

behaviour finance

INTRODUCTION

Falling stock markets over recent years
have led to some financial products
returning less than the initial investment to
consumers when they have matured.
Despite product literature including
information about risk to capital,
consumers appear not to have understood
the risks they took on.

Providers of financial services products

need to investigate how they evaluate
financial products, including how
consumers perceive investment risk, to
ensure that they understand the products
they buy. Decision making within
financial products has not received much
research attention and consumer
understanding of risk within financial
products even less so. These topics are of
practical importance for consumer
understanding of financial products.

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21

Kathleen Byrne
Cardif Pinnacle,
Pinnacle House,
A1 Barnet Way,
Borehamwood,
Hertfordshire
WD6 2XX, UK.
Tel: [phone];
Fax: [phone];
e-mail: kathy.byrne@
cardifpinnacle.com

Literature on decision making and its

relevance to financial decisions is reviewed.
Risk is defined in relation to financial
products and different facets of risk are
discussed. The research methodology
includes a research model, constructs used
and details of the research sample. Results
are presented and practical implications for
product providers are discussed.

LITERATURE REVIEW

Consumer decision making literature in
the context of financial products is
surprisingly scarce. This was noted by
Harrison

1

in this Journal in 2003 and

Goodman

2

explained the actuarial

profession’s recent research into consumer
understanding of risk. Both economists
and psychologists have studied risky
decision making. However, much research
relates to simple choices, often using
gambling devices. There is little coverage
of consumer understanding of financial
risk involved in the evaluation of financial
products.

Decision-making models

Decision making for financial products is
complex because products are intangible,
outcomes are uncertain and financial risk
following a poor decision is significant.
Marketing textbooks

3,4

present a five-stage

model of complex decision making.
Evaluation of competing products is the
stage at which investment risk is evaluated
and quantified and this is the focus of this
paper.

Evaluation of alternatives
Marketing textbooks

3,4

state that

consumers seek certain benefits from the
alternatives they consider. Products are a
series of attributes with different abilities
to produce benefits to satisfy the need
identified. Consumers decide which
attributes are relevant and most important

to them. While different processes may be
used to evaluate products and form
preferences, decisions are rational and
cognitively based.

Risk perceptions play an important role

in evaluating competing products and
behavioural finance authors

5–10

have

demonstrated that risky decision making
can be irrational rather than rational and
cognitively based. In complex decision
making consumers must make choices
between different alternatives and use
heuristics, or rules of thumb, to simplify
the choice they make and in doing so
introduce bias into their decisions.
Heuristics and biases include
representativeness,

5,11–16

availability,

6,17,18

anchoring,

6,16

overconfidence,

19

loss

aversion,

11,17,20

status quo bias,

21,22

hindsight bias,

23

confirmation bias

17

and

mental accounting.

24

Representativeness leads to bias because

people ignore objective information that
does not fit with their stereotype and place
more weight on information confirming
stereotypes. Jordan and Kass

16

investigated

the role of judgmental heuristics in private
investors’ evaluation of risk and return.
Anchoring, representativeness and the
affect heuristic are used by investors and
lead to biases in risk/return judgments.
They found that anchoring effects occur in
expected returns, whereas the affect and
representativeness heuristics affect
perceived investment risk. Both informed
and uninformed groups show judgmental
heuristics, with uninformed investors
showing larger biases.

The way that information is presented

or ‘framed’ means that alternative
descriptions of a decision problem give rise
to different preferences when the same
problem is framed differently.

25,26

What is risk?

The classical economist view of risk is a
situation where the future outcome is

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Byrne

unknown but a probability can be placed
on each possible outcome. Dean and
Thompson

27

put forward two major

concepts of risk, the positivist and
contextualist. A positivist concept of risk is
defined in terms of probabilities based on
objective, verifiable experience and a
contextualist concept of risk is based on
the context in which it is used. Olsen

28

suggests that risk is a continuum taking
into account both the emergent and
multidimensional nature of risk and
concludes that experts tend to focus on
probabilistic models whereas non-experts
use contextual models.

Kahneman and Tversky

6

were the first

to bring behavioural aspects into
economically based risk models. They
found that people appear irrational in
decision making and utility theory does
not fully explain how people make
decisions. They offered an alternative
theory, prospect theory, which assigns
values to gains and losses rather than to
final assets and replaces probabilities with
decision weights. They observed two
effects, first the certainty effect, which
leads to risk aversion in choices for sure
gains and risk seeking in choices for sure
losses. Secondly, the isolation effect where
people generally discard components
shared by all prospects leading to
inconsistent preferences when the same
choice is presented in different formats.
Other models consistent with prospect
theory include Einhorn and Hogarth’s

29

ambiguity model and Harvey’s

17

heuristic

judgment theory.

Sitkin and Pablo

30

identify several

studies that present contradictory results to
those predicted by prospect theory. They
present a model, based on theoretical
analysis, reconciling these contradictions
by examining the usefulness of placing risk
propensity and risk perception in a more
central role than previously recognised.
Sitkin and Weingart

31

test this model in

which risk propensity and risk perception

mediate the effects of problem framing
and outcome history on risky decision-
making behaviour. They conclude that a
mediated model of risk behaviour is more
powerful than one in which the direct
effects of a large number of antecedent
variables are examined individually.

Risk propensity

Several researchers

30,32,33

have investigated

the link between personality and risk
propensity to test whether risk propensity
is a stable personality trait. Their findings
suggest that risk propensity or risk
preference is not a personality trait and
people’s risk perceptions are important in
their propensity to take or avoid risks.
Ensuring people have the right perception
of the level of risk should mean that they
understand the risks they are taking on.

Investment risk

Olsen

28

found that professional investment

managers and experienced individual
investors share a common conception of
investment risk. Jordan and Kass

16

found

that investors’ risk perceptions have four
dimensions:

— downside risk
— upside risk
— volatility
— ambiguity

While there is overlap between these and
Olsen’s findings, there are also differences.
Olsen did not include the ambiguity
dimension of Jordan and Kass, although he
considered economic uncertainty.

Risk and return

Ganzach

34

found that for familiar assets,

risk/return judgments tend to be derived
from past performance as proxies for
current risk perceptions. The relationship

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between risk and return judgments is
positive. For unfamiliar assets, risk and
return judgments are derived from global
preferences and the relationship between
risk and return is negative.

Muradoglu

35

looked at experts’ and

novices’ predictions of stock prices.
Investors are positive feedback traders
when presented with a time series
excluding contextual information,
supporting previous research. With
contextual and real-time information,
however, optimism is the norm, bullish
trends are extrapolated and mean reversion
is expected in bear markets only.
Differences are observed in return
expectations and perceived risks due to
contextual information, stock market
trends and expertise.

Summary

People appear far from rational in making
decisions under risk because the complex
nature of financial decisions means that
heuristics used to simplify complex
decisions bring bias into the evaluation.
Risk perceptions appear to play a central
role in financial decisions. The role of risk

propensity, past experience and framing
are also important. Experts appear better
at evaluating investment risk than novices
and the impact of expertise on risk
perceptions is likely to be important. (See
Table 1.)

METHODOLOGY

Research context

The research sample was drawn from
investment consumers so that the results
would be directly applicable to investment
products. The research instrument was
designed to simulate investment decisions
that consumers might expect to make
when considering an investment rather
than using simplified gambling examples
that have often been used to research risky
decision making.

Research model

Role of risk perceptions
Sitkin and Weingart’s model

31

was

developed for management decisions under
risk but appears potentially applicable to
financial decisions as risk perceptions are a

Table 1

Literature summary

Main research findings

Author(s)

Financial decisions use complex decision making

Assael, 1995

3

; Kotler, 2003

4

Evaluation of alternatives
Decisions are rational and cognitively based

Assael, 1995

3

; Kotler, 2003

4

Risky decision making can be irrational

Tversky & Kahneman, 1974

15

; Kahneman &

Tversky, 1979

16

; Thaler, 1980

7

; Slovic, 1991

8

;

Camerer, 1998

9

; Rabin, 1998

10

Framing

Khaneman, 2000

25

; Johnson

et al, 1993

26

Risk
Risk is multidimensional and emergent

Dean & Thompson, 1995

27

; Olsen, 1997

28

;

Jordan & Kass, 2002

16

Risk propensity is not a personality trait

Sitkin & Pablo, 1992

30

; Sitgin & Weingart,

1995

31

; Weber & Milliman, 1997

32

; Weber, Blais

& Betz, 2002

33

Risk/Return relationship is positively correlated for
experts/familiar assets and negatively correlated for
novices/unfamiliar assets

Ganzach, 2000

34

; Muradoglu, 2002

35

Risk propensity and risk perception are key mediating
variables in risky decision making

Sitkin & Pablo, 1992

30

; Sitkin & Weingart,

1995

31

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key mediating variable in both types of
decision. For financial decisions, the
expected return and investor expertise are
also expected to be important. This model
is used as a research framework, adapted
to include return and expertise variables
(Figure 1). A positive or negative sign
indicates the direction of each relationship.
The purpose of the research is to test
whether the relationships indicated in the
model exist and their strength. Therefore
the research looks at pairs of relationships
rather than testing the whole model. The
hypotheses to be investigated are also
shown and are now discussed.

H

1

:

Outcome history is positively
correlated with risk propensity

Successful risk-taking experience is
expected to lead to a higher risk propensity
and unsuccessful experience to risk aversion
in future decisions.

H

2

:

Risk propensity is negatively
correlated with risk perception

The higher the level of perceived risk, the
less likely someone is to take that risk and
vice versa. In the investment context, the
higher the level of perceived risk the less
likely someone is to invest and, if they do
invest, the higher the perceived risk the
smaller the investment. Conversely, the
lower the perceived risk, the more they
will invest.

H

3

:

There is positive correlation between
risk perception and expected return for
expert investors

H

4

:

There is negative correlation between
risk perception and expected return for
novice investors

The link between risk perception and a
decision to invest is complicated by an
additional variable, which is the expected
return from the investment. The literature
review shows the rational view is that the
higher the level of risk associated with an
investment then the higher the return the
investor should expect. This is where

Outcome History

Problem Framing

Risk

Propensity

Risk

Perception

How much to

invest

Return

Expectations

Experts

Novices

+

+

+

–

–

–

H3

H4

H2

H1

H5

+

+

+

H1

Source: Adapted from Sitkin and Weingart (1995)

31

Figure 1

Model of the determinants of investment decisions

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investment expertise may differentiate
behaviour, with experts following the
rational view of positive correlation
between risk and return and novices
showing negative correlation.

H

5

:

Return expectations will be driven by
past performance information provided
as an anchor

Framing effects are difficult to measure in
complex situations and so the effect of
anchoring on return expectations is the
only framing effect investigated.

Constructs

Experience
Several authors looked at investors’
expertise in their research.

16,28,35

Jordan

and Kass’s construct using knowledge (‘I
know a lot about investing money’) and
experience (‘I am very experienced at
investing’) was used as this was most
relevant to financial products.

Risk perception
This construct encapsulated components
covering both probabilistic and contextual
elements of risk perception:

— Upside risk (potential for good returns)
— Downside risk (potential for loss, not

meeting investment objectives, strength
of regulation)

— Volatility (returns varying over time)
— Feelings (uncertainty, worry)

Risk propensity
This was defined as the likelihood to
engage in a particular activity and in the
investment context was measured by the
amount invested in a specified product.

Research sample

Investment customers of Pinnacle
Insurance plc (Pinnacle) were selected as a
suitable group to study as they had made

or considered a lump sum investment. By
using this customer base bias is introduced,
as Pinnacle’s customers may not be
representative of the whole population of
UK consumers with a lump sum to invest.

Research was conducted by

questionnaire mailed to selected customers.
In order to be able to investigate decision
making across different customer groups,
the customer base was segmented by
customers’ past investment experience with
Pinnacle. Two main product types had
been offered in the past, a guaranteed
insurance bond (GIB) and structured
capital at risk products (Scarps) where the
return was linked to the performance of
specified stock market indices. Falling
stock markets have reduced returns under
Scarps and equities but have not affected
GIB returns as GIBs are fixed rate
products. GIB customers have had good
experience with their investment, while
Scarp customers may have been
disappointed with their returns. A third
customer group, direct customers, was
identified. Some direct customers have
policies with Pinnacle and some do not, so
this group has unknown past experience.
Three customer groups were identified as
follows:

Premier customers
These customers had structured capital
at risk (Scarp) policies (‘Premier
Bonds’) that matured between
November 2003 and March 2004.

GIB customers
These customers had guaranteed
insurance bonds (GIBs) that matured
between June 2003 and June 2004 and
were subject to similar economic
conditions to Premier customers.

Direct customers
These customers have registered to
receive investment offers and invest
directly rather than using an
independent financial adviser (IFA).

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Two versions of the questionnaire were
used; version two included past
performance information to test the effect
of anchoring on return expectations. Each
of the sample groups needed to have
approximately 50 respondents so that
results compared across groups would be
statistically significant. A 10 per cent
response rate was expected, therefore
questionnaires were sent to roughly 1,000
customers in each customer group so that
around 50 responses for each version of
the questionnaire would be obtained.

An extract from the customer database

for the three customer groups was
obtained at 30th June, 2004. A report was
run against this database to remove the
following customers:

— Customers who had opted out from

mailings

— Private bank customers, since these

IFAs might object to questionnaires
being sent to their clients

— Customers who had invested over

£100,000 in a single investment
because the aim was to look at the
views of the mass affluent rather than
ultra high net worth customers.

The research instrument

Questions were based on previously tested
questionnaires where possible because these
questions had already been tested for
validity. The questionnaire was divided
into five sections with an introductory

paragraph preceding these. Questions were
structured with a four-point scale so that
respondents had to make a choice on
whether or not they agreed or disagreed
with the statement. The choice was
limited to four, as customers were not
expected to be able to make very fine
distinctions. In some questions a ‘don’t
know’ response was allowed so that
respondents were not forced to make a
choice when they genuinely did not know
how to answer. Appendix 1 shows the
questions relevant to this paper.

RESULTS

There were 371 useable questionnaires, an
overall response rate of 11.24 per cent, with
53 per cent responding on version 1 and 47
per cent on version 2. Table 2 summarises
the responses. Statistical tests were based on
95 per cent confidence intervals.

Demographics

Figure 2 shows the age profile of the
respondents compared with the age profile
of the database that was mailed with the
questionnaire. Age was not available in the
Direct database, so comparisons are shown
for the GIB and Premier database against
GIB and Premier respondents and against
all respondents. The old age profile of
respondents reflects the age profile of the
database.

Gender information was available for

both respondents and the full mailing

Table 2

Questionnaire responses

Premier
customers

GIB customers

Direct customers

Customer
group
unknown

Number mailed

622

618

501

500

530

530

Questionnaire version

1

2

1

2

1

2

1

2

Responses
received

54

66

56

61

63

29

24

18

Responses by
group

120

117

92

42

Response rate

9.68%

11.69%

8.68%

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database. Figure 3 shows a comparison
between the database and respondents,
which shows that males are over-
represented in the sample and females are
under-represented compared with the
database population.

Investment expertise

Respondents’ ratings of their knowledge
and experience with investments were
used to calculate an expertise score as the
arithmetic mean of these scores.

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

%

of population

18 to 29

30 to 39

40 to 49

50 to 59

60 to 69

70 to 79

80 and over

Age Group

GIB & Premier Database

GIB & Premier Respondents

All Respondents

Figure 2

Age profile

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

%

of population

Male

Female

Unknown

Gender

Database

All Respondents

Figure 3 Gender profile

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The level of investment expertise was

compared between the three customer
groups. One-way ANOVA tests, followed
by the Scheffe procedure, identified
significant differences. Direct customers
have significantly higher expertise levels
than other customers but GIB and Premier
customers have similar expertise.

A grouping variable was set according

to expertise score. Experts (49 per cent of
sample) included respondents who agreed
that they were both knowledgeable and
experienced with investments, while
novices (51 per cent) disagreed with these
statements.

Risk profile

The financial products investors hold in
their portfolios, both now and previously,
were investigated. This allowed the actual
level of risk taken on to be measured for
comparison with risk perceptions and
investment choices. For each respondent
two variables were calculated by
weighting their investment holdings
according to the level of risk, as defined
by an expert, to calculate a current risk
score and a past risk score. These variables
can be compared to see whether past risk
propensity is correlated with current risk
propensity, enabling Hypothesis 1 to be
tested. There was strong correlation
between current and past risk scores (78
per cent), which confirms Hypothesis 1
and suggests that past risk behaviour is an
indicator of future risk behaviour.

A paired sample t test was used to test

the null hypothesis of no difference in
current and past investment risk scores.
This showed that differences were
statistically significant. The mean past risk
score is lower than the mean current risk
score, which suggests that the level of risk
in investment portfolios is increasing over
time. This increase in risk propensity
supports the proposition that successful
risk taking leads to higher risk propensity.

Risk perception and propensity

Respondents completed risk assessments
and risk likelihoods (likelihood of
engaging in each activity) for nine
gambling and simple investment decisions.
For most cases there was negative
correlation between risk likelihood and
risk perception; only one gambling
activity did not show significant
correlation between risk likelihood and
risk perception. This means that the higher
the level of perceived risk the less likely a
respondent is to engage in it.

Investment risk perception

Respondents provided their perception of
risk inherent in four defined financial
products (deposits, property, equities and a
structured capital at risk product) by
answering a series of eight perception
questions for each product. An average
risk perception score was calculated by
product, Cronbach’s alpha ranged from
0.60 to 0.81. Product definitions are shown
in Appendix 2.

Future returns

Expected future returns under the four
financial products assessed for risk were
investigated. Respondents using version 2
of the questionnaire were given past return
information for each product.

Independent sample t tests were used to

test Hypothesis 5 to establish whether
there was a difference in means between
the two questionnaire groups. There was a
significant difference for deposit accounts,
property and Scarps but not for equities.
This may be because the past performance
for equities has been poor and produced
negative returns and performance
information is discarded as it does not fit
with respondents’ future return
perceptions. In all cases, the group given
past performance data has a higher mean

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score than the group not provided with
this information. This suggests that for
some investments, providing past
performance information creates
expectations of higher returns than if no
past performance information were
provided. Therefore Hypothesis 5 is
proved for some, but not all, products
tested.

Correlations between risk perception

and future return expectations were
investigated. For all product types except
property, there was significant correlation.
Experts had significant positive correlation
between risk assessment and future return
expectations for all product types except
property, while novices showed no
correlation. These results support
Hypothesis 3 for all product types except
for property, but do not support
Hypothesis 4.

Individual investors who do not have

expertise in property may explain the lack
of correlation. Property is the third least
likely investment to be held, only 26 per
cent of respondents currently hold
property investments while a further 10
per cent have held this type of investment
previously. A chi-squared test shows no
difference in the propensity to hold
property between experts and novices.

Investment decision

Respondents were asked to allocate a
£10,000 investment between the four
financial products used in previous
questions. Table 3 shows the mean amount
allocated between products.

The amount allocated differed between

customer groups, so a one-way ANOVA

test was carried out to test significance.
Differences were significant for deposit
and Scarp allocation but were not
significant for property or equity
allocations. Using a Scheffe procedure, the
differences in means for deposit allocation
are significant between Premier and GIB
customers for deposit allocation, with
Premier customers allocating the least to
this product type.

Interestingly, the difference in allocation

for Scarps was between Premier customers
and Direct customers, with Premier
customers allocating more than any other
customer group. These customers could be
considered to have had bad previous
experience with this product and might
therefore have been expected to be less
likely to invest. Conversely, Premier
customers, having invested in Scarps
previously, are more likely to be familiar
with this investment.

The relationship between risk

perceptions and investment allocation (risk
propensity) was used to test Hypothesis 2
for complex products. Negative correlation
between risk perception and investment
allocation was significant for all investment
types except for deposits. This means that
investors allocate least to the assets they
perceive as most risky. Hypothesis 2 is
proved for all products except deposits.

Further investigation into deposits

showed no significant differences among
experts and novices. Individuals using
deposit accounts for ‘rainy day’ money
might explain this and therefore most
investors allocate some money to a deposit
account irrespective of their risk
assessment. Deposits feature in 89 per cent
of respondents’ portfolios.

Correlation between future return

expectations and investment allocation was
significant for all investment types except
for deposits. The higher the expected
return, the higher the investment allocation.
No significant differences were found
between experts and novices for deposits. A

Table 3

Mean investment allocations

Product

Mean amount allocated

Deposits

£4,723.30

Property

£2,264.56

Equities

£2,259.55

Scarps

£781.23

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decision to invest in deposits does not
appear to take account of future return
expectations or level of perceived risk.

DISCUSSION

Investment expertise

Expertise is important for consumer
understanding of risk and the link between
risk and return. Direct customers had
significantly higher expertise scores than
other customers. This may be expected as
direct customers have an active
involvement with investments and buy
direct rather than using an IFA. In
contrast, most other customers are
introduced by an IFA and therefore seek
external advice on investment decisions.

Improving investment expertise,

measured by knowledge and experience,
should enable consumers to make better
investment decisions. Expert consumers
more accurately assess risk and have a
better understanding of risk/return
relationships. Any initiatives to improve

consumer knowledge, understanding and
expertise should be welcomed. This
research highlights the importance of the
FSA’s consumer education remit.

Risk model

The research results generally support the
relationships identified in the model of
determinants of investment decisions
presented in Figure 1.

The portfolio holdings of respondents

support the link between a positive
outcome history leading to a higher risk
propensity. Premier customers, however,
who could be considered as having had an
unsuccessful risk-taking experience, have a
higher propensity to invest in a similarly
risky product than other customer groups.
The returns under Premier policies were
below less risky products such as deposits
but were above higher risk products such
as equities. These customers could consider
the experience to have been successful, but
this is unlikely.

Both literature review and research

Outcome History

Problem Framing

Risk

Propensity

Risk

Perception

How much to

invest

Return

Expectations

Experts

+

+

+

+

+

Past Performance

Information

+

+

+

–

–

+

+

Figure 4

Updated model of the determinants of investment decisions

How do consumers evaluate risk in financial products?

Henry Stewart Publications 1363–0539 (2005)

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31

findings indicate the central role that risk
perceptions play in financial decisions. This
is important because perceptions can be
influenced by how information is
presented. As expected, risk propensity
and risk perception were negatively
correlated with each other for both simple
decisions and also more complex
investment allocation. However, deposit
accounts were selected for investment
irrespective of how risky a respondent
considered them to be. This suggests that
this asset class is seen as a suitable
investment by all investors, at least for
part of their portfolio.

Return expectations were positively

correlated with investment allocation and
this relationship could be added to the
model. A revised model (Figure 4) is
proposed that takes account of this result
and the lack of correlation between risk and
return expectations for novices. The
findings confirm outcome history as a
predictor variable and expected return as an
additional mediating variable, which in turn
is mediated by risk perception for experts.

Limitations

The research sample may not be
representative of investment customers in
general because Pinnacle offers a limited
investment product range. These are low
to medium risk products, so the sample

may be biased towards low risk investors.
The customer age profile is skewed
towards older ages, which is unlikely to be
representative of the age profile of
investors in general.

CONCLUSIONS

The research has shown that the more
expertise a consumer has, the more
accurately they perceive risk and the better
they understand the link between risk and
return. Expertise appears to be important
in consumers’ understanding of the
products they have purchased.

Trends in the UK, such as the move of

pension schemes from defined benefit to
defined contribution, mean that
increasingly financial decisions will be
placed in the hands of consumers who are
likely to be inexperienced and novice
investors. Consumers need financial
knowledge and experience to be properly
equipped to make these types of financial
decision. This means that financial
education will become even more
important in the future. The FSA, the
media and product providers all
potentially have roles to play. The
possibilities of advisers mis-selling or
consumers mis-buying products should
be reduced if consumers are better
informed and understand the risks they
are taking on.

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APPENDIX 1: SELECTED QUESTIONS

Q3 Please indicate the extent to which you agree with the following statements

about your knowledge and experience with investments:

Strongly

Agree Disagree Strongly Don’t

agree

disagree

know

I know a lot about investing money
I am very experienced in investing

Q5 Which of the following types of investment products do you currently hold

or have held in the past?

Deposit account

National Savings

Government bonds (gilts)

PEP

Guaranteed insurance bond GIB (Life
Insurance bond)

Property (excluding your own house)

ISA — mini cash

Stocks and shares

ISA — mini stocks and shares

Structured product (no risk to capital) *

ISA — mini insurance

Structured product (capital at risk) **

ISA — maxi stocks and shares

TESSA

Investment trusts

Unit trusts
With profits bond

Questions 6 and 7 used the same statements but required a different choice of responses.
The statements were:

Betting a day’s income at the horse races
Investing 10 per cent of your annual income in a moderate growth unit trust
Lending a friend an amount equivalent to one month’s income
Investing 5 per cent of your income in a very speculative stock
Betting a day’s income on the outcome of a sporting event (eg football match)
Investing 5 per cent of your annual income in a building society deposit
Investing 10 per cent of your annual income in government bonds (gilts)
Gambling a week’s income at a casino
Taking a job where you get paid exclusively on a commission basis

Q6 For each of the following statements, please indicate your likelihood of

engaging in each activity.

Responses: Very unlikely, unlikely, likely, very likely

Q7 For each of the following statements, please indicate your initial reaction of

how risky each situation is.

Responses: Not at all risky, a bit risky, moderately risky, extremely risky

Q8 For each type of investment indicated, please answer the following questions

assuming that you have invested in it.

How do consumers evaluate risk in financial products?

Henry Stewart Publications 1363–0539 (2005)

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(Question required responses for each of four products, which were Deposits, Property,
UK Stocks & shares, Structured capital at risk product)

a) This investment bears a high risk of losing money
b) This investment bears a high risk of missing personal investment objectives
c) I feel uncertain about investing in this investment as I feel uninformed about it
d) Investing in this investment also entails good chances to realise higher, above average

returns

e) I think there will be significant variations in performance over time
f) Investors who experience losses with this type of investment are very likely to lose

most or all of their investment

g) I would worry very much if I had invested in this type of investment
h) This type of investment is very well regulated

Responses: Strongly agree, Agree, Disagree, Strongly disagree, Don’t know

Q9 For each of the investment types in Q8, please estimate the amount you

would expect back at the end of the next five years assuming you have
£10,000 to invest.

(Question required responses for each of four products, which were Deposits, Property,
UK Stocks & shares, Structured capital at risk product)

(Version 2 of the Questionnaire included the following note:
Note: Over the last five years, if you had invested £10,000 you would have got back on
average £11,716 from a deposit account, £19,918 from property, £8,528 from UK
stocks & shares and £10,420 from a structured capital at risk bond.)

Responses:
Less than £6,000

£6,000 to £7,749

£7,750 to £8,799

£8,800 to £9,999

£10,000 to £11,500

£11,501 to £12,750

£12,751 to £16,000 More than £16,000

Q10 Please allocate an amount of £10,000 between the following investment

types according to your investment preferences:

Investment type

Investment amount

Deposit account
Property
UK Stocks & shares
Structured capital at risk bond
Total

£10,000

Q12 Please indicate your age group
18–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80 and over

Q13 Please indicate your gender:

Male

Female

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APPENDIX 2: INVESTMENT DEFINITIONS

Deposit account:

A bank or building society account that allows you to withdraw

your money without penalty at any time.

Guaranteed insurance bond:

An insurance policy that pays a fixed income each year

and returns your investment at the end of a fixed term. Monthly income, annual income
or growth options are usually available.

Property:

A direct investment in property, which excludes your own house but

includes second homes, buy to let property and commercial property.

Stocks and shares (Equities):

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and Spencers, BT etc, also known as equities.

Structured product:

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Structured capital at risk product (Scarp):

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# Kathleen Byrne

How do consumers evaluate risk in financial products?

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