Introduction: us to use behavioral finance as the basis

Introduction:

Behavioral economics and behavioral finance are terms that apply
to a field of study that involves the application of social and human cognitive
and emotional patterns for the purposes of understanding economic decisions and
how they impact market prices, returns and resource allocation. Behavioral
economics is combines the disciplines of psychology and sociology within an
economic framework.

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This paper aims at shedding light on the emotional and
psychological influence that can impact financial decisions and how this
influence can result in irrational behavior.

 

Behavioral finance is a fairly novel topic that has gained
prominence since the early 1990s. Amos Tversky and Daniel Kahneman, winners of
the 2002 Nobel Memorial Prize in Economic Sciences, helped popularize the topic
with their development of Prospect Theory. Also recently Richard H. Thaler was
the recipient of the 2017 Nobel Prize in Economic Sciences for his
contributions to behavioural economics. Thaler incorporated psychologically
realistic assumptions of limited rationality, social preferences, and lack of
self-control, and showed how these human traits systematically affect
individual decisions as well as market outcomes. Psychology plays a big part in
investing. Understanding the psychological motivations can help investors avoid
financial pitfalls. Behavioral
finance bridges the gap between theory and practice by scientifically recording
human behavior. To date, research has focused on rational investors in
efficient markets, while reality deals with day-to-day irrational investor
behaviors and inefficient markets. Combining theory and practice allows us to
use behavioral finance as the basis for advisory services, asset management,
and financial product development

 

A brief history of decision making

The Merriam Webster dictionary defines decision making as, ‘the
act or process of deciding something especially with a group of people’. Chester
Barnard in his book ‘The Functions of the Executive’ imported the term “decision making”
from the lexicon of public administration into the business world (Buchanan & O’Connell, 2006). The following table
summarizes the major milestones in the history of decision making. Decision making under risk and uncertainty

Decision making under certainty indicates confidence in the
forthcoming state of nature and hence the solution is choosing an alternative
that provides the most favorable result. Most decisions involve some level of
risk. Academically, a clear distinction has been made between risk and
uncertainty (Knight, 1921). Risky decisions are
those where the probabilities of the various possible outcomes are known. Uncertainty
is the possibility of there existing several future states of nature, but the
probabilities of each of these occurring are unknown. Most of the important
decisions we make involve uncertainty rather than risk. At times a decision
maker cannot assess the probability of occurrence for the various states of
nature.

 

Expected Utility Theory

The Expected Utility theory has dominated the economic analysis of individual
decision-making under risk from the early 1950’s to the 1990. The expected utility principle was formulated
in the 18th century by Daniel Bernoulli (1738). It was first axiomatized by Neumann
and Morgenstern (1944), and it was further
developed by Savage (1954) who integrated the
notion of subjective probability into expected utility theory. Since its
publication expected utility theory has been dealt with numerous criticisms. One of the main criticisms of the utility
theory is that it provides only a partial analysis of the problem of rational
choice and the discussions of the rationality of decisions in the context of
utility theory are often misleading (Tversky, 1975). Following
the publication of the Prospect Theory (Kahneman & Tversky, 1979), this has become the
best supported alternative to expected utility theory.

 

Efficient Market
Hypothesis 

Market efficiency is the degree to which stock prices
reflect all available and relevant ‘Fama developed the theory of Efficient Market Hypothesis (EMH)
which stated that all available information is already built into stock prices
and hence it is not possible for an investor to outperform the market. Fama
jointly won the 2013 Nobel Prize in Economic Science for this empirical
analysis of asset prices. The
efficient markets hypothesis is the most extensively tested hypothesis in all
the social sciences and is regarded one of the foundations of modern financial
theory. However, the EMH does not account for irrationality as one of the
models fundamental assumption is that the market price of a security reflects
the impact of all relevant information as it is released (Phung, 2002).

 

The Capital Asset Pricing Model

A
fundamental question in finance is how the risk of an investment affects its
expected return. The Capital Asset Pricing Model (CAPM) provided the first
coherent framework for answering this question (Perold, 2004). It was developed in
the early 1960s by William Sharpe, Jack Treynor, John Lintner and Jan Mossin. This
model is an extension of the work done earlier by Harry Markowitz on
diversification and modern portfolio theory. The CAPM suggests that the
distribution of expected rates of return across all risky assets is a linear function of a single
variable. The CAPM is used to determine a theoretically appropriate
required rate of return for an asset, if that asset is to be
added to an already well-diversified portfolio, given the asset’s
non-diversifiable risk. The empirical research testing of CAPM has led to
major innovations in both theoretical and applied econometrics.  In 1990,
Sharpe was the joint recipient of the Nobel Memorial Prize in Economic
Sciences. The CAPM is based on the assumption that: investors are risk averse, capital
markets are perfect, all investors have access to the same investment opportunities and all make the same estimates
of expected returns, standard deviation of returns and the correlations among
asset returns. The model is is often criticized as being unrealistic
because of these very assumptions.ehavioral finance is the newest chapter in
the history of portfolio theory. Behavioral finance explains the typical
mistakes- biases- made by investors. It also provides a detailed picture of
investors’ risk preferences. Unlike the Markowitz analysis, the prospect theory focuses on the significance of
investment losses. In their studies, Kahneman and Tversky found that most
investors are averse to loss. This means that investment losses must be compensated
through the opportunity for higher returns. For most investors, these returns
must be at least twice as high as the potential loss. The utility function of the prospect
theory is shown in the figure below. A maximizer of prospect utility evaluates
the result of his investments using a reference point. For example, this can be
the purchase price of a security. Loss aversion is reflected in the fact that
the utility function initially has a much steeper curve than the profit area.
The prospect utility theory draws from the expected utility theory the characteristic
of declining marginal utility of the gains. The loss area reflects the
declining marginal damage of the losses. This is demonstrated by the fact that
prospect utility maximizers would risk their investment for a break-even opportunity
rather than face a definite loss. Thus, they prefer a random payout to the
expected utility if it is negative. If markets were efficient as per Fama’s
theory, all investment returns would have normal distribution and the application
of the mean-mean standard deviation criterion would still be justified for
prospect theory investors. In reality, the efficient market hypothesis is not
valid, so very few investments have returns with normal distribution. For this
reason, the loss aversion under the prospect theory is the key to an optimal portfolio.
We must replace the efficient market line in the mean-standard deviation model
with a behavioral efficient frontier based on the prospect theory.

 

Figure 2:
The Behavioural Efficient Frontier based on Prospect Theory

The behavioral
efficient frontier was first developed in a paper by Enrico De Giorgi, Thorsten
Hens, and Janos Mayer (2011). It depicts the prospect
theory using a risk-return diagram. Investment results are broken down into
cases in which a profit is made and those in which a loss is sustained. The
degree of loss aversion determines the selection of an optimal portfolio on the
behavioral efficient frontier.

 

Overview of Heuristics and Biases Framework

Heuristics referred to rule of thumb, are means of reducing
the search necessary to find solution to a problem. They are shortcuts that
simplify the complex method of assessing probabilities and values ordinarily
require making judgments, and eliminating the need for extensive calculations.

A heuristics and biases framework can be envisioned as a
counterpart to standard finance theory’s asset pricing model. When decision
maker faced with huge amounts of data and information and an array of decision
problems, people are incapable of doing the complex optimization calculations
that are fundamental assumption under standard finance theory. Instead, they
rely on a limited number of cognitive strategies or heuristics that simplify
the complex events in making decisions. Heuristics are information processing
shortcuts that mainly result from one’s experiences. Of course, such
simplifying shortcuts are productive and allow humans to function in daily
life. By nature, heuristics are imperfect and consequently will result in
biases and errors in decisions.

Confirmation bias – The confirmation bias refers to
the phenomenon of seeking selective information to support one’s own opinions
or to interpret the facts in a way that suits our own world view. They avoid
critical opinions and reports, reading only those articles that put their point
of view in a positive light.

Availability bias – The attention bias states that
products, companies, and issuers that are more frequently highlighted in the
media will be remembered more quickly by investors when they look for a
suitable investment. Negative or scarcely accessible information is not
considered.

Home bias – Statistics show that most investors tend
to buy stocks from companies in their home country. These stocks seem more
trustworthy, as investors grew up with these company names. They are also
mentioned more frequently in the local media.

Anchoring – When making decisions, investors do not
rely on fundamental factors. Rather, they tend to base their decision on the
price at which the stock was purchased. This purchase price acts as the anchor
that causes irrational decisions. When making decisions, people are influenced
by random data, even if they know the data has no informational value or is
outrageously high or low.

Myopic loss aversion – Most investors fear losses
more than they enjoy profits. If they check their stock performance too often,
they will see they have lost money and sell everything off. A long-term view
would be better. The more they can keep their curiosity at bay, the more likely
they are to turn a profit with their investments, provided that their portfolio
is broadly diversified.

Mental accounting – Many private investors make
distinctions in their head that do not exist financially. Often, losses
incurred are viewed separately from paper losses. This means that people are
too quick to sell stocks when they earn a profit and too slow to sell when they
sustain a loss.

Disposition effect – With the disposition effect,
gains are realized too early and losses too late. Turning a paper profit into
real profits makes us happy, while we tend to shy away from turning a paper
loss into a real loss. One possible explanation for this is mental accounting.

Overconfidence – In most cases, we overestimate our
own abilities and think we are above average. Most experts overestimate
themselves – frequently to a greater degree than laypersons do. Overconfidence
is often seen when the markets are on the rise.

Hindsight bias – Hindsight is 20/20. The statement “I
knew the whole time this would happen” shows that we have an explanation for
everything after the fact. This hindsight bias keeps us from learning from our
mistakes.

Representativeness bias – After even a brief period
of positive returns on the financial markets, we may think the world has
changed for the better. People tend to think in terms of schemes and
stereotypes experienced in the past. They arrive at a result too quickly, based
on imprecise information.

Gambler’s fallacy – Here, the effective probabilities
are greatly underestimated or overestimated. For example, based on the false
assumption that prices are about to drop, we sell too soon and vice versa
assuming that the prices will recover soon, even though they are not yet doing
so.

Framing bias – Decisions are based largely on how
facts are depicted in statistical terms. For instance, we do not think that
“Four out of ten are winners” and “Six out of ten are losers” mean the same
thing. The statements are identical, but most people do not realize it.

Regret avoidance – If we invest in a blue chip stock
and it does not perform as hoped, we call this bad luck. However, if we invest
in a niche product that fails to perform well, we tend to regret this more than
we do the failure of the blue chip stock. This is because many other people
have made the same mistake and thus our decision to buy it does not seem so
wrong.

 

Market Anomalies

Behavioral economics and finance has evolved as a response
to irrational behavior among economic players. However, there are anomalies
that occur in the marketplace that would suggest irrational behavior within the
system itself. In the late 1970s and early 1980s, several scholarly works
pointed to apparent inconsistencies between market prices and economic
conditions. Some economists assert that this imbalance is caused by investors
who take advantage of market inefficiencies so as to yield higher returns.
These traditionalists suggest reviewing the returns as a method for pinpointing
and correcting market inefficiencies. However, a 1993 study concluded that
while this approach may remove some irrational behavior from the markets, it
will not correct fundamental inconsistencies (Summers, 1993).

 

Conclusion

The American author Dale Carnegie once advised, “When
dealing with people, remember you are not dealing with creatures of logic, but
creatures of emotion” (Speaking
Tree, 2017).

Indeed, in virtually
every facet of life, humans demonstrate the propensity to behave both logically
and emotionally. Quite often, however, these two types of behavior are
significantly divergent from one another. As this paper has demonstrated,
behavioral economics and finance has evolved not as a replacement of
neo-classical economics but as a complement thereto. By employing psychological
techniques to the study of economic and financial systems, behavioral economics
helps cast a light on irrational consumer decision-making and behavior. The
study of heuristics, framing and market anomalies can help the economist create
a more complete profile of consumer behavior. Adherents to the field of
behavioral economics assert that understanding the basis of irrational consumer
behavior not only aids business development but government policymaking as
well; particularly during times of economic recession and/or market flux.

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