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					   Risk and Decision Making by Finance Executives

Author:           Les Coleman

Mailing Address: Department of Finance

                  The University of Melbourne

                  Parkville. Victoria. 3010



phone:            +61413 901085/+613 8344 3696

fax:              +613 8344 6914


             Electronic copy available at:

Purpose – When finance managers face decisions, they do not always make clinical

evaluations using rational methodology, but systematically depart from utility

maximisation. This article addresses biases that are related to risk propensity, and

categorises them under five headings: decision makers’ characteristics and

perception; reference levels; mental accounting and the assumption of mean

reversion; the longshot bias or overconfidence; and the desire for immediate

gratification. The research reported in the paper seeks to understand the

mechanisms of these biases using a study of decision making by Australian finance

executives in a setting that is representative of a typical business decision.

Design/methodology/approach - This paper uses a case study that was designed

to identify why decision makers facing choices will prefer a risky alternative. Data

were collected using e-mail contact and an electronic survey. Respondents (n=67)

provided demographic data, and answered questions that probed their attitudes and

decision styles. Risk propensity was quantified by respondents’ attitude towards a

risky decision, and was explained using independent variables related to decision

maker traits.

Findings – Just over half the executives proved willing to take a risk, and almost half

the variance in their risk propensity was explained roughly equally by respondents’:

endowment, perception of risk’s role in decisions, assessment of alternative choices,

and expectation of the decision’s outcome. Manipulation of the cases along four

dimensions varied the decision’s facts, but they proved only marginally significant to

risk taking.


                Electronic copy available at:
Originality/value - The study provides a practical explanation of the risk taking

behaviour of finance executives; confirms that context is more important to decisions

than their content; and adds to the growing body of applied behavioural research in


Keywords: decision, risk, manager decision making, applied behavioural finance

       Risk and Decision Making by Finance Executives♣

Qualitative aspects of decision making in finance – which form part of what is now

termed applied behavioural finance - have long been seen as important to the actions

of institutions and individual investors. For instance, Slovic (1972: 779) wrote: “Many

aspects of investment analysis are said to be psychological in nature”, and then

provided a catalogue of biases that remains a good description of the field. Evidence

has continued to grow that financial decision makers (both managers and investors)

do not make clinical calculations using rational methodology, but systematically

depart from utility maximisation.

Even though it seems intuitively important to understand the processes followed by

finance executives when they make decisions in the face of risk, the subject has

seen limited real-world examination. Financial economists have largely modelled

asset pricing without examining how investors make their choices (Barberis and

Thaler, 2003), and this is reflected in finance education. For example, Damodaran

(2001: vii) began his authoritative text on corporate finance by defining the subject

as: “encompass[ing] all of a firm’s decisions that have financial implications.” But, in

keeping with most other treatments, he only provided a normative depiction of how

financial decisions should be made.

There is, however, a growing body of descriptive material. Corporate behavioural

finance (Baker, Ruback and Wurgler, 2004) has developed studies covering topics

ranging from bank lending practices (McNamara and Bromiley, 1997) to decisions by

institutional investors (Shapira and Venezia, 2001) and equity and bond traders

(Willman, Fenton-O'Creevy, Nicholson and Soane, 2002).

Other researchers have sought more specific explanations for managerial decision

making. Statman and Sepe (1989) analysed the announcement effect of termination

of poorly performing investments and found (in conformance with the disposition

effect which is discussed below) that returns rose because shareholders rewarded

managers for decisive behaviour. Lintner (1956) showed that managers’ decisions to

change dividends are related to historical payments (typically targeting a steady

increase) and signal permanent changes in earnings; managers’ dividend strategy

has changed little over the years (Benartzi, Michaely and Thaler, 1997). Grable

(2000) evaluated risk taking in personal financial decisions using university faculty

and students.

Numerous authors have post-audited key strategic decisions – particularly mergers

and acquisitions – and generally concluded that only a small proportion are

financially successful (Jarrell, Brickley and Netter, 1988; and Rau and Vermaelen,

1998). Other studies have found that managers are prone to use rules-of-thumb or

heuristics such as targets for capital structure and payback period for investment

hurdles (Graham and Harvey, 2001). They also suffer from a herd mentality that

leads to waves of similar decisions such as IPOs, mergers and investment bubbles

(Loughran, Ritter and Rydqvist, 1994).

Despite this considerable body of work, few studies have used subjects who are

experienced managers; and most of the analyses have employed univariate test

statistics to examine the significance for risk propensity of individual aspects of

decision makers and decisions. Thus there remain three significant research gaps.

First relatively little is known about how finance managers actually reach decisions,

particularly what decision stimuli they place most weight upon. The second gap is a

limited understanding of how personal attributes and cognitive processes lead to the

heuristics and biases that managers seem to employ. As a resuly surprisingly little

attempt has been made to explain managerial risk taking. The third gap is that no

integrated model has been developed that shows the interaction and relative

importance of the many known influences on risk and decision making.

This paper makes two contributions to the literature. The first is to examine the

decision processes of practising finance managers using a representative decision.

The second is to incorporate a respondent questionnaire with strong grounding in the

behavioural literature and sufficient granularity to provide reliable measures of

respondents’ characteristics and decision modes. This specifically incorporates risk

propensity – not solely job-related competencies – as a key feature of decision

making; and enables development of an integrated model of decision making by

finance managers when facing risk. Results will add to the growing body of work in

behavioural finance that seeks to understand the sources and effects of managers’

decision biases.

The balance of the paper proceeds as follows: the first section provides a review of

literature on decision making by managers who are facing risk; the second section

describes the objectives and design of the empirical research; section three

discusses the results, whilst section four addresses their implications for researchers

and practitioners.

                     I. Review of Financial Decision Making Under Risk

There is now an extensive literature on behavioural finance, with surveys provided by

Barberis and Thaler (2003), Camerer (1995), Rabin (1996), and Ricciardi (2004).

Shefrin (2001) has collated some of the most influential papers. Although much of

this field examines biases at the market level, there is a growing body of applied,

micro-level work that examines financial decisions by managers, including studies by

Baker, Ruback and Wurgler (2004) and de Bondt and Thaler (1994).

It has long been recognised that managers adversely impact firm performance

because they do not unconditionally seek to maximise shareholder value. Although

this has identified a large number of sub-optimising behaviours, most attention has

been paid to mathematically tractable factors, particularly managers’ conflicts in their

role as agents (Jensen and Meckling, 1976). Scholars have largely ignored the

salience of recognised, but more qualitative, biases in managers’ decision making

such as hubris (Roll, 1986), overconfidence (Kahneman and Lovallo, 1993) and firm

outlook (Singh, 1986). The contention of this paper is that the latter attributes are of

considerable importance to maximisation of value, and hence merit more detailed

understanding so that they can be actively managed.

The goal of this section is to summarise the literature in finance (and, where

appropriate, related disciplines) that relates to the real world behaviour of finance

managers who face risks.

Given the many behavioural anomalies that are known to affect managerial risk

propensity, it is desirable to provide some structure. In this section, the anomalies are

grouped under five headings: decision makers’ characteristics (especially

demography and personality) and perception; reference levels; mental accounting

and the assumption of mean reversion; the longshot bias or overconfidence; and the

desire for immediate gratification. This approach is consistent with recent studies

such as Kahneman (2003) that seek to understand the mechanisms of biases, rather

than just catalogue them.

1.1 Decision Maker Characteristics and Perception

A variety of studies of real-world decision making have shown that up to a quarter of

the variation in individuals’ risk taking is explained by personality factors. According

to Trimpop (1994), risk takers are psychologically flexible. They are usually better

educated, have a history of successful risk taking, tolerate ambiguity, seek novel

experiences, and rapidly respond to stimuli. They are described as adaptable,

adventurous, aggressive, informal, optimistic and sociable. Risk takers have a need

to be better and faster, and agree with statements such as `I set difficult goals for

myself which I attempt to reach’; they are typical Type-A personalities (see also:

Smith and Friedland, 1998 and Williams and Narendran, 1999).

Also important to risk propensity are a variety of relatively stable personal traits.

Byrnes, Miller and Schafer (1999) conducted a meta-analysis of 150 studies to

compare male and female risk taking and found that the proportion of women

accepting any risk is an average of six percent less than the proportion of men

offered the same risky choice. The general consensus from other studies is that

increasing risk propensity is linked to higher income and education (Hartog, Ferrer-i-

Carbonell and Jonker, 2000). Risk takers are likely to be younger, single, and in a

professional occupation (Grable, 2000); they also exhibit multiple risk behaviours and

so are more likely to pursue dangerous occupations and sports, and adopt risky

personal habits such as smoking, reckless driving, and sexual activity (Zuckerman

and Kuhlman, 2000).

An explanation for the mechanism whereby personality determines risk propensity is

the proposal by Lopes (1987: 275-6) that

  “Risk-averse people appear to be motivated by a desire for security, whereas risk-

  seeking people appear to be motivated by a desire for potential. The former motive

  values safety and the latter, opportunity … Risk-averse people look more at the

  downside and risk seekers more at the upside.”

Significantly, this mechanism is consistent with physiological evidence that is

obtained from magnetic resonance imaging (MRI) studies which use radiation to

examine humans at the molecular level. These found that different parts of the brain

– and hence different decision criteria (e.g. emotion or logic) – are used when

decision features vary (Greene, Sommerville, Nystrom, Darley and Cohen, 2001).

Other studies detected a “switch” in the brain that alternates decision maker

perspective between the optimistic, big picture and a cautious, detail focus (Miller,

Liu, Ngo, Hooper, Riek, Carson and Pettigrew, 2000).

The perception of decisions is important. Framing, for instance, involves presenting

identical data with a different emphasis, which shifts a decision maker’s expectation

of the outcome. Thus a positive frame (i.e. projecting gains) induces greater

weighting for a successful outcome (Kühberger, 1998). Decision makers also place

their own frame on a decision by editing the materials to be analysed through

instinctive perception of the costs and benefits of each outcome (Finucane,

Alhakami, Slovic and Johnson, 2000), and by self framing (Wang, 2004).

Another subjective influence is perceived control: people are more willing to take a

risk when they have a competence through relevant skill or knowledge (Heath and

Tversky, 1991), or when they are able to exercise some control over the outcome

(Slovic, 2000). Although most analyses of financial expertise conclude that its value

is marginal at best (De Bondt, 1991), support from experts adds to perceived control

and is especially valuable to risk-neutral decision makers (Eeckhoudt and Godfroid,


A well recognised influence on decision making is bounding (Simon, 1959), where

individuals are simply unable to access or process all available information.

Economically, this implies that the costs of gathering additional knowledge about a

decision are not expected to provide a reasonable return (Conlisk, 1996). Decision

makers become more risk averse when limits are placed on them (Mano, 1990);

similarly they do not like ambiguity (Ellsberg, 1961), and so will avoid outcomes with

unknown probabilities.

1.2 Reference Levels

As discussed above, decision makers’ personality, training and experience incline

them to be instinctively risk averse or risk seeking. This section introduces more

transient influences which alter decision makers’ perspective in light of needs and

decision setting (e.g. recreation or retirement savings). This second group of decision

influences relate to changes in risk propensity around a reference level.

A useful model is risk-sensitive foraging that is used to explain risk taking by animals,

and assumes they choose foraging strategies in light of food needs (Bateson, 2002).

An excellent example was provided in a breakthrough experiment where researchers

established birds in a laboratory habitat, and taught them to feed by pecking on keys

that delivered either a fixed number of pellets, or a variable quantity (Caraco,

Blanckenhorn, Gregory, Newman, Recer and Zwicker, 1990). The experimenters

then threatened the birds’ survival by reducing the laboratory’s temperature. The

birds proved to be risk sensitive as their state changed: risk averse when food was

adequate and temperatures warm, and risk embracing as the temperature dropped

and food supplies became critical.

The essence of risk-sensitive foraging is that decision makers alter risk preference

around their satisficing level, or endowment which meets requirements at the time.

This means that - when endowment is inadequate to sustain the decision maker - risk

propensity is high; however, when endowment is adequate and survival is probable,

a lower risk strategy is preferred.

Despite its intuitive appeal, risk-sensitive foraging has only seen limited explicit

application to human behaviours; one of the few examples is the explanation of

political lobbyists’ strategies by Gray and Lowery (1998). However, there is

considerable evidence that the mechanism is relevant to humans and organisations

who – like animals - will take greater risks when their survival is threatened. For

instance, Prospect Theory proposes domain-sensitive risk propensity so that decision

makers are risk averse above an endowment reference point and risk embracing

below that point (Kahneman and Tversky, 1979). An example of this in practice was

provided by Singh (1986) who obtained responses to a questionnaire and publicly

available data for 64 medium to large North American firms; he concluded they have

a satisficing level of performance, and increase risk when results are below that level.

An important element of risk-sensitivity is loss aversion. Because losses can have

serious consequences, any loss is valued more than an equal gain, and the utility of

any loss relative to the same gain increases with the size of the loss. In other words,

the pain of loss relative to an equivalent gain is directly proportional to the amount,

and inversely proportional to risk propensity. This is most obvious in laboratory

studies where decision makers are offered a variety of choices (Schneider and

Lopes, 1986): as the size of the potential loss increases, people increasingly prefer a

low risk outcome.

Taken together, these behaviours evidencing risk sensitivity challenge the

assumption in finance theories of constant risk aversion (Parrino, Poteshman and

Weisbach, 2005).

Reference levels can also serve as an anchor. For instance Lovallo and Kahneman

(2003) point out that executives typically begin a decision with a forecast outcome,

often prepared by a sponsor of the proposal. Whilst decision makers will adjust their

expectations, this is generally not enough. So the reference anchor provides an

overly optimistic assessment of the outcome. This is consistent with studies of

decision makers’ forecasts which show that their `error bars’ are far too narrow: thus

actual outcomes frequently fall outside the range of possibilities, even when experts

are involved (Camerer, 1995).

1.3 Mental Accounting and Mean Reversion

The third group of influences on decision makers comprise mental accounting and

the assumption of mean reversion.

Thaler (1985) developed the concept of mental accounting in which decision makers

apportion their wealth, knowledge and other resources into discrete and non-fungible

mental accounts. In economic terms this leads consumers to over-weight sunk costs

and current, cash outlays. But it also leads to a number of behavioural anomalies.

For instance, recent experience becomes important because it provides a

personalised sampling of outcomes and changes endowment relative to the

satisficing level: thus sequential outcomes have synergetic impacts on risk propensity

and hence on decisions.

One particularly relevant feature of mental accounting is what Tversky and

Kahneman (1971) called the law of small numbers. Because it is too demanding to

collect and process a statistically robust sample, decision makers overgeneralise

from small samples and tend to overweight personal experience and striking

observations (e.g. crises, highly publicised incidents, freakish calamities). Thus

recent experience carries more weight than population-based distributions, and –

because of self-framing - is especially likely to be over-weighted when it supports a

preferred outcome (Zackay, 1984).

The net result is that people place more emphasis on the consequences of decision

outcomes than on their probabilities. Thus decisions involving risk turn on

expectations of how alternative outcomes will impact endowment, rather than on

probabilities of the outcomes. This explains why a number of studies (Forlani, 2002

and Mullins et al, 1999) find that the facts of a decision are frequently ignored.

Another important decision influence is the assumption by decision makers that

mean reversion will apply unless there is reason to believe otherwise (Heath,

Huddart and Lang, 1999). An obvious example is the disposition effect, or tendency

for investors to sell assets that have risen in value in preference to those that have

made a loss (Odean, 1998); further support comes from evidence of the gamblers’

fallacy, which assumes that the recent occurrence of an outcome (e.g. win by red,

heads or favourite) lowers the probability of re-occurrence in an identical, statistically

independent event (Morrison and Ordeshook, 1975). This assumption of mean

reversion exerts Bayesian influences so that successful decision makers expect a

run of wins to be followed by losses, and – in the absence of overconfidence - will

tend to become less risk prone; whilst unsuccessful decision makers expect a turn for

the better and can become more risk prone.

1.4 Longshot Bias in Decision Makers

Overconfidence in managers is part of a pattern which psychologists term self-

enhancing biases. According to Rabin (1996: 50):

   “We are over-optimistic regarding our health and other aspects of our life; we feel

   we are less vulnerable to risk than others; and we are more responsible for our

   successes than we are for our failures. We think that we are superior to others in

   all sorts of ways: we are better at controlling risk, better drivers, and more ethical.”

There is much evidence of overconfident behaviour by finance managers who prefer

low probability outcomes that cannot be justified by their statistical record. For

instance, a variety of studies have shown that the average failure rate for common

business strategies lies in the range between 70 and 90 percent. Examples include

acquisitions (Rau and Vermaelen, 1998), research and development projects

(Palmer and Wiseman, 1999), company formation (Camerer and Lovallo, 1999),

mineral exploration (Mackenzie and Doggett, 1992) and new product launches

(Roskelly, 2002). According to one study of managers in US corporations, at least

half of all their decisions fail (Nutt, 1999).

One of the best-known depictions of the longshot bias relates to acquisitions where

Roll (1986) argued that managers of acquiring firms are over-optimistic in the

valuation of targets and over-confident in their ability to monetise potential merger

synergies. Selective analysis that induces a level of overconfidence bordering on

hubris explains why firms overpay for acquisitions and – through the winner’s curse –

suffer poor returns.

1.5 Desire for Immediate Gratification

The final striking feature of risk taking behaviour is the preference of managers for

immediate gratification (Angeletos, Laibson, Repetto, Tobacman and Weinberg,

2001). Although finance assumes that elapsed time is the only factor separating the

same decision now and in the future, actual behaviour reflects a bias towards

immediately achieving a desirable outcome. This effectively underweights risk

probabilities and accepts high opportunity costs. It is equivalent to use of a higher

discount rate for costs than benefits, which is consistent with behavioural evidence

(Sagristano, Trope and Liberman, 2002). This leads to a preference by managers for

investments with high early payouts, and a delay in cost-saving projects (present

value of costs saved will be reduced relative to present value of opportunity cost)

including risk management (which avoids costs rather than producing an incremental


                       II. The Model and Research Methodology

This section discusses the objectives and methodology used to research the how of

risk and decision making by finance executives, using a descriptive model built up

from the literature survey above.

2.1 Decision Model

The behavioural and managerial evidence in section I shows that individual risk

taking responds to a combination of personal, environmental, situational and

definitional aspects of the decision. These can be described by five groups of stimuli

that could be expected to drive the decision of a finance executive who faces a risky


Three of the stimuli are relatively obvious empirical parameters that can be reliably

measured: the facts or features of the decision; the population of decision events

which is made up of the previous outcomes of similar decisions; and experts’

opinions of the likely outcome of this event.

A fourth – and more qualitative – group of decision stimuli can be thought of as the

decision maker’s paradigm which comprises a pattern of personal features that are

relatively stable across different decision types. These include competencies,

personal attributes (especially demography and personality), endowment, experience

in previous decision making, and future aspirations.

The fifth group, which is more transient, comprise the decision maker’s perception

(how the decision is self-framed), its institutional setting, and the relevance of the

decision maker’s skill which indicates how much control they have over the outcome.

These various factors can be distilled into a decision model as shown in figure 1. The

principal objective of this study is to validate this depiction of managerial decision


                                  [Insert Figure 1 Here]

2.2 Survey Materials

The research here is intensive in the form of a survey involving senior finance

executives as this gives sufficient granularity in responses to critically examine the

influences on decision making. The Survey is of the in-basket style Gill (1979), and

takes the form of a case study involving a decision with two alternative choices, one

of them risky and the other relatively safe. Participants are asked to indicate which

alternative they would recommend and provide personal details by answering a

variety of questions on their demography and personality.

The subject of the case study is a Grand Prix racing team, Carter Racing, and it is

loosely modelled on events leading up to the 1986 Challenger space shuttle disaster.

The case was designed to evaluate the reasons why experienced decision makers

select a risky alternative, and is commonly used to illustrate managerial decision

making (Sitkin and Weingart, 1995).

Original material was sourced from the copyright holders of the case study. This was

shortened to one page, and formatted as a memo seeking a typical business

decision. The memo described the experience of Carter Racing, which had

developed a unique engine design and was in its first year of Grand Prix racing. The

engine was prone to fail at high cost, but Carter’s initial success had attracted

attention, and the team needed to perform well in its next race. Respondents were

given details of Carter’s recent performance and some technical information, and

asked if they would recommend that the team should race or not race in the next

event. In keeping with the business style, no irrelevant information was included.

Using an approach that proved successful on several occasions (e.g. Forlani, 2002;

Mullins et al, 1999; and Sitkin and Weingart, 1995), the case was internally

manipulated to provide varying levels of risk by incorporating opposing values to four

facts that intuitively seem essential to the decision: finishing position in the last ten

races (either one or five top ten finishes); number of blown engines in the last ten

races (one or five); expert opinion on the cause of engine failure (ambient

temperature is, or is not, the cause of engine failure); and the anticipated

consequences of a wrong decision (almost certain bankruptcy, or some financial

pressures). Thus the case study came in 16 versions, and subjects were randomly

assigned to one.

The questionnaire had three parts. The first obtained the dependent variable through

the critical question: `If you were the owner of Carter Racing, what is the probability

that you would decide to race tomorrow?’. The second section contained 18

questions designed to elucidate reasons for the choice, which incorporated tests of

framing, endowment, expert opinion and outcome expectations.

The third section contained 51 questions. The first 15 measured personal

competencies including education, income and decision experience (years in

workforce, industry, type of job); and demographic features including age, gender,

marital status, and nationality. Other questions related to personality: locus of control

(by powerful others, internal control and chance) (Levenson, 1974); tolerance of

ambiguity (Budner, 1962); sensation seeking (seeking novel experiences and willing

to take risks to have them), impulsivity (rapidly responds to cues; not inhibited from

risk taking), aggression and sociability (Zuckerman and Kuhlman, 2000); egalitarian

preferences; extraversion, emotional stability, and conformity to social norms

(Robinson and Shaver, 1973); Type A personality (Williams and Narendran, 1999);

autonomy orientation, flexibility and competence; anxiety and susceptibility to

boredom; need for tension, risk and adventure (Keinan, 1984); lack of inhibition,

feelings of self-efficacy, and self-discipline; worldviews (hierarchic, egalitarian,

individualist, and fatalist) (Slovic, 2000); achievement motivation (McClelland, 1961);

and personal traits (Goldberg, 1990).

To ensure validity and reliability of the questions, the majority were drawn from

studies that had been previously published in peer-reviewed journals. Responses

were measured on a five-point Likert scale where 1=strongly agree and 5=strongly

disagree. Whilst altering the setting of questions (i.e. their frame) can elicit different

responses, the strategy of using a previously published case study and questions

was designed to facilitate validation of resultsi. The questionnaire was available on a

research website, and testing showed that it took an average of 20 minutes to


2.3 Survey Participants

Participants came from the Finance and Treasury Association, which is a

professional body whose members are active in corporate finance and typically

employed by banks and major companies; it publishes a directory of members’

names, positions and e-mail addresses. A total of 530 potential finance executives

were contacted to participate, with a follow up after two weeks.

Given that target respondents were practicing managers, e-mail was used to solicit

responses as it is a common medium of business-to-business contact, and a

common mode for decision making; it was not expected to introduce a bias in the

sample. The e-mail provided a hot link to the questionnaire website. The advantages

of this approach are that it is cheap, typically generates quick responses, and is

perceived as environmentally friendly. As responses come electronically, they can be

accurately compiled and are obtained anonymously which promotes completion of

sensitive questionsii. E-mail also avoids the shortcomings of personal interviews,

where bias can be induced by interviewer guidance (e.g. in cognitive processes) or

propensity to agree with questions (Dillman, Phelps, Tortora, Swift, Kohrell and

Berck, 2001). Conversely, e-mail surveys can produce response rates that are low, or

vary widely for no apparent reason (Smee and Brennan, 2000).

2.4 Limitations of Methodology

Critical assumptions underpinning this research are that: responses to a hypothetical

decision collected by an on-line survey can be generalised to actual finance decision

making; responses are representative of the decision making processes of Australian

finance executives in general; parameters can be quantified without error; and the

absence of real risks does not diminish the validity of the results. Other limitations are

the size of the sample and a practical limit to the number of questions, which

inevitably restrict the extent of research.

As questionnaires can do no more than recognise patterns in subjects’ reports, they

rely upon the goodwill and accuracy of participants. In a sensitive area such as risk

and decisions, there is no guarantee that responses will reflect true preferences.

Another deficiency of intensive techniques is possible contamination of the results

through what has been called the Hawthorne Effect since experiments by Mayo

(1933) suggested that simply observing behaviour can change itiii. Such concerns are

further complicated by the ethical research requirement for informed consent: even

describing the research proposal can frame responses.

Despite these limitations, the research strategy has important strengths, especially:

strong grounding in the literature, including empirical studies; linkage between

questions to ensure internal consistency of findings; emphasis on the real world so

that decision makers are operating in a familiar environment; and use of

heterogeneous samples of experienced decision makers. This should test the

research questions in a more realistic environment than using students in a

laboratory which is the approach of most decision studies.

                                   III. Survey Results

This section reports the results from analysis of the survey responses.

3.1 Responses and Respondents

The summary of respondents’ demographic traits provided in table 1 shows they are

not a random sample of the population as they are: predominantly male (84 percent),

tertiary qualified (100 percent), in professional or executive roles (96 percent), with

considerable work experience (almost 70 percent have 16 or more years in

employment) and relatively high incomes (59 percent earn over $100,000 per year).

Conversely the sample group provides a good spread of ages and industry, and

appears a broad cross-section of decision making finance executives.

                                  [Insert Table 1 Here]

There were a total of 67 useable responsesiv, which gives a response rate of 12.5

percent. Although this is expected for an unsolicited e-mail survey (Smee and

Brennan, 2000), it is modest and makes it desirable to validate the

representativeness of respondents. The approach suggested by West and Berthon

(1997) was employed and uses a two-tailed t-test to compare the first and last

quarters of responses. The methodological assumption is that late respondents are

closer in sentiment to the non-responding pool than are the early respondents. The

last quarter of respondents proved slightly less likely to race than the early

respondents (55 percent probability of racing versus 61 percent, respectively), but

the variation is not significant (p>0.6; see panel A of table 2). This gives confidence

that the survey data are not biased.

                                  [Insert Table 2 Here]

3.2 Results

The cases were designed to be `risk-neutral’ on average, and 56.7 percent of

respondents chose the risky alternative of racing. Assuming a binomial distribution of

race-don’t race responses, the overall result is not significantly different (p>0.27) to

that expected from a randomly chosen, risk-neutral samplev. Thus these executives

are risk neutral or slightly risk-prone on average.

A simple univariate analytical strategy was chosen to identify which effects on

decision making were most significant. Only respondents who gave a clear decision

(i.e. percentage probability of racing >60 or <40) were included, and this reduced the

sample size to 58. For each of the 69 questions, responses were divided into two

groups, one of which strongly agreed or agreed with the statement and the other

which disagreed or strongly disagreed. The sample means of each of the 69 paired

groups were compared, and significant differences reported in table 2.

Panel B examines risk propensity in light of the four manipulated facts of the case,

and shows that only two proved significant (p<0.10). The first was reliance on

experts: respondents who were told that experts knew the cause of engine failures

were considerably more likely to take the risk and race than those who were told

experts did not know the cause (61.4 and 48.6 percent, respectively). Thus support

from experts strengthens risk propensity.

The second significant fact related to the outcome from failure. Those who were told

that failure after taking the risk would result in bankruptcy were far less likely to race

than those who expected the worst case outcome was only some financial distress

(49.6 and 62.2 percent, respectively). Thus respondents proved loss averse.

The next step was to identify influences on risk propensity from respondents’

perception of the decision and their personal traits. Those that proved statistically

significant (p<0.10) are reported in panels C and D of table 2.

Results in panel C showed statistically significant support for a longshot bias as the

probability of racing was higher for respondents who foresaw large potential for gain

and for those who believed that racing was too good an opportunity to pass up. The

results also suggest that respondents self-framed the decision so that – even though

virtually all saw it as a risky activity – those who took the risk considered Carter’s risk

was low and was likely to prove successful.

Panel D shows that respondents who take a risk are younger, more controlled and

Type-A personalities, which confirms risk prone traits found in other studies (Grable,

2000; and Trimpop, 1994).

The table also reveals two negative findings that have significant implications for

managers’ decision making. The first is the absence of several intuitively important

factors. Gender, for instance, did not prove significant to risk propensity; nor did

perception of control, and respondents’ self-judgement of their risk propensity.

The second finding is that executives do not have a stable risk propensity that is

generalisable across different settings. Respondents were asked about their

tendency to take risks in their personal life, personal investment strategy and in

working for clients or employers. Only one of the three paired combinations was

statistically significant: the link between risk propensity in personal finances and

business. None of these self-reports of risk propensity was significantly correlated to

decisions in the case study.

As a key research objective was to identify which personality traits influence risk

taking in finance, the analysis above was extended by use of bivariate correlations

between probability of racing and self-report of 51 personal and psychological

measures. The statistically significant (p<0.05) relationships are listed in table 3, and

demonstrate strong agreement with previously published studies. Specifically, no

significant relationship found in this study contradicted the original relationship

between personal attribute and risk propensity.

                                  [Insert table 3 here]

Results in table 3 characterise risk-prone finance managers as confident and

sociable; calm and relaxed, although hard driving. They believe that risk is required

to get ahead, and that risk taking contributes to success. An interesting finding is that

risk-prone executives believe that chance plays an important role in success, which

implies that they consider outcomes of risky actions are at least partly beyond their


The next step was to more comprehensively explain respondents’ risk propensity by

using multiple regression to derive an expression that is parsimonious and logical. It

was also desirable to show the extent to which subjects’ decision making matches, or

fails to match, the processes in the proposed decision making model.

The result is shown in table 4 using a layout proposed by Hair, Anderson, Tatham,

and Black (1998: 212). This shows that the probability of racing is increased by

agreement with the following statements:

   •   `If this opportunity is passed up, there will never be another as good’

   •   `Carter Racing is likely to succeed tomorrow’

And the probability of racing is decreased

   •   in proportion to the value of investments; and

   •   by agreement with the statement: ‘Risk is higher when facing situations we do

       not understand’.

The solution identified variables which determine a person’s innate traits, propensity

to select a risky alternative when making a decision, and their decision making style.

                                  [Insert Table 4 Here]

These results can then be used to identify which of the proposed relationships (set

out in figure 1) are important. As shown in figure 2, it is possible to describe

executives who take risks along four dimensionsvi.

The first dimension is personal attributes: risk propensity is higher for those with a

lower value of investments, which is correlated to lower income, fewer years of

employment and younger age. A second dimension is decision making style which

assumes that risk is not higher when facing decisions that we do not understand.

These risk takers describe themselves as `controlled’ and consider they have

received more breaks in life than most. The third dimension is an assessment of the

situation which concludes there is no alternative to taking the risk. The final decision

dimension is a judgement that risk will bring success. These people describe

themselves as more willing to take risks than their colleagues and as `confident’.

Table 4 and figure 2 indicate that almost half the variability in risk propensity by

finance managers can be explained by four measurable parameters. However, the

most interesting aspect of this finding is that none of the quantitative measures that

were proposed in figure 1 as influences on decision making – experts’ opinion,

decision facts and features and previous outcomes – had a significant, direct

influence on the decision to take a risk or not. By contrast, the explanatory variables

were all qualitative. This is particularly important given the relative neglect of these

parameters in most previous studies.

                            IV. Discussion and Implications

This study examined the reasons why finance executives select a risky alternative

when making decisions, and did not consider the effectiveness of these decisions.

Even so, it raises four particularly significant conclusions:

   i. Just over half the executives surveyed proved willing to take a risk

   ii. Executives’ risk propensity is strongly influenced by demographic and

       personality characteristics which explain 24 percent of the variance in risk


   iii. Factual elements in the case had limited impact on the decision. The most

       important transient influences on decision making are judgements about the

       outcome, which explain a quarter of the variation in risk propensity.

   iv. Results are consistent with risk-sensitive foraging and Prospect Theory.

Consider each in turn.

The conclusion that finance executives are risk neutral or slightly risk prone on

average contradicts most finance assumptions (Bodie, Kane and Marcus, 2005: 144),

but is consistent with several previous studies. For instance, Levy and Levy (2002)

studied investment-type decisions by students, faculty and professionals and found

that between 47 and 67 percent of their subjects were risk prone. Similar results were

obtained for managers by MacCrimmon and Wehrung (1984) and Williams and

Narendran (1999).

The second important conclusion is that relatively stable, personal characteristics

explain around a quarter of the variation between executives in their risk propensity.

This closely matches published data. In this study, risk prone executives proved to be

younger, and this attribute was associated with lower income and wealth. They are

Type-A personalities (‘I regularly set deadlines for myself’); sociable, confident and

calm; and believe in the need to take risks to be successful. The decision making

style of risk takers is driven by belief that they are luckier and more capable than their


The third important conclusion relates to the how of risk taking, which is driven by

contextual and process variables that can be thought of as decision making style,

alternatives to taking a risk, and expectation about the outcome. Risk-prone

respondents do not see a viable alternative to taking a risk, and are confident of a

successful outcome. Respondents conform to the conclusion of West and Berthon

(1997: 30) that “successful risk taking individuals are likely to believe that they can

beat the odds, that nature is good to them, and that they have special abilities.”

The executives’ risk propensity was increased by expert support, which is consistent

with studies that find experts play important facilitating roles in high risk decisions

such as mergers and acquisitions (Lewis and Zalan, 2004). Executives also proved

loss averse in their decision making and less likely to take a risk when it could result

in ruin.

Findings of this study contradict the normative assumption that decisions are

determined solely by their facts. Overall, executives tend to place much less weight

on the quantitative content of a risky decision than they do on its qualitative aspects.

Using their own unique perspective of the decision context, executives make risky

choices by looking to the future, virtually independently of apparent facts. This is

consistent with the finding by Forlani (2002) and Mullins, Forlani and Walker (1999)

that risk propensity is influenced by contextual factors such as a history of successful

decision making.

Framing of the decision, especially its expected outcome, is also important for its

influence on affect. This is why managerial risk propensity can be encouraged by

rewarding individuals’ initiative and success, rather than forcing adherence to

process (Pablo, Sitkin and Jemison, 1996). Longer term, there is a suggestion that

successful experience in risky decision making and education about the process will

help modify behaviour. All this is consistent with the observation by Kahneman

(2003) that “the central characteristic of agents is … that they often act intuitively.

And the behavior of these agents is not guided by what they are able to compute, but

by what they happen to see at a given moment.”

The fourth conclusion of the study is to support risk-sensitive foraging and Prospect

Theory. Respondents with low incomes and less wealth have higher risk propensity,

and this is consistent with an inverse relationship between risk propensity and


These results have significant implications for finance management, particularly

investment decisions and agency theory.

The finding that at least half the sampled finance managers are prepared to make a

risky choice is clearly important based on the model developed by Parrino,

Poteshman and Weisbach (2005). They reached the intuitively obvious conclusion

that manager risk propensity is critical to selection of investments: managers who are

risk-averse will reject attractive, but risky, investments; whereas risk-neutral decision

makers find risky projects more attractive. Thus the assumption in many models of

myopic risk aversion will mischaracterise decisions by managers and investors.

The basis for the longshot bias in managerial decision making is apparent in the

result that variables with strongest links to risk propensity were belief that the

situation has large potential for gain and confidence in a successful outcome. This

points to one possible explanation for high risk strategies which is that managers

recognise the skewness in returns and prefer the small chance of a high payout

despite their low expected return (Golec and Tamarkin, 1998). The temptation to

gamble may be fostered by executives’ belief in their luck, and confidence that their

skill can increase the probability of success. It is encouraged, too, by experts’


The underweighting of risk probabilities is consistent with the desire for immediate

gratification. Moreover, there is strong evidence of self-framing in the high risk

propensity of respondents who foresee the potential for gain, low risk for Carter and

likelihood of success.

The study’s results also provide insights into contributors to agency problems. Much

of the variance in executives’ risk propensity arises from personal traits and

expectations about the outcome. Thus simply expanding on data and analysis will not

change decisions. Managers decide to take a risky alternative because of their innate

features, learned decision making style, and expectation of a successful outcome.

This means that a significant shift in firm risk propensity requires a change in its

managers, not just their compensation or the organisation’s structure. This explains

why so many organisations will make major changes to their senior staff, despite the

costs, disruption and loss of corporate memory.

The executives surveyed here are risk prone, although far from homogenous in their

risk propensity; and they do not rely solely on facts when taking risks. This, too, has

important implications for modern finance theory, which assumes financial decision

makers are rational, homogenous and risk averse, with diminishing marginal utility for

money. Finance executives and (if the agent-principal relationship holds true) their

firms do not follow the logical decision making process assumed in strategy

textbooks: collect the facts, weight them by probability, evaluate each outcome, and

choose the highest value adding alternative (Dearlove, 1998). Thus decision theory

needs to specifically recognise affect (with its psychological meaning as associated

feeling or emotion) and the concept of decision maker utility (Samson, 1987), even

though it has waned in popularity in the last decade.

This study’s results point to two gaps in the finance research agenda. The first is the

need to examine decisions (and, by implication, other finance practices) close to a

real-world context where subjects follow their natural decision styles, rather than

conforming to norms imposed by experimental settings. Given that risk propensity

can explain half the variance in executives’ decision making, a further area for

research is the impact of risk on firm performance. Although there has been

considerable effort using risk with its meaning of variance (Nickel and Rodriguez,

2002), less research has used risk in the way executives think of it. This is

particularly important as existing strategy paradigms have proven unable to explain

firms’ financial performance (Campbell-Hunt, 2000).


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                                                   TABLE 1
                                 Distribution of Respondents (Percent)
Gender                 Male           Female
                        84              16

Age (years)            ≤ 25            26-35               36-45       46-55      55-65
                         6              24                  33          32          5

Marital Status     Never married      Married         Separated       Divorced
                        18              76                  3            3

Education Level      Diploma         Bachelor        Postgraduate
                                      Degree              Degree
                         7              35                  58

Work Experience         <5             5-10                11-15       16-20      > 20
(years)                  3              15                  15          19         48

Industry           Manufacturing    Wholesale or     Agriculture or   Finance    Services   Government
                                    Retail Trade      Resources
                         8               7                  3           48         26           8

Occupation            Clerical      Professional      Executive       Student
                         2              57                  39           2

Income ($K PA)         < 25            25-50               50-75      75-100     100-150      > 150
                         4               2                  11          24         22           37

Investments ($K)       < 25           25-100              100-250     250-500    500-999     > $1 mill
                        17              15                  15          29         20           4

                                     Table 2: Analysis of Decisions to Take a Risk

This table compares the characteristics of finance executives by univariate analyses that test for differences between the
mean risk propensity (measured here as the probability of taking the risk to race) of different samples of respondents.
In each panel, t-tests were used to compare the sample means. Let the respective means of two samples of size n1 and n2
be mean1 and mean2, with standard deviations stdev1 and stdev2. In comparing the means, t> 1.28 for p<0.10, t> 1.67 for
p<0.05, and t> 2.39 for p<0.01, where t = (mean1 - mean2)/√[(stdev12/n1)+(stdev22/n2)]. Significances at the ten, five and
one percent levels are denoted by *, ** and ***, respectively.
Panel A compares the first and last quarter of responses that were received. This assumes that late respondents are close
to non-respondents and so tests for biases from non responses. The t-statistic for the difference between the means of the
two samples is: (0.613-0.550)/√(0.3072/16+0.4162/16) = 0.487; which – using a two-tailed test- has a significance of 0.625.
Panel B reports results that partition respondents according to facts presented to them. Four facts were varied between
cases to give 16 versions, and differences in risk propensity for each are reported here.
Results in panel C classify respondents by their perceptions of the decision and its frame, whilst panel D classifies
respondents by their demographic or personality traits. Only statistically significant (p<0.10) relationships are reported in
panels C and D.

                                                                 TABLE 21
                                 Probability of Racing (i.e. Risk Propensity) by Decision Maker Feature
                   INDEPENDENT VARIABLE                                       Sample 1                  Sample 2
                      Sample 1 and Sample 2                          Mean       Std dev     n      Mean   Std dev   n
Panel A: Test of Respondent Bias
First and last quarters of responses                                  61.3       30.7      16      55.0    41.6     16    0.5
Panel B: Internally manipulated facts of the case
Recent Results: zero or five top five finishes in last ten races      50.4       38.0      24      60.9    34.8     34    -1.1
Recent Vehicle Performance: one or five blown engines in last
                                                                      58.6       35.4      28      54.7    37.5     30    0.4
ten races
Experts’ Opinion: knows cause and does not know                       61.4       35.8      36      48.6    36.3     22    1.3
Outcome of Racing: almost certain bankruptcy or some
                                                                      49.6       38.5      26      62.2    33.8     32    -1.3
financial pressures
Panel C: Respondent Perception of Decision (Strongly Agree&Agree versus Strongly Disagree&Disagree)
If this opportunity is passed up, there will never be another as
                                                                      82.6       21.6      19      36.3    32.2     30   6.0***
The situation faced by Carter has large potential for gain            65.1       33.8      43      34.0    29.9     10   2.9***
Carter Racing can tolerate large risks                                80.6       24.8      18      35.6    35.7     25   4.9***
Carter Racing is in a positive situation                              68.8       33.6      16      47.6    37.2     29    1.9
Carter has a record of making the right decisions                     65.0       35.0      14      33.8    37.8     13   2.2**
Although Grand Prix racing is risky, Carter Racing can expect
                                                                      78.8       21.5      17      54.8    35.0     23   2.7***
safe operation
The average person would make the same decision as me                 42.5       38.1      20      82.5    21.4     12   -3.8***
Carter Racing is likely to succeed tomorrow                           79.0       26.5      20      26.7    28.9     18    5.8***
Panel D: Respondent Trait or Response (Strongly Agree&Agree versus Strongly Disagree&Disagree)
I am calm and relaxed when participating in group discussions         64.5       34.2      33      33.6    38.3     11    2.4
I regularly set deadlines for myself                                  60.4       35.3      47      25.7    30.5      7   2.8**
Age (35 or less, >35 years)                                           67.8       28.8      18      51.5    38.3     40    1.8
Income (<$100K PA, >$100K PA)                                         73.3       27.7      12      52.2    37.1     46    2.2
Investments (<$100K, >$100K)                                          70.5       28.6      20      49.2    37.9     38    2.4

                       Table 3: Personality and Risk Propensity

This table compares the influences of personality on risk propensity as identified in this
study with the results from the earlier study from which the question was sourced.
Relationships are shown where significance in this study exceeds 20 percent;
relationships that are significant at the ten and five percent levels are denoted by * and
**, respectively. Positive correlations mean that risk prone decision makers agree that
the phrase describes their behaviour or beliefs.

                                             TABLE 3
                              Personality Links to Risk Propensity
       Author                       Measure of Risk Propensity                 Sign of Relationship
                                                                               Original    Coleman
Austin, Deary and      o      Successful people take risks                        +           +
Willock (2001)
Levenson (1974)        o      Locus of control: When I get what I want, it’s      +           +
                       usually because I’m lucky
Robinson, Shaver, and o       I am calm and relaxed when participating in         +         + (**)
Wrightsman (1991)      group discussions
                       o      In general I am very confident of my ability        +         + (**)
Rohrmann (1997)        o      I’ve not much sympathy for adventurous               -           -
Williams and           o      Type A: I regularly set deadlines for myself        +         + (**)
Narendran (1999)
Zaleskiewicz (2001)    o      To achieve something in life, one has to            +          + (*)
                       take risks
Zuckerman and          o      My behaviour can be described as Sociable           +           +

                              Table 4: Mulitivariate Regression of Impacts on Risk Propensity

 This table reports the results of multivariate regression of the dependent variable Probability of Racing (which is a proxy
 for risk propensity) against 65 independent variables related to respondents’ perception of the decision and personal
 characteristics. The dependent variables were measured using Likert scales and are reported so that a positive co-
 efficient means agreement with the statement.

                                                             TABLE 4
                                         Statistics Associated with Probability of Racing

Summary of Model
R squared                   0.494                 Standard Error of Estimate        1.344
Adjusted R squared          0.462                 Observations                        67

Variables in the equation

                  Term                        Coefficient   Standard    Standardised Regression      t value     Significance
                                                              Error         Coefficient (beta)
Intercept                                       8.125        0.766                                    10.61         0.00
Value of investments, excluding                 -0.240       0.103                -0.214             -2.338         0.023
principal residence
Risk is higher when facing situations we        -0.439       0.186                -0.219             -2.367         0.021
do not understand
If this opportunity [for Carter to race] is     0.492        0.149                0.328               3.309         0.002
passed up, there will never be another
as good
Carter Racing is likely to succeed              0.687        0.175                0.387               3.926         0.000

       Figure 1 : Proposed Links between Decision Components

Decision Maker’s Paradigm                         Experts’ Opinion
      Personal attributes
 Historical decision outcomes
          Aspirations                                Decision Facts or Features

                                RISK BEHAVIOUR/

                                                     Population of Outcomes
Decision Maker’s Risk Profile                                 Results
         Risk Perception
       Institutional Setting
     Relevance of own Skill

                   Figure 2. Determinants of Risky Decision Making

 [Correlation coefficients are shown beside arrows. Positive correlations with numeric
 measures mean they increase with the other variable or risky decision; positive
 correlations with statements mean agreement with risky decisions. Overall R2 = 0.60]

     Income               0.36

   Years of               0.56

I have gotten more                     Decision Making               - 0.31
 breaks in life than       0.29               Style
most people I know                    Risk is higher when
                                      facing decisions we     - 0.24
    “Controlled”           0.19        do not understand
   describes my

                                                                              Probability of
The situation faced                                                            Racing (%)
by Carter has large        0.43
 potential for gain
                                       If this opportunity
                                          is passed up,       0.53
   describes my                       there will never be
    behaviour              0.21         another as good

Carter has a record
of making the right
     decisions                             Outlook
I am less willing to                   Carter Racing is
take risks than my                     likely to succeed
    colleagues             - 0.34

   describes my

     I am grateful for the advice and suggestions of numerous colleagues, and feedback from participants at
a workshop at University of Melbourne. The comments of three anonymous reviewers and the Journal’s
editor have significantly improved the paper. Remaining errors and omissions are my responsibility.
    Copies of the case study and questionnaire are available on request from the author.
     Although the Perseus software that was used to develop the questionnaires provides the ability to
surreptitiously record details of respondents’ servers, this was not done.
      Note, though, that this study involved only five workers and the results were never published. Gale
(2004) derides it as an “anecdote” and “fable”.
      Although a larger sample may be desirable, a wide variety of well-accepted studies has used much
smaller samples, and these have been of homogeneous students. Examples include: Abdellaoui (2000) –
64 economics students; Bleichrodt (2001) – 66 health economics students; Fox and Tversky (1998) – 50
students interested in basketball; Kilka and Weber (2001) – 55 graduate finance students; and Sitkin and
Weingart (1995) – 38 MBA students.
     Assume for simplicity a binomial distribution of race-don’t race responses, with a mean of 0.5 and
standard deviation of [√(0.5*0.5)/67 =) 0.061. The observed pattern would occur by chance in 27.0 percent
of equivalent random samples.
      Unfortunately the sample size is too small to permit structural equation modelling; however, the
dimensions above could be extended using basic path analysis to propose a more integrated model of risk


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