1. Behavioral Finance____________________________________ 3
2. Individual Behavioral Biases ___________________________ 21
3. Investment Processes _________________________________ 41
4. Key Formulas _______________________________________ 57
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Behavioral Finance
1. Behavioral Finance
Learning Objectives
This summary includes a review and an analysis of the principles set forth by CFA Institute.
Upon review of this summary, you should be able to:
Compare the perspective of traditional finance on investor decision making with that
of behavioral finance ........................................................................................................pg. 4
Explain investment decision making from the point of view of utility theory and
prospect theory ..................................................................................................................pg. 9
Explain how cognitive limitations, including bounded rationality, affects investment
decision making ...............................................................................................................pg. 11
Compare the traditional finance perspective on capital markets and portfolio
construction with that of behavioral finance .................................................................pg. 15
©2014 Allen Resources, Inc.
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Study Session 3
Overview
Traditional finance describes investor behavior in an idealized world of rational, risk-averse
investors who have universal access to all relevant information, and who can act upon their
convictions through a market mechanism (an efficient market) that instantly incorporates this
information. Recent discoveries about investor behavior and allegedly persistent market
anomalies have called into question these basic assumptions, and are leading to a developing
field called behavioral finance.
The theories in behavioral finance are not yet as developed, integrated, and comprehensive as
those in traditional finance. However, behavioral finance has provided explanations for some
anomalies found in traditional finance. In the future, there may be a more comprehensive theory
of investor behavior that incorporates elements from both traditional and behavioral finance.
Analyses of investor behavior can be classified as normative, descriptive, or prescriptive.
Normative investor behavior is that which we would expect from a rational, self-interested
investor under idealized conditions. This is the perspective of traditional finance.
Descriptive investor behavior refers to actual observed investor behavior. At times, it varies
from normative behavior, exhibiting biases, errors, and even irrationality. Descriptive investor
behavior is the focus of behavioral finance.
Prescriptive analysis relates to strategies intended to help investors recognize and overcome
biases and cognitive errors so that they may achieve more favorable results, ones closer to those
in the idealized world of normative behavior.
Investor Decision Making: Two Perspectives
Learning Objective: Compare the perspective of traditional finance on investor decision making
with that of behavioral finance.
As stated above, traditional finance is based on idealized circumstances in which all investors
are rational, risk-averse agents who seek to maximize their utility through participation in an
efficient market, one that quickly incorporates all relevant information into market prices. One
of the cornerstones of traditional finance is utility theory.
Utility Theory - Background
Utility theory posits that each investor has a utility function, a formula that can be used to
evaluate the utility (to the investor) of all possible investment options. Generally, higher utility is
expected with higher expected returns. Because investors are assumed to be risk averse, there is a
downward adjustment in expected utility based on the degree of risk (often measured by standard
deviation of returns).
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Each investment, and its effect on the entire portfolio, can be viewed through the prism of utility
theory. It is assumed that the rational, self-interested investor will choose investments to
maximize their own aggregate utility.
Axioms of Utility Theory
There are four main axioms to utility theory:
1. completeness
2. transitivity
3. independence
4. continuity
Completeness means that an individual investor can have preferences among alternatives, and
can decide between any two alternatives, say X and Y. The investor may prefer Y to X, X to Y,
or be indifferent between them.
Transitivity provides for consistent logic in preferences. If an investor prefers option X to Y,
and option Y to Z, then they should, by the transitive axiom, prefer X to Z. Similarly, if an
investor prefers option X to Y, and is indifferent between options Y and Z, then they should
prefer X to Z.
Independence allows for additive utilities. For example, suppose:
•
X and Y are mutually exclusive options,
•
the investor prefers X to Y, and
•
Z is a third choice that can be combined (with weight w) with X and/or Y.
Under independence, the utility of (X + wZ) should be greater than the utility of (Y + wZ). If the
utility of X is dependent on how much of Z is available, then independence does not hold, and
the utilities are not additive.
Continuity states that indifference curves should be continuous. If an investor prefers Option X
to Option Y, and Option Y to Option Z, there should be some combination of Option X and
Option Z which produces the same utility as Option Y.
In traditional finance theory, all four of these axioms hold, and one can probability weight the
potential outcomes in a given scenario to come up with an aggregate expected utility.
Traditional finance theory also maintains that investors update their beliefs with new information
(instantly) and adjust their expectations accordingly. The way in which investors should update
their beliefs is related to Bayes’ Theorem.
©2014 Allen Resources, Inc.
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Study Session 3
Bayes’ Theorem
Often times, we will have knowledge of the probability of event B given that event A1 occurs,
and the probability of B given that event A2 occurs. But suppose B is the outcome of an
experiment designed to try to tell us whether A1 or A2 has occurred. That is, we want to know the
probability that A1 has occurred, given that we observe B. We can use Bayes’ Theorem to find
the conditional probability we want to know, using the conditional probabilities we already
know.
Bayes’ Theorem provides the probability of A1 given B has occurred:
P( A1 | B ) =
P( A1 ) × P( B | A1 )
P( A1 ) × P( B | A1 ) + P( A2 ) × P( B | A2 )
To illustrate, consider the following problem. Suppose you are an analyst with a forensic
accounting firm. Your company closely examines the financial statements of publicly-traded
companies for evidence of accounting fraud. Suppose 1% of companies in your stock universe
commit accounting fraud. Let A1 be the event “company has committed accounting fraud” and
A2 be the event “company has not committed accounting fraud.” Thus, if we select a company at
random, the probability that the company has committed accounting fraud is 0.01 (i.e., P(A1) =
0.01). This probability is called the prior probability because it is the probability assigned
before any empirical data are obtained; it is the initial probability based on the present level of
information.
The prior probability that a company has not committed accounting fraud is P(A2 ) = 0.99, found
by 1 – .01.
Assume that there is a software program to detect fraudulent accounting in financial statements,
but it is not foolproof. Let B denote the event “the program shows fraud is present.” Assume that
historical evidence shows that if a company has committed accounting fraud, the probability that
the software will detect it is 0.80, i.e., P(B|A1 ) = 0.80.
Assume further that the probability is .02 that a company has not committed accounting fraud,
but the software program indicates that it has, i.e. P(B|A2 ) = 0.02.
Now, suppose we select a company at random, run the fraud detection software, and the results
indicate accounting fraud is present. What is the probability that the company actually has
committed accounting fraud? In symbolic form, we want to know P(A1|B) which is P(has
committed fraud | the software detected fraud). P(A1|B) is called the posterior probability,
which is the revised probability based on the benefit of additional information.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Using Bayes’ Theorem, we can determine the posterior probability:
P( A1 | B ) =
P( A1 ) × P( B | A1 )
P( A1 ) × P( B | A1 ) + P( A2 ) × P( B | A2 )
=
(0.01)(0.80)
(0.01)(0.80) + (0.99)(0.02)
=
0.008
0.008 + 0.0198
= 0.288
Therefore, the probability a company has committed accounting fraud, given that it has been
flagged by the fraud detection software is 0.288. How is the result interpreted? If a company is
selected at random, we had found earlier that the probability it had committed accounting fraud
is 0.01. If the financial statements are examined by the fraud detection software and the result is
positive for fraud, the probability that the company actually has committed fraud is increased
from 0.01 to 0.288. This is a substantial increase, but note how uncertain the result is, even with
only a 2% error rate by the software.
The theorem can be generalized into more than just two events:
P( Ai | B ) =
P( Ai ) × P( B | Ai )
P( A1 ) × P( B | A1 ) + … + P ( An ) × P( B | An )
Where An refers to any of the n possible outcomes.
Using the preceding notation, the calculations for the fraud problem can be summarized as
follows.
Prior
Probability
Conditional
Probability
Joint Probability
Posterior Probability
P( A i )
P(B| A i )
P( A i and B)
P( A i |B)
Fraud, A1
0.01
0.80
0.008
0.008 / 0.0278 = 0.288
No fraud, A 2
0.99
0.02
0.0198
0.0198 / 0.0278 = 0.712
P(B) = 0.0278
1.00
Event
Ai
©2014 Allen Resources, Inc.
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Study Session 3
Evaluation Under Traditional Finance
Returning to our discussion on utility theory, it should be emphasized that traditional finance
does not assume that all investors have the same utility function. It merely posits that all
investors go through the same evaluative process when faced with choices under uncertainty:
1. Follow the axioms of utility theory.
2. Assign probabilities to uncertain events.
3. Incorporate new information using a Bayesian approach.
4. Choose the option that maximizes utility, subject to whatever constraints (budget, legal)
are imposed.
Rational Economic Man
An investor who follows this process can be considered a Rational Economic Man (R.E.M.),
and such a person is believed to act with rationality and self-interest using complete and perfect
information. A follower of behavioral finance would point out, however, that this ideal is far
from reality. Investors are driven by both emotion and reason; they may behave altruistically,
rather than solely out of self-interest, and perhaps most obvious of all, information is seldom
complete and perfect - especially for investors lacking access to the fastest and most
comprehensive market data providers.
Risk Aversion
A fundamental assumption made by traditional finance is that investors are risk averse. That is,
they do not take gambles with their money unless they are adequately compensated for the risk.
So, for example, suppose you offered a gamble to a risk-averse investor, one in which a fair coin
is flipped, and if the investor calls head or tails right, they receive $100, but if they call it wrong,
they must pay $100. A risk-neutral or risk-seeking investor will take the gamble. A risk-averse
investor will turn you down, unless, their downside risk is significantly limited, e.g., to $50.
A certainty equivalent is a concept that relates to such games and gambles as described above.
Suppose you were offered a 50/50 shot of winning either $0 or $100. How much would you pay
to take the 50/50 chance? A risk-averse investor would not pay $50 or more, because the
expected payoff is $50, and aversion implies they would insist on paying less than the expected
value before agreeing to play. The largest amount they would be willing to pay is the certainty
equivalent. Certainty equivalents can also be defined to be the lowest amount they would be
willing to accept to not participate. For instance, if you offered the above chance for free, and
then tried to buy out the risk-averse player, you would need to pay a certainty equivalent.
Behavioral Finance Perspective
Behavioral finance rejects the assumptions of rationality, self-interest, and perfect information as
an unrealistic portrayal of the investment world and those acting in it. Behavioral finance starts
with observation of actual investor behavior and interprets it through an understanding of human
cognition and psychology.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Behavioral finance studies the ways in which individuals deviate from behavior predicted or
assumed by traditional finance. The perspective of the individual investor can be termed the
micro perspective; the perspective of the market, the macro perspective. The micro perspective
examines the effects of cognitive errors, biases, and emotions on their decisions; the macro
perspective examines the effects of these behaviors upon markets.
Challenges to Traditional Finance Assumptions
First, rationality is not the only influence upon investors. Emotions have effects as well, and they
can lead to herd behavior, fear of loss, overestimating the likelihood of infrequent large losses,
and temptations to forgo saving in order to spend immediately. Behavioral finance posits that
investors exhibit bounded rationality - they are rational within the constraints of their
knowledge, their cognitive capacity, and their mastery over their emotions.
Also, perfect information obviously does not exist. Even good, relevant information comes at a
cost - in both time to obtain and process it, as well as money (e.g., think of the cost of a
subscription to a Bloomberg terminal).
Finally, self-interest is not the only motivating factor in many cases. For example, a parent who
invests for a child’s college tuition is a form of altruism, though it is undeniable that indirect
benefits to self-interest are intertwined with that and with most altruistic behaviors.
However, R.E.M. is an appealing theoretical concept because it facilitates the use of models
through simplification and quantification. It provides a guide as to what would be normative
behavior, thus implicitly defining non-normative behavior - and giving insight into why such
deviations occur.
Utility Theory and Prospect Theory
Learning Objective: Explain investment decision making from the point of view of utility theory
and prospect theory.
Utility Theory
As mentioned above in the section on traditional finance, utility theory posits that when faced
with choices that have various levels of returns and risk (or benefits and costs), individuals
evaluate the utility of each option according to their own utility function and choose the option
that maximizes their utility, subject to any constraints.
Behavioral finance questions whether anyone actually goes through such calculations. This is a
valid point, but it only addresses truly deliberative analyses. It could be that individuals
subconsciously or implicitly make such evaluations through a “gut feeling” or other seemingly
superficial reflections.
Risk is a major component of utility theory. Behavioral finance points to the popularity of lottery
tickets as an example that disproves the notion that individuals are necessarily risk averse.
©2014 Allen Resources, Inc.
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Study Session 3
Buying lottery tickets is effectively risk seeking, because it creates uncertainty in a situation that
had no uncertainty or risk before. Under the assumptions of traditional finance, individuals need
the prospect of a reward to be willing to move from a position of certainty to one of uncertainty.
If the potential rewards are large enough, they are willing to make that tradeoff.
The purchase of insurance is a tricky example. An individual voluntarily accepts a small certain
loss (the premium) in exchange for an uncertain, large gain (in the case of insurance, the gain is
actually the avoidance of an uncertain, large loss). Some would say this is risk seeking.
However, prior to buying insurance, the individual is already in a position of uncertainty - they
face the possibility of a large, uncertain loss. Insurance converts this large, but uncertain loss into
a certain, small loss (the premium). The willingness to buy insurance is considered by actuaries
to be evidence of risk aversion, not risk seeking.
Risk evaluation is reference-dependent; one’s wealth or frame of reference influences the
perception of risk and whether behavior will effectively be risk seeking or risk avoiding. It has
been conjectured by researchers Friedman and Savage that some people have a double inflection
utility function, in which risk aversion characterizes behavior for both low and high levels of
income, yet risk-seeking behavior emerges for intermediate levels of income. This means that the
utility curve is concave (increasing at a decreasing rate), for low and high wealth values, but
convex (increasing at an increasing rate) for intermediate wealth values.
Prospect Theory
An alternative to utility theory is prospect theory. The insight of prospect theory is that
individuals evaluate an option based on potential gain and loss from a baseline condition, not on
prospective differences in total ending wealth.
Deviations from the baseline condition (or reference point) are judged by a value function, which
differs for gains and losses. The value function is concave for gains, indicating risk aversion (as
with traditional finance). However, the value function is convex for losses, indicating riskseeking behavior. A common example of risk-seeking behavior is that an individual choosing
between:
1. a certain loss of $1,000 or
2. a 50/50 probability between a loss of $2,000 and a loss of $0
will tend to choose option 2. We see this type of behavior with gamblers in a loss position - there
is a temptation to double up on bets to try to avoid a loss and “get out of the hole.”
Neuro-economics is a field that integrates knowledge of biological systems into the behavioral
finance picture. Brain imaging studies allow researchers to identify where blood flow is active
under a range of mental situations related to reason, judgment, fear, anticipation, and uncertainty.
There has been a great deal of focus on the role of the chemical neurotransmitters dopamine and
serotonin. Increases in dopamine have been found to be closely related to reward and pleasure;
decreases in serotonin are related to anxiety and depression.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Effects of Cognitive Limitations
Learning Objective: Explain how cognitive limitations, including bounded rationality, affects
investment decision making.
The history of evaluating decision making from an expected value and expected utility basis
dates back to the mathematicians Pascal and Bernoulli. More recently, risk has been recognized
as having an effect on decision making. Through relaxing assumptions underlying traditional
finance, we can start to gain insights into behavioral finance. In particular, by relaxing the
assumption that investment decisions are made with reliance on perfect information, we come to
the concept of bounded rationality - individuals do not (and cannot) access all information, and
the information they do access may be erroneous or incomplete. This way, we can understand
how they may sometimes be risk seeking. Rather than being risk averse, we find that people tend
to be loss averse.
Bounded Rationality
Recent behavioral finance theories have made the case that people are not fully rational (in the
traditional finance sense) when it comes to decision making. They do not have perfect
information, and they do not optimize utility functions. Instead, they tend to make a decision
when they feel they have a solution that is “close enough” for their purposes. It would be very
expensive (cognitively, if not also monetarily) and time-consuming to actually optimize a
solution, and thus it is infeasible and self-defeating to spend so much effort on such optimization.
Once a solution is found that meets the basic criteria and constraints, the choice is made.
One term used to describe the purpose individuals have under bounded rationality is satisfice - a
blend of two words, satisfaction and suffice. If a solution is satisfactory and will suffice, the
search for more optimal solutions may stop. For example, suppose an investor wishes to invest in
a large-cap, U.S. stock index fund, and they already have an account with Fund Family A, which
offers one. They may simply choose that one rather than bothering to investigate whether there
are Fund Families B and C which may offer such funds as well, perhaps with lower expense
ratios or a lower minimum investment.
Prospect Theory
Prospect theory is based on the evaluation of different alternatives, or prospects. Prospects are
most often viewed in terms of gains and losses from a baseline condition. Choices are made in a
two-stage process. First, there is a framing, or editing, phase. This presents the prospects in
context. Cognitive constraints require a simplification process to be an integral part of the
framing of prospects. Second, there is the evaluation phase in which the prospect with the
maximum perceived value is chosen.
For each prospect, there are three steps in the framing process, followed by three more steps that
may be applied to two or more prospects together.
©2014 Allen Resources, Inc.
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Study Session 3
1. Codification - of prospects in terms of losses and gains relative to a reference point, not
in terms of ending wealth.
2. Combination - simplifying a list of prospects by combining probabilities with identical
gains/losses. This is somewhat of a trivial step, but as an example, if a prospect has a
10% chance of a 20% gain, an 80% chance of a 2% loss, and another 10% chance of a
20% gain, you would combine the first and last to show a combined 20% chance of a
20% gain.
3. Segregation - for each prospect, one should separate the riskless component (if any)
from the risky component.
For two or more prospects:
4. Cancellation - one should discard common outcome probability pairs when deciding
between prospects, because they do not differentiate between the prospects. Just focus on
what distinguishes them.
5. Simplification - We tend to round off probabilities. So if faced, say, with outcomes X, Y,
and Z that have probabilities 39%, 32%, 29%, we are more likely to think of them as
40%, 30%, and 30%, particularly if we are doing any quick figuring in our heads. Very
low probability outcomes may be discarded entirely unless their potential severity is such
that they should not be ignored.
6. Detection of dominance - outcomes that are strictly dominated (that is, worse in every
potential scenario) are discarded and not evaluated further.
Prospect theory has uncovered some interesting anomalies, such as in the following study, which
illustrates isolation effects.
Suppose a large group of students was asked which of the following two options they would
prefer:
Option X: a 75% chance of receiving nothing, but a 25% chance of receiving $3,000
Option Y: an 80% chance of receiving nothing, and a 20% chance of receiving $4,000
Based on traditional finance, the expected value of Option Y is the higher of the two options
($800 versus, $750 for Option X), and indeed, most students will indicate a preference for
Option Y.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Now, consider a variation on this question of prospects. Change the framing of it so that it is now
a two-stage process. In Stage 1, let there be a 75% chance of being eliminated before getting to
Stage 2, and those eliminated get nothing. The other 25% go on to Stage 2, where there is
another expected value option set:
Option A: 100% chance of getting $3,000
Option B: 80% chance of getting $4,000, but a 20% chance of getting nothing
Students are asked to choose Option A or Option B before they enter Stage 1. The vast majority
will choose Option A, even though it has a lower expected value than Option B.
E[Option A] = 0.25 × $3,000 = $750
E[Option B] = 0.25 × [0.80 × $4,000 + 0] = $800
It appears that most students engaged in cancellation, ignoring Stage 1, because it affected both
options and thus did not distinguish between them. They simply viewed it as a choice between a
sure $3,000 and a 20% chance of getting nothing (overshadowing the 80% chance at $4,000).
Rather than risk getting nothing, they chose what they effectively considered a sure thing.
Evaluating Prospects
Traditional finance maintains that individuals are risk averse, while behavioral finance, via
prospect theory, maintains that individuals are loss-averse, using a value function and weighting
scheme - one that reflects the tendency of people to overreact to small probability loss events and
underreact to higher probability gains.
One author proposes a value function (similar to a utility function), which would take the form:
U=
i
wi pi V ( xi )
Where:
xi = potential outcomes
pi = the probability of each outcome
V = value assigned to the outcome
wi = weights from a probability weighting function. These weights will likely differ from the
expected probabilities. For instance, to induce a person to take a 50/50 gamble, the gain often
has to be twice as large as the potential loss. This would have an effect on the weighting
function.
©2014 Allen Resources, Inc.
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Study Session 3
Framing also plays a key role by revealing how reference-dependent people are. Consider a
situation in which individuals are presented with the following two scenarios:
Scenario 1
50% chance of winning $300
50% chance of losing $200
Even though the expected outcome is a gain of $50, most individuals would reject this gamble
because the amount that can be won is less than twice the amount that could be lost.
Scenario 2
Option A: 100% chance of losing $100
Option B: 50% chance of winning $100, 50% chance of losing $200
In Scenario 2, Option A has an expected value of -$100. Option B has an expected value of -$50.
Most people would take the gamble under Option B under Scenario 2, which is curious because
it has the same net effect on wealth as Scenario 1, where they would not take the gamble. In
other words, they will gamble to potentially avoid a loss, but will not be as likely to gamble in
order to get a gain, even if the net effect of the expected loss and gain are the same (in this case,
+$50).
This research was pioneered by Kahneman and Tversky, and they conjectured that the difference
is in individuals’ utility functions for gains and losses. This illustrates that people are risk averse
when there is a moderate to high probability of a gain, and a low probability of loss, but riskseeking when there is a high probability of loss and a low probability of gain. This is illustrated
in the graph of utility on the following page (x-axis is expected change in wealth; y-axis is
utility).
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
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Capital Markets and Portfolio Construction
Learning Objective: Compare the traditional finance perspective on capital markets and portfolio
construction with that of behavioral finance.
As mentioned above, traditional finance views capital markets as highly efficient - they fully,
accurately, and instantly incorporate all available information into market prices. Prices are seen
as “right” at any given time. Advocates of traditional finance point out that stock prices appear to
follow a random walk, and are thus not predictable; therefore, all information must already be in
stock prices or an arbitrageur would quickly remedy the situation by buying or selling securities
until the “right” prices were reached. Behavioral finance advocates criticize this view,
maintaining that stock prices are not necessarily correct simply because they are unpredictable.
Traditional Finance View
It has been observed that it is very difficult for any investor to consistently outperform the
market without taking on additional risk. Under the efficient market hypothesis (EMH),
investors have rational expectations, and information is incorporated very rapidly into prices.
How does information get incorporated into stock prices? Presumably, by buying and selling
based on information. Why do investors buy and sell based on information? Presumably, they
must think they earn a return for gathering and acting on information; if not, information would
not be gathered, and prices would not be efficient. However, note that the existence of such
return implies that markets must not be efficient! This observation is known as the GrossmanStiglitz paradox.
©2014 Allen Resources, Inc.
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Study Session 3
Rather than ask whether or not markets are efficient, we can ask how efficient they are. This has
led to three main categories of market efficiency: weak, semi-strong, and strong.
Weak-Form Efficiency
Weak-form efficiency means that, at the very least, past prices and trading volume are not
helpful to predict future prices. Serial correlation tests of asset prices reveal that past prices are
of no help in projecting future prices. Burton Malkiel is said to relate a story in which a technical
analyst is brought a chart purporting to show price changes for a stock, but which really
represented series of coin flips. The analyst did not recognize the random pattern and instead
thought that the pattern clearly indicated a buying opportunity. In a similar vein, Warren Buffet
is credited with the joke, “I realized technical analysis didn’t work when I turned the charts
upside down and didn’t get a different answer.”
Empirical evidence for weak-form efficiency has been generally established.
Semi-Strong Efficiency
With semi-strong efficiency, all publicly-available information is incorporated into stock prices
so rapidly that one cannot generate a return in excess of the market return just by gathering and
analyzing such information.
One way to test semi-strong efficiency is through event studies, especially stock split
announcements. One interesting study found that stock prices tend to rise after a stock split has
been announced. Since there is no economic benefit inherent in a split of stock, the results
seemed to expose an inefficiency. However, as it turns out, stock-splitting is significantly
correlated with subsequent above-average increases in dividends. Thus, the market begins to
factor in the potential for such an increase after the announcement of the split. So this event
study turns out to actually support semi-strong efficiency.
A more direct approach to evaluating market efficiency is to look at the performance of activelymanaged funds. If markets are not semi-strong efficient, portfolio managers should be able to
generate alpha (returns that are, even after adjusting for risk, superior to the market as a whole).
However, empirical evidence reveals that the opposite is the case. The vast majority (90%) of
professional managers have negative alpha; the alpha of the average professional manager is
-0.4%, before fees and -1.1% after fees. This evidence supports semi-strong efficiency.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
Market Anomalies
There are many market anomalies which, if they did not have good explanations, appear to argue
against semi-strong market efficiency. These anomalies fall into three major categories:
fundamental, technical, and calendar.
1. Fundamental Anomalies
•
Growth versus value: value stocks are said to outperform growth stocks, because the
performance of both types tend to revert to the mean over time, which means that value
stocks tend to improve and growth stocks tend to decline.
•
Small-capitalization stocks are thought to also generally outperform the market.
However, some researchers maintain that this is not an anomaly, because size and value
represent risk factors for which the market pays a premium.
2. Technical Anomalies
There are claims that certain technical anomalies can lead to abnormal returns. However, the
evidence is not widely accepted, and in any case, the magnitude of abnormal returns is so
small that once trading expenses are considered, they cannot be acted upon profitably. These
claimed anomalies are:
•
Moving averages - one should buy when a short-term moving average line (of price)
moves up and crosses a long-term moving average line, because the stock is thought to be
poised to “break out.” Similarly, one should sell when a short-term moving average
breaks through from above a long-term moving average. This suggests that the stock is
about to fall more sharply in value.
•
Trading ranges - this is the notion of levels of support and resistance, where a stock that
breaks new highs (crossing the previous highs, which are considered a “resistance” level)
is thought to be moving to a new, higher trading range. Conversely, a stock that breaks
through its support level may be headed down to a new, lower trading range.
3. Calendar Anomaly - The January Effect
The January Effect is a well-documented phenomenon, though it seems to be muted of late
and possibly undergoing some temporal shift. The effect observed is that small-capitalization
stocks tend to do unusually well in January, yet unusually poorly in the prior December.
There are some rational reasons for this, however. One is tax-motivated selling at the end of
the calendar year, driving down prices, with repurchases in January, driving up prices. The
other relates to “window dressing” for the disclosure of portfolio holdings: fund managers are
thought to wish to have their portfolios full of blue-chip stocks for end-of-year reporting, and
so lesser-known stocks (usually the small ones) are sold in December, only to be bought again
in January (driving up their values). Theoretically, one could earn abnormal returns by buying
in December and selling in January.
©2014 Allen Resources, Inc.
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Study Session 3
Evidence for the semi-strong form of market efficiency is moderately strong, but not
unquestioned.
Tests for the strong form of market efficiency, in which all public and private data are
incorporated into stock prices (including inside information), are impractical to conduct because
researchers cannot test whether insider information is included in stock prices - by definition, it
is inside information, and therefore, they would not know about it.
Generally, the evidence is that larger and older markets are more efficient than smaller and
younger markets (such as emerging markets). Similarly, efficiency tends to be less strong for
small cap stocks and illiquid markets (like real estate and venture capital).
As an aside, hedge funds can have mixed effects on market efficiency. On the one hand, they
often engage in high-volume arbitrage trading, forcing efficiencies in pricing upon the system.
However, by imposing lockout periods during which investors cannot sell their shares, they
inhibit efficiency.
Portfolio Construction
Traditional finance maintains that portfolios are constructed with an eye toward mean-variance
efficiency, with consideration of risk tolerance, investment objectives/constraints/circumstances,
an investment policy statement, and a regularly updated asset allocation plan. Investors are
assumed to have perfect information and rational expectations.
Behavioral Finance View
Behavioral finance denies that there can be (and in fact, does not offer) a single unified theory
that explains all of investor behavior. None of its findings have been embraced as much as the
efficient market hypothesis.
Spending and Saving
Some researchers in behavioral finance build on the traditional life-cycle model, in which
investing and spending follow archetypal patterns throughout one’s life. However, this model
can be adapted to account for issues of self-control (the temptation to spend now and vaguely
hope to save later), mental accounting (the tendency to treat the same amount of money
differently depending on some kind of internal categorization or allocation), and framing biases
(making different choices in equivalent economic situations because of how the situations are
presented).
Behavioral finance classifies wealth into a hierarchy:
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•
current income (lowest level)
•
current assets
•
present value of future income (highest level)
Study Guide for the Level III 2015 CFA® Exam - Reading Highlights
Behavioral Finance
There is a tendency to spend more from the resources at the lower levels in the hierarchy than
those at the upper. But some types of wealth can be classified in more than one way. For
example, consider stock dividends. If they are paid in cash, rather than reinvested, they may be
thought of as current income, and thus spent. Similarly, if you automatically invest a portion of
your paycheck into your 401(k) pension plan, you might not think of it as part of your current
income. This all affects the likelihood of spending the money in the present.
Asset Pricing
Behavioral finance posits that asset prices are, in part, a function of both fundamentals and
market sentiment. Even sentiment can be measured (indirectly), such as through analysts’ ratings
and forecasts. Assuming that not all analysts think and rate alike, every security will have a
distribution of recommendations, and these can be thought of as reflecting investor sentiment.
The discount rate can be considered to be a function of the risk-free rate, fundamentals, and
sentiment.
Sentiment can be considered errors in belief, deflecting prices away from what they should be
based on fundamentals. One question for analysts is, are such errors random, or do they bias
prices upward or downward? If the latter, then an active strategy could be profitable.
Behavioral Portfolio Theory
Behavioral Portfolio Theory (BPT) posits that investors construct their portfolios in layers,
with different expectations of returns and attitudes about risk in each layer.
BPT aims to maximize wealth subject to a safety constraint. Investors combine a safety layer
(such as government bonds) with various aspirational layers (risky assets).
Portfolio construction has five notable features:
1. The amount allocated to each layer depends on investor goals and the importance
assigned to each goal.
2. Allocation of funds within a layer (to specific assets) depends on the goals set for each
layer.
3. The number of assets chosen for a layer depends on an investor’s utility function. The
greater the risk aversion, the greater number of assets held.
4. Concentrated positions in some securities may occur if an investor thinks they have an
informational advantage.
5. Investors reluctant to realize losses may hold disproportionate amounts of cash to avoid
forced liquidations (this illustrates an aversion to realizing losses).
Note that portfolios formed via BPT may not be mean-variance efficient.
©2014 Allen Resources, Inc.
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Study Session 3
Adaptive Markets Hypothesis
The Adaptive Markets Hypothesis (AMH) applies principles of biological evolution to markets
- there is competition, natural selection, and adaptation. Success is measured as adaptation and
survival in a competitive environment. Under AMH, individuals are not purely rational; they
may be motivated by self-interest, but they can make mistakes. The successful investor will learn
from mistakes and adapt their behavior.
Implications of AMH include:
1. Survival is the essential objective.
2. The ability to adapt and innovate is critical for survival.
3. The relationship between risk and reward is not constant; it actually varies over time.
4. Active management can add value through arbitrage opportunities.
5. Any given strategy will have good years and bad years.
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Study Guide for the Level III 2015 CFA® Exam - Reading Highlights