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BEHAVIORAL FINANCE

▪ Behavioral Finance is a field of study that relates to the issues investors

face when making financial decisions

▪ Many studies suggest that the average investor harms himself due to heu-

ristics embodied in the human psyche

▪ These common heuristics include anchoring, groupthink/herding behavior,

confirmation bias, bounded rationality, overconfidence/bias towards action and loss aversion/mental accounting

▪ These heuristics often cause irrational behavior that can negatively affect

investment performance

▪ Investors are human and should not fight the fact that they are fallible

▪ Processes can be implemented that control behavioral and emotional pit-

falls in investing

THE PROBLEM

Studies often suggest that the average individual investor is unable to keep pace with the broad market over time. Firms

like DALBAR, a provider of research, ratings and rankings of intangible factors to financial services companies, and Morn-

ingstar, an investment research provider, have been studying the effects of investors’ behavior on their own investment re-

sults for decades to prove the preceding statement true. Using mutual fund data as a proxy for individual investor returns,

what these firms have determined, and what this paper will address, is that when following their emotions, investors often

make poor decisions that negatively impact their own investment results.

On a performance basis, over the last twenty years ending in 2012, the average equity mutual fund investor outperformed

the S&P 500 Index nine years, or 45% of the time. Additionally, during the calendar years when the average investor does

outperform the equity market, the outperformance is not enough to make up for the average underperformance (-4.55%

average underperformance and +3.51% average outperformance). The graph below illustrates the lack of excess returns

earned by the average mutual fund investor annualized over the last twenty years (DALBAR).

Your Success Is How We Measure Ours | Stringer Asset Management LLC | Email: info@stringeram.com | Phone: 901-800-2956

0%

2%

4%

6%

8%

10%

0% 18%

20-Y

ear

Ann

ualiz

ed R

etur

n

Lower Risk Spectrum Higher

Source: DALBAR 2013 QAIB & Zephyr Associates, Inc. Equity Benchmark is represented by the S&P 500 Index; Fixed Income Benchmark is represented by the Barclays U.S. Aggregate Bond Index. Investor returns are calculated by DALBAR using data supplied by the Investment Company Institute. Past perfor-

mance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index.

Fixed Income Benchmark

Fixed Income Investor

Equity Benchmark

Equity Investor

EXHIBIT 1: AVERAGE INVESTOR VS. BROAD MARKET PERFORMANCE (JANUARY 1993-DECEMBER 2012)

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In their 2013 Quantitative Analysis of Investor Behavior (QAIB) study, DALBAR calculated that on an annualized basis, the

average equity investor earned 4.25% over the past 20 years compared to the S&P 500 Index’s return of 8.21%. One ex-

planation for this underperformance is that investors tend to chase returns, investing in a product after a period of strong

performance and exiting once performance declines. In other words, investors buy high and sell low. Investors’ lack of pa-

tience in general is demonstrated by the reality that the average holding period for an equity mutual fund is 3.31 years for

the past 20 years according to the same study – not even a full market cycle. The difference in returns over a 20-year peri-

od (-3.96% annualized) confirms that investors’ behavior is detrimental over the long term.

The Investment Company Institute (ICI) provides an example of investors making poor timing decisions by tracking asset

flows into and out of equity and fixed income mutual funds. These flows compared to the performance of the respective

broad market index are shown below.

The numbers show that mutual fund investors move out of equity funds in droves after significant down years (2002, 2008)

and subsequently miss the following bounce back in the market. For example, in 2008, $229 billion exited equity mutual

funds and the market returned 26.46% in 2009. Similarly, $29 billion entered fixed income funds and earned a below aver-

age 5.93% (if invested at the beginning of the year 20091). The opposite is also true. The largest inflow to equity mutual

funds was $314 billion in 2000 after the technology run-up in 1999. The S&P 500 Index subsequently fell 9.11%, 11.88%,

and 22.10% in 2000, 2001 and 2002, respectively, further proving that individual investors make very poor timing decisions.

Inflows into equity funds remained positive until 2002, when $29 billion was withdrawn. In 2003 the S&P 500 Index returned

28.68%.

Individual investors not only underperform the broad market, but in many cases underperform their own investments. Morn-

ingstar captures the true costs of chasing performance by measuring what they call the investor return; that is, what the av-

erage investor in a mutual fund actually earned. Morningstar calculates investor returns by adjusting the officially reported

EXHIBIT 2: CALENDAR YEAR EQUITY & FIXED INCOME FUND FLOWS ($BILLIONS) AND MARKET PERFORMANCE

2013* 2012 2011 2010 2009 2008 2007

Equity Flows ($) $161.0 -$153.1 -$128.3 -$23.4 -$1.8 -$229.1 $74.2

S&P 500 Index 32.39% 16.00% 2.11% 15.06% 26.46% -37.00% 5.49%

Fixed Income Flows ($) -$83.4 $303.6 $125.1 $235.6 $379.6 $29.1 $108.5

Barclays U.S. Aggregate Bond Index -2.02% 4.21% 7.84% 6.56% 5.93% 5.24% 6.96%

Difference in Flows ($Equity - $Fixed) $244.4 -$456.7 -$253.4 -$258.9 -$381.3 -$258.2 -$34.3

Difference in Returns (S&P- BC Agg) 34.41% 11.79% -5.73% 8.50% 20.53% -42.24% -1.47%

2006 2005 2004 2003 2002 2001 2000

Equity Flows ($) $148.5 $124.0 $172.0 $144.2 -$29.6 $32.9 $314.5

S&P 500 Index 15.79% 4.91% 10.88% 28.68% -22.10% -11.88% -9.11%

Fixed Income Flows ($) $60.2 $31.3 -$10.6 $33.2 $141.7 $87.7 -$50.0

Barclays U.S. Aggregate Bond Index 4.33% 2.43% 4.34% 4.11% 10.27% 8.42% 11.63%

Difference in Flows ($Equity - $Fixed) $88.3 $92.7 $182.6 $111.0 -$171.2 -$54.9 $364.5

Difference in Returns (S&P- BC Agg) 11.46% 2.48% 6.54% 24.57% -32.37% -20.30% -20.74%

Data Source: Investment Company Institute & Zephyr Associates, Inc. Equity Benchmark is represented by the S&P 500 Index; Fixed Income Bench-mark is represented by the Barclays Capital U.S. Aggregate Bond Index. Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index. *includes estimates.

1The annualized return for the Barclays Capital U. S. Aggregate Bond Index is 7.91% from 1/1976-12/2013.

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returns based on cash flows into and out of the funds. The divergence between the actual return and the investor return

shows how well or how poorly investors timed their entry and exit points. For example, during the ten-year period ending

December 2013, the average reported annualized return for mid-cap growth funds was 9.01%. The average investor in

these funds return 7.80% annualized over this same time period. For mid-cap value funds, the average reported return was

8.95% compared to an average investor return of 6.69%.

Plenty of clear-cut examples of investor underperformance exist at the individual fund level as well. The following chart

shows the total return versus the investor return of four mutual funds with very different objectives. The first three mutual

funds on the list – Funds A, B and C – are actively managed, benchmark-independent funds that have historically not close-

ly followed the returns of a broad market benchmark. Fund D is an actively managed large cap core fund that is benchmark-

aware, so its performance should not be expected to deviate significantly from that of the broad equity market.

In each case, the 10-year annualized investor return is significantly lower than the total return of the Fund. Furthermore, the

average investor’s return for Funds A and C was actually negative, while the mutual funds and the broad market generated

positive absolute performance. Investors in Fund D performed in line with the Fund and the broad market – most likely a

result of staying invested over a longer time period. The data suggests two things. First, in most cases, investors in actively

managed investments make poor timing decisions, therefore causing them to underperform their own investments and the

broad market alike. Second, investors in Funds A, B and C, which will deviate from their respective indexes, are more likely

to be governed by their emotions when making buy and sell decisions, the reasons for which will be discussed later in this

paper. Since Fund D is a more benchmark-aware Fund, investors may not have been as tempted to abandon the strategy

during a period of significant deviation from the broad market’s return.

The numbers are difficult to dispute. Individual investors can be their own worst enemies when making investment deci-

sions. Their poor timing decisions fueled by overall impatience and aversion to loss cause them to underperform the broad

market and the actively managed investments they own. The remainder of this paper will discuss several reasons for this

type of investor behavior and will present solutions for overcoming these emotional and behavioral headwinds.

BEHAVIORAL FINANCE

According to Hersh Shefrin, the Santa Clara University professor best known for his work on the subject, behavioral finance

is “the application of psychology to financial behavior – the behavior of practitioners.” In his book entitled Beyond Greed and

Fear: Understanding Behavioral Finance and the Psychology of Investing, Shefrin organizes the field of behavioral finance

into three underlying themes: 1) investors rely on rules of thumbs, or heuristics, to make decisions, which ultimately lead to

errors; 2) practitioners’ perceptions of risk and return are highly influenced by how decision problems are framed; and 3)

errors and decision frames result in inefficient markets. In this paper, we will focus primarily on the first theme of behavioral

EXHIBIT 3: 10-YEAR FUND PERFORMANCE VS. INVESTOR PERFORMANCE THROUGH DECEMBER 2013

Fund Name Investment Style Total Return Investor Return Difference

Fund A Multicap Core 9.75% -1.80% -11.55%

Fund B Large Cap Core 8.62% 4.03% -4.59%

Fund C Specialty Equity 2.98% -3.47% -6.45%

Fund D Large Cap Core 7.29% 5.72% -1.57%

S&P 500 Index Broad Market Index 7.41% N/A N/A

Source: Morningstar. Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index.

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finance: heuristics, and how they cause investors to make mistakes. We will then take each of these common errors and

offer some solutions to overcome them.

A few key players have had significant impact on behavioral finance as a field of study. Amos Tversky and Daniel Kahne-

man wrote the article “Judgment Under Uncertainty: Heuristics And Biases” in 1974, which was published in Science. The

paper discussed how individuals rely on heuristics, or shortcuts, to reduce complex tasks to simpler operations. In 1979, the

two were published in Econometrica for their article “Prospect Theory: An Analysis of Decision under Risk.” Richard Thaler

also published a number of important works on the topic. Andrew Lo has also been prolific; among other contributions, he

proposed the idea of the Adaptive Markets Hypothesis, which attempts to reconcile efficient market theory with behavioral

finance. Finally, Jason Zweig, formerly of Money Magazine and currently a columnist with the Wall Street Journal, has man-

aged to put a more popular spin on the subject of behavioral finance and has authored several works on the topic, including

Your Money and Your Brain (2007). While numerous individuals and works are worth referencing and will be referenced

throughout this paper, these five professionals will be cited most often. It is also worth noting that Daniel Kahneman was

awarded the Nobel Memorial Prize in Economic Sciences in 2002 for applying psychology to economic theory, which is a

testament to the credibility behavioral finance has gained over the years.

Behavioral finance is based on the premise that individual investors are not rational. In the 2005 study “Fear and Greed in

Financial Markets: A Clinical Study of Day Traders”, Lo, Repin, and Steenbarger conclude, “To the extent that emotional

reactions ‘short-circuit’ more complex decision-making faculties (e.g. those involved in the active management of a portfolio

of securities), it should come as no surprise that the result is poorer trading performance.” In other words, not properly con-

trolling emotions during the investment process can lead to detrimental mistakes, as illustrated in the investor returns exam-

ples in the first section of this paper (exhibit 1).

This is not to say that heuristics are detrimental in all aspects of life. In many situations they can be very helpful. To refer-

ence an example from Dr. Lo, imagine getting dressed in the morning with five jackets, ten pairs of pants, twenty ties, ten

shirts, ten pairs of socks, four pairs of shoes and five belts available to choose from that day. The result is two million poten-

tial outfits. If it takes one second to evaluate each outfit, it could take over 23 days to get dressed. Obviously taking 23 days

to choose an outfit is not practical, which is why humans need heuristics, or mental short-cuts, to act more quickly. Howev-

er, heuristics and behavioral biases are also what cause investors to purchase mutual funds after a year of strong perfor-

mance or to hold on to losing stocks believing that they will eventually make their money back.

The next section of this paper will discuss some of the heuristics and biases that cause investors to make mistakes. While

countless examples of heuristics and biases are available, this paper will focus on some of the most dominant behaviors,

including anchoring, groupthink and herding behavior, and confirmation bias. It will also address bounded rationality, over-

confidence and bias towards action, and loss aversion and mental accounting.

ANCHORING

Anchoring is the tendency for individuals to focus on a single factor as the primary reason for a decision. For example, in

bargaining, a sales person may lead off with a high price to bias the final price upward. Investors can suffer from anchoring

by believing that a company’s most recent share price is an indicator of its inherent value.

“When I bought something at X and it went up to X and 1/8th, I sometimes stopped buying, perhaps hoping it would come back

down. We’ve missed billions when I’ve gotten anchored. I cost us about $10 billion [by not buying enough Wal-Mart]. I set out to

buy 100 million shares, presplit, at $23. We bought a little and it moved up a bit and I thought it might come back a bit – who

knows? That thumb-sucking, the reluctance to pay a little more, cost us a lot.”

–Warren Buffet (2004 Berkshire Hathaway annual meeting)

Page 5

Russell Fuller used the following exercise to test the anchoring heuristic of individuals:

1. Take the last three digits of your phone number

2. Add 400

3. Answer these two questions:

- Was Attila the Hun defeated in Europe before or after that year (A.D.)?

- What is your best guess of the exact year Attila the Hun was defeated?

Experiments on hundreds of people showed that regardless of the number given in the first part of the experiment, that re-

sponse served as the anchor for the person’s best guess of the year Attila the Hun was defeated (which was 451 A.D.) The

results are as follows:

The experiment confirmed that the first data point given usually serves as an anchor for any subsequent estimate or re-

sponse. In their 1974 paper “Judgment Under Uncertainty: Heuristics And Biases,” Amos Tversky and Daniel Kahneman

demonstrated the anchoring effect by asking subjects to estimate various quantities in terms of percentages. For each

quantity asked, the experimenter would spin a wheel of fortune in the subject’s presence. The subject was then asked to

determine whether their estimated quantity was higher or lower than the number spun on the wheel. The subject then had

to estimate the value by moving upward or downward from the given number. Different arbitrary numbers were given to dif-

ferent groups, and the experiments were able to determine that the number given had a marked impact on the subjects’

estimated value. For example, the subjects were asked to estimate the percentage of African countries in the United Na-

tions. For groups that received a 10 on the wheel of fortune, the median estimate was 25%. For groups that received a 65,

the median response was 45%. Interestingly, the subjects’ accuracy did not improve when incentives were given.

In a similar experiment, Dan Ariely and Drazen Prelec, professors at MIT’s Sloan School of Management, and professor

George Loewenstein of Carnegie Mellon University asked a group of students to bid on items based on the last two digits of

their Social Security numbers (“Coherent Arbitrariness: Stable Demand Curves Without Stable Preferences”). The students

were asked to write their numbers on a sheet of paper. Then, given the items – two different bottles of wine, a cordless

trackball, a cordless keyboard and mouse, a design book and a box of chocolates – they were supposed to use the last two

digits of their Social Security numbers as the price they would have to pay for each of the items. The students were asked

to indicate on the sheet of paper whether they would pay that amount for each of the items. Then, they were asked to write

down the maximum amount they would be willing to pay for each of the items. When the data was analyzed, Ariely discov-

ered that the students with the highest-ending Social Security numbers bid the most for the items, while the students with

the lowest-ending Social Security numbers bid the least. The results were as follows:

When the phone number plus 400 is between: The average guess of when Attila the Hun was defeated is:

400 and 599 629 A.D.

600 and 799 680 A.D.

800 and 999 789 A.D.

1000 and 1199 885 A.D.

1200 and 1399 988 A.D.

Last two digits of Social Security Number Average Bid on Keyboard

Highest 20% within the group of students $56

Lowest 20% within the group of students $16

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After all of the data was taken into account, the experiment determined that students with Social Security numbers ending

in the upper 20 percent of the class placed bids that were 216-346% higher than the students in the lowest 20 percent

group.

Anchoring affects analysts as much as it affects individual investors. Sean Campbell and Steven Sharpe tested their hy-

pothesis that recent past values of data act as an anchor on expert forecasts in their paper entitled “Anchoring Bias in Con-

sensus Forecasts and its Effect on Market Prices.” For example, regarding retail sales, they investigated whether the fore-

cast of January sales growth tended to be too close to the December estimate of sales growth. They found that the average

forecast is weighted too heavily (about 30%) towards the recent past.

Russell Fuller displays a similar concept in his article “Understanding and Applying Behavioral Finance: Behavioral Biases

and the Systematic Mispricing of Securities.” He graphically represents the tendency of analysts to anchor earnings surpris-

es on the previous estimate and shows that each subsequent upward revision is smaller than the previous.

Like the overly narrow confidence bands that individuals set as a result of overconfidence, anchoring tends to lead to sur-

prises. This is because once an investor or analyst forms an original thesis, that person tends to under react to new infor-

mation that may disagree with that thesis. For example, an analyst may determine that a stock is worth $25. Negative news

is released that indicates the stock price should fall 25%. However, anchored by his original forecast, the analyst may not

fully incorporate the negative news and only project a 15% drop. The analyst will be negatively surprised if the stock fully

incorporates the bad news and falls 25%. The same is also true on the upside.

GROUPTHINK/HERDING BEHAVIOR

In 1971, Irving Janis coined the term “Groupthink” to describe “the mode of thinking that persons engage in when concur-

rence-seeking becomes so dominant in a cohesive ingroup that it tends to override realistic appraisal of alternative courses

of action.” According to Janis, “the symptoms of groupthink arise when the members of decision-making groups become

motivated to avoid being too harsh in their judgments of their leaders’ or their colleagues’ ideas.”

One example of social confirmation is the Asch Conformity Experiment, in which Solomon Asch showed that group influ-

ence has a greater effect on individual decision making than simple objective facts (Asch). In Asch’s experiment (exhibit 5),

$0

$20

$40

$60

$80

$100

Q-4 Q-3 Q-2 Q-1 Q0 Q+1 Q+2 Q+3 Q+4

Pric

e

EXHIBIT 4: EARNINGS REVISIONS

Source: Fuller, Russell J. “Understanding and Applying Behavioral Finance: Behavioral Biases and the Systematic Mispricing of Securities.”

CFA Institute (May, 2008).

Initial Earnings Announcement Period Reaction

First Earnings Surprise

Second Earnings Surprise

Page 7

he lined up five students and asked them to determine which line on the right was the same length as the line on the left

(line X). The answer seems obvious: line B. However, unknown to the fifth student, the first four were collaborators with the

experimenter. For the first several rounds, the collaborators gave the correct answer

to Asch’s questions and the fifth student also gave the correct answer. Then the first,

second, third, and fourth students began to give an incorrect answer to the question.

The purpose of the experiment was to determine if the fifth student would give an in-

correct response to a very simple question based on the responses of his or her

peers. The outcome was that at least half of the time a third of the students tested in

the fifth position gave the same wrong answer as the collaborators. Forty percent

gave some wrong answers, and only one-fourth of the students consistently gave the

correct answer despite the wrong answers of the first four students. The experiment

proved that despite being presented with a question that had a very simple, objective

answer, individuals can be pressured to give incorrect responses based on the re-

sponse of the crowd. This is the problem with social confirmation bias. Despite possi-

bly knowing the correct answer, individuals can still be pressured to act with the

crowd, even when the crowd is obviously wrong.

Closely related to social confirmation (because there is an additional bias that results from listening to others who share the

same views) is the herding effect, which is a social behavior that causes individuals to make decisions in line with the rest

of their peer group. One benefit of herding is that for an outside observer, herding can actually be used as an indicator of

market peaks and troughs. However, for investors that suffer from the herding effect, the result is typically acting bearish

when it pays to be bullish and vice versa. In his article “The Herding Effect - Why Investors Are Usually Wrong”, Simon

Maierhofer cites the following examples of how investors are almost always wrong when they follow the crowd:

▪ After two decades of market enthusiasm surrounded the technology sector, 65.7% of individual investors

(according to AAII) were bullish on stocks in March 2000, while 11.1% were bearish. During the same month, the

Nasdaq Composite Index peaked and subsequently fell 74.46% until September 2002.

▪ In October 2002 the Dow Jones and S&P 500 traded around 7,500 and 800, respectively, and 24.5% of investors felt that

stock prices in general would increase, while 54.8% thought that stocks would fall further. Stocks bottomed during that

same month.

▪ Between the 2002 lows and the 2007 all-time highs, the Dow Jones and S&P 500 rallied nearly 50%. When the

stock market peaked on October 16, 2007, 54.6% (a record level) of investors were bullish, while only 16.5% of

investors were bearish.

Most recently, in March 2009, only 18.9% of investors were bullish, while over 70% of investors felt bearish about the stock mar-

ket. From the market bottom in March 9, 2009 through December 31, 2013, the S&P 500 gained over 170%.

12/31/2013 | 1,848.36

3/09/2009 | 6,76.53

Based on this evidence, investor pessimism has peaked at major market bottoms!

X A B C

EXHIBIT 5: ASCH EXPERIMENT LINES

600

1,000

1,400

1,800

2,200

M-09 J-09 S-09 D-09 M-10 J-10 S-10 D-10 M-11 J-11 S-11 D-11 M-12 J-12 S-12 D-12 M-13 J-13 S-13 D-13

S&

P 5

00 P

rice

Leve

l

Source: Bloomberg. Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index. The S&P 500

Price Level does not take into account reinvested dividends, taxes, inflation or investment fees.

EXHIBIT 6: S&P 500 PRICE LEVEL RETURN (MARCH 2009 – DECEMBER 2013)

12/31/2013 | 1,848.36

3/09/2009 | 676.53

Based on this data, investor pessimism often peaks at major market bottoms!

Page 8

CONFIRMATION BIAS

Confirmation bias causes individuals to put too much weight on evidence that confirms their existing or favored beliefs. The bias

may also cause them to search for information that supports their own opinion while ignoring conflicting data. Jason Zweig has

likened the concept to “a compulsive yes-man who echoes whatever you want to believe.” In a recent study comprised of nearly

8,000 participants, experimenters determined that people are almost two times more likely to select information confirming their

prior beliefs and behaviors than they are to consider evidence that challenges those beliefs. Paying credence to this finding, in the

“Investment Committee Decision-Maker Study” published by Vanguard in 2009 for which 111 investment committee members

were surveyed about the structure and dynamics of their committees, half of the survey respondents agreed with the statement,

“My committee tends to seek out information that confirms our preconceptions.”

In 1979, Charles Lord, Lee Ross and Mark Lepper of Stanford University conducted an experiment to evaluate the effects of prior

theories on subsequently considered evidence. In the experiment, subjects who either strongly supported or opposed capital pun-

ishment were exposed to various research and studies on the deterrent efficacy of the death penalty. Some of the research pro-

vided pro-deterrent information and some provided anti-deterrent information. At various points during the experiment, the sub-

jects were asked questions about whether their initial attitudes toward the death penalty had changed as a result of being ex-

posed to the information. It is important to note that all of the studies were fictitious but purported to be true. The data gathered as

a result of the experiment showed a strong support for the hypothesized bias in favor of the study that confirmed subjects’ initial

attitudes. The subjects also reported corresponding shifts in their beliefs as the different studies were presented, which effectively

increased the subjects’ attitude polarizations. Lord, Ross, and Lepper’s work illustrated that once individuals have formed beliefs,

the presentation of new, conflicting information is not very effective in changing those beliefs.

Accepting data that makes us question our beliefs is no doubt a difficult task, but not doing so can lead to errors. Further, precon-

ceived ideas can often cause investors to disregard relevant facts. In his recent Wall Street Journal article “How to Ignore the Yes

-Man in Your Head,” Jason Zweig quotes Staley Cates, president of Southeastern Asset Management (managers of the Longleaf

Partners Funds). Cates recalls, “We’ve made tons of errors like this. A lot of psychological traps can be combated with humility,

but on this one, that doesn’t help.” As an example, Longleaf held a large position in General Motors for too long, letting product

improvements and cost savings “blind [them] to the fact that GM might not make it” without government intervention. The invest-

ment team let the good news about General Motors confirm their initial belief that the company was a sound investment. Ulti-

mately, if they had considered all of the information, GM may not have had such a negative impact on Longleaf’s performance.

The confirming evidence trap precludes individuals from making objective decisions. Preconceived notions can cause investors to

ignore relevant pieces of information that could have a significant impact on their investments. Methods to overcome the confir-

mation bias will be discussed in the Solutions portion of this paper.

BOUNDED RATIONALITY

Bounded rationality explains in part why individuals often make errors in their decision making. The idea is that a human being’s

rationality is limited by the information he has, the cognitive limitations of his mind and the amount of time available to make a

decision. Bounded rationality also explains why individuals become paralyzed by too many options. It recognizes that it is impos-

sible for human beings to have enough data points or time to analyze all of the potentially relevant information in making choices,

which explains why individuals develop behavioral biases and heuristics.

Perhaps the most potent example of the bounded rationality is the explosion of the Space Shuttle Challenger, which took place

on January 28, 1986, 73 seconds after launch. Engineers who worked for the Utah-based NASA contactor Morton Thiokol pro-

“When the facts change, I change my mind. What do you do, sir?”

–John Maynard Keynes

Page 9

duced the solid rocket motors that lifted the space shuttle from the launch pad. The rockets were essentially stacked metal cans

stuffed with highly explosive propellants, and the cans would tend to pull apart where they joined due to the forces of liftoff. Rub-

ber o-rings were used to line the joints in order to keep the propellant from leaking out. The proper function of the o-rings was

necessary to avoid disaster.

The engineers at Thiokol knew that if temperatures were too low at liftoff, the o-rings would stiffen and not provide a secure seal.

However, the data that the engineers presented NASA was not convincing enough to make their case. Pressure was mounting

from all angles, and it would benefit both Thiokol and NASA if the launch was a success. For one, Thiokol wanted to please

NASA because there was a potential for their firm to benefit from upcoming booster rocket contracts. Further, NASA did not want

to appear incompetent – the flight had already been delayed several times and NASA’s budget constraints exacerbated concerns

about losing government funding (from the Report of the PRESIDENTIAL COMMISSION on the Space Shuttle Challenger Acci-

dent). When the engineers plotted the data, they only plotted the incidents of failure (ten out of 24 test flights).

With the data organized in this manner, it is much easier to see that below 60 degrees, 100 percent of flights failed. As most peo-

ple are aware, NASA ignored Thiokol’s official recommendation to not launch and went ahead as planned. The temperature at

Kennedy Space Center on January

28, 1986 was 31 degrees Fahrenheit.

Seven astronauts were killed that

day in the Challenger explosion.

Perhaps more applicable to invest-

ing, Sweden’s privatized social secu-

rity plan can be viewed as an exam-

ple of how bounded rationality can

negatively affect investor returns.

Richard Thaler and Henrik Cronqvist

of the University of Chicago used the

Swedish plan to demonstrate that

0

1

2

3

50° 60° 70° 80°Num

ber

of In

cide

nts

Calculated Joint Temperature (°F)

EXHIBIT 7: FLIGHTS WITH INCIDENTS OF O-RING FAILURE

EXHIBIT 8: FLIGHTS WITHOUT INCIDENTS OF O-RING FAILURE

0

1

2

3

50° 60° 70° 80°Num

ber

of In

cide

nts

Calculated Joint Temperature (°F)

Flights with no incidents of failure

With only the failures plotted, there appeared to be no

correlation between temperature and o-ring failure, i.e.,

the results of the engineers’ tests were inconclusive,

which was enough for NASA to rationalize moving forward

with the launch. However, the data looks dramatically dif-

ferent with all flights plotted.

Exhibit 8 shows that all of the flights that experienced no

incidents of failure occurred at temperatures above 65°F,

and all flights below 65°F failed. Presenting all of the data

may have made a more convincing argument. The next

chart shows the percentage of flights that failed at each

temperature.

100.0%

16.7% 12.5%

83.3% 87.5%

< 60°F 61°-70°F > 71°F

Failure Success

EXHIBIT 9: PERCENTAGE OF FLIGHTS WITH O-RING FAILURES IN TEMPERATURE RANGES

Source for Exhibits 7, 8 & 9: Report of the PRESIDENTIAL COMMISSION on the Space Shuttle Challenger Accident.

Page 10

individuals left to make their own decisions do not always act in their own best interest (Cronqvist and Thaler). In late 2000, Swe-

den moved to a privatized social security plan and allowed eligible working-age adults to select up to five funds from a list of 456

investment choices (any fund meeting certain fiduciary standards was allowed to enter the system). The participants were en-

couraged to actively choose their own portfolios; those who did not would be placed into a default fund. Fund managers were

permitted to set their own fees, but the fees of the default fund were negotiated. Further, funds were allowed to advertise to attract

money. About two-thirds (66.9%) of Swedish social security participants elected to choose their own portfolios. As it turned out,

those who chose their own portfolio rather than investing in the default fund fared worse. The results are as follows.

The mean actively selected portfolio had a higher allocation to equities (specifically Swedish stocks), and less of the equity invest-

ments were indexed, which resulted in a higher management fee. Low-correlation investments such as Hedge Funds and Private

Equity can be helpful during a market decline but the active portfolio lacked exposure to these asset classes. As a result of the

differences, the mean actively selected portfolio underperformed the default fund by almost 10% in the three years following the

launch of the plan. Interestingly, the most popular choice among participants who elected to choose their own funds was in a spe-

cialty fund heavy in Swedish technology and health care stocks. In the five years preceding the plan launch, the Fund rose

534.2% (the highest of the 456 fund choices). However, in the three-year period following the launch, the Fund fell 69.5% (yet

another example of investors making poor timing decisions).

Sweden’s social security example illustrates how individual investors can behave rationally to a point. Several biases are appar-

ent in this example as well; the participants who chose their own investments displayed a familiarity or home bias by having a

larger percentage allocated to Swedish stocks. Additionally, the fact that the best performing fund for the previous five years was

the most popular choice among active participants is testament to the recency bias, or investors putting a heavier weight on the

last piece of information they receive. Further, the design of the plan is an example of frame dependence (how individuals make

decisions according to the framework within which information is received). In the instruction guide the participants received prior

to choosing funds, they were encouraged to actively select their investments while the default fund was positioned as a second-

tier option. As a result, participants were led to believe that they would be better served if they could make their own investment

EXHIBIT 10: COMPARISON OF DEFAULT AND THE ACTIVELY MANAGED PORTFOLIO (OCTOBER 2000 – 2003)

Portfolio Characteristic Percentages

Default Fund Mean Actively Selected Portfolio

Asset Allocation

Equities 82.0% 96.2%

Sweden 17.0% 48.2%

Americas 35.0% 23.1%

Europe 20.0% 18.2%

Asia 10.0% 6.7%

Fixed Income Securities 10.0% 3.8%

Hedge Funds 4.0% 0.0%

Private Equity 4.0% 0.0%

Indexed 60.0% 4.1%

Fee 0.17 0.77

Beta 0.98 1.01

Performance -29.9% -39.6%

Source: Cronqvist and Thaler.

Page 11

choices, which consequently was not the case. The government must have realized the negative impact the plan design had on

participants, because by the time Thaler and Cronqvist’s study was published in 2004, the Swedish government had ended its

efforts to encourage active selection.

Concerning the financial markets, many investors are aware of the “lost decade” – the negative equity market performance (as

represented by the S&P 500 Index) from 2000 to 2009. Consequently, many investors have become weary of the equity market

in general. By only analyzing that decade, investors may come to the conclusion that the equity market is a very volatile system

and that positive performance is hard to achieve – analogous to only looking at the incidents of o-ring failure. However, by analyz-

ing all the historical equity market performance and the P/E ratio, which measures the price paid for a share of a company relative

to the net income earned by that company, investors can gauge the “temperature” of the equity market. When the complete histo-

ry is graphed, a linear relationship forms between the Shiller P/E ratio, which is simply the inflation-adjusted price relative to the

inflation-adjusted average earnings for the previous ten years, and the market performance for the next ten years. The Shiller

price-to-earnings ratio was very high in 1999 so it should be no surprise that the next ten years performed poorly. The current

Shiller P/E ratio is around 25x, and historically this figure has returned in the range of -4.0% to +9.0% annualized over the next

ten years with an average of +4.0%.

These examples all point to the same conclusion: individuals are only rational to a point. In the face of too many choices or not

enough information or time, investors tend to make poor decisions. The previous section of this paper outlines in detail the nega-

tive consequences bounded rationality can have on investors. The following biases and heuristics can all be considered exam-

ples of irrational behavior. The last section of the paper will discuss solutions to identify and modify these behaviors.

-5%

0%

5%

10%

15%

20%

25%

0 5 10 15 20 25 30 35 40 45 50

10-Y

ear

For

war

d S

&P

500

Ret

urn

Shiller P/E Ratio (x)

EXHIBIT 11: P/E RATIO AND THE FORWARD MARKET RETURN (DECEMBER 1935 – 2013)

Source: Bloomberg and Robert Shiller. Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an

index. Dividends and capital gains are reinvested. Performance does not take into account taxes, inflation or investment fees.

Current Shiller P/E Ratio

(December 2013)

December 1999:

10-Year S&P 500 Return: -0.95%

Shiller P/E Ratio: 44.20x

Page 12

OVERCONFIDENCE AND BIAS TOWARDS ACTION

Overconfidence can generally be defined as the belief that one can interpret information better than the average investor and

subsequently choose superior investments. In reality, not everyone can be better than the average investor. According to Glaser

and Weber in their 2003 paper “Overconfidence and Trading Volume,” overconfidence can manifest itself in the following forms:

miscalibration, the better than average effect, unrealistic optimism and the illusion of control.

Consider the following example of overconfidence taken from a study conducted by Russo and Shoemaker from their book Deci-

sion Traps in which a group of subjects was asked to provide 90 percent confidence intervals for a list of general-knowledge

questions with numerical answers.

Russo and Shoemaker’s self-test illustrates the first manifestation of overconfidence, miscalibration, which refers to individuals

setting their confidence intervals too narrow. That is, when asked to determine a range of possible outcomes, they set their high

guesses too low and their low guesses too high. In this example, subjects were asked to give a range of possible answers within

which they were 90 percent confident the correct answer existed. Well-calibrated individuals would be able to correctly bracket

the answers 90 percent of the time, or in the case of this particular study, nine out of ten questions. In their sample of more than

1,000 participates, Russo and Shoemaker determined that less than one percent of the subjects answered nine or more ques-

tions correctly. Most of the subjects missed four to seven of the ten questions. The results led them to conclude that individuals

are more confident of their general knowledge than is warranted. Furthermore, individuals in general are not well calibrated with

respect to their ability.

The better than average effect basically means just that: people think they are better than average. In his book Your Money &

Your Brain, Jason Zweig gives several examples of people judging themselves as better than others. One example is a study by

two psychiatrists at the University of Washington, Caroline Preston and Stanley Harris, in which they asked fifty individuals in the

Seattle area to rate their own “skill, ability, and alertness” the last time they drove a vehicle. About two-thirds of the drivers rated

their abilities as at least as competent as usual, if not extra good or perfect. The irony of the study is that it was conducted at the

hospital because the last time each of the drivers was behind the wheel; they ended up in an ambulance. Zweig goes on to re-

count that according to the Seattle police department, 10% of the drivers polled admitted they may have been partially responsi-

ble for the accident; 68% were directly responsible for their crashes; and 44% would ultimately face criminal charges. The disper-

sion between the drivers’ perceived driving abilities and their actual drive abilities can be seen in the following chart.

“It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so.”

–Josh Billings, 19th century humorist

Self-Test of Overconfidence 90% Confidence Interval

Lower Upper

1. Martin Luther King’s age at Death

2. Length of the Nile River (in miles)

3. Number of countries in OPEC

4. Number of books in the Old Testament

5. Diameter of the moon (in miles)

6. Weight of an empty Boeing 747 (in pounds)

7. Year in which Wolfgang Amadeus Mozart was born

8. Gestation period of an Asian elephant (in days)

9. Air distance from London to Tokyo (in miles)

10. Deepest known point in the ocean (in feet)

Page 13

Closely related to the better than average effect is excessive optimism, which is the tendency for individuals to exaggerate their

own abilities. In 1998, Werner De Bondt (“Investor Psychology And World Equity Markets”) conducted a study related to exces-

sive optimism and overconfidence. Among his findings were that investors were excessively optimistic about the stocks that they

owned but not about the Dow Jones Industrial Average Index in general. In a separate survey of 300 professional mutual fund

managers conducted in 2006, 74% of the sample believed themselves to be above average at their jobs. Of the remaining 26%,

the vast majority thought that they were average at their jobs, but almost no one believed themselves to be below average. In the

survey, the managers submitted comments like, “I know everyone says they are, but I really am [better than average]!” (Montier).

Excessive optimism is exacerbated by the illusion of control (individuals believe they can influence the outcome of certain events

even though their actions have no effect on what actually happens). In his book Behavioral Finance and Wealth Management:

How to Build Optimal Portfolios That Account for Investor Biases, Michael Pompian references a 1975 study conducted by Ellen

Langer, Ph.D. of Harvard University on the illusion of control. Langer defines the illusion of control as the “expectancy of a per-

sonal success probability inappropriately higher than the objective probability would warrant.” As an example, in one study, Lang-

er observed that people who were allowed to choose their own numbers in a hypothetical lottery game were willing to pay a high-

er price for the ticket than those who were assigned random numbers. In reality, paying more for a lottery ticket because one can

choose the numbers makes no sense because the outcome is completely random. But because participants were given a sense

of control, they believed their chance of winning was greater. Andrew Breinholt and Lynnette Dalrymple built on Langer’s initial

study by examining the effects of desire for control and belief in luck on the illusion of control bias. They did so by manipulating

the level of the participants’ involvement and varying the sequence of outcomes. What they hypothesized was that participants

with a high desire for control would wager more on the outcome of a card game when given more involvement in the process.

The experiment was as follows: 218 participants were randomly assigned to either the high involvement group or the low involve-

ment group and to either the descending or random outcome sequence group. Those in the high involvement group were allowed

to shuffle and deal their own cards (via computer simulation); conversely, in the low involvement group, the computer shuffled

and dealt the cards. Each group played fourteen hands of “Red & Black,” a game in which four random cards are placed face

down on the computer screen, and the object is to wager on whether a chosen card matches a selected target color. The results

proved that participants in the high involvement group wagered more than those in the low involvement group, even though the

odds of winning each hand were 50/50. Even though the participants were informed during the instructions of the game that the

odds of winning each hand were exactly 50/50, those who believed they had more control over the outcome by being able to

shuffle their own cards felt that they had a better chance of predicting the correct outcome.

Perhaps the best real world example of overconfidence is the story of Long-Term Capital Management (LTCM). The firm was

hailed as the most impressive hedge fund in history when it was founded in 1993. LTCM was founded by John Meriwether, the

famously successful bond arbitrageur, and the investment team was remarkable, including a number of academia’s elite and two

Nobel Prize-winning economists (Myron Scholes and Robert Merton). From 1994 to 1998 the firm was very successful, generat-

ing impressive returns for its investors. However, when Russia defaulted on its debt in 1998, the bets that the firm had made went

67%

10%

68%

44%

0%

20%

40%

60%

80%

Believe they are above averagedrivers

Admitted they may have beenpartially responsible for the

accident

Were directly responsible fortheir crashes

Would ultimately face criminalcharges

EXHIBIT 12: PERCENTAGE OF POLLED HOSPITAL PATIENTS WHO…

Source: Preston and Harris.

Page 14

south. Refusing to admit to their mistakes, the team at LTCM continued to defend their positions. The academics at LTCM were

so confident in the models that they had constructed that even when all other evidence pointed to the contrary, they remained

convicted in their decisions. Ultimately, Long-Term Capital Management lost $3 billion in 1998 and needed to be bailed out by a

group of Wall Street’s most powerful executives led by the New York Federal Reserve Bank.

Overconfidence has many implications for investors, but the ultimate consequences are excessive trading and undiversified port-

folios. Brad Barber and Terrance Odean studied the effects of turnover on account performance in their study “Trading is Hazard-

ous to Your Wealth: The Common Stock Investment Performance of Individual Investors.” Turnover is often described as the

amount of trading in a portfolio, so portfolios with higher turnover usually have higher trading costs and other associated fees.

Gross return is the portfolio performance not including those costs and net return is the portfolio performance including those

costs. In this paper, they found a direct relationship between higher turnover and lower net returns for individuals.

In another study conducted by Brad Barber and Terrance Odean in 2001, it was determined by using account data for over

35,000 households from a large discount brokerage firm from February 1991 through January 1997 that men traded 45% more

than women, which reduced their net returns by 2.65% a year. Women, although less susceptible to overconfidence and exces-

sive trading, underperformed by 1.72% a year during this same time frame (“Boys will be boys”). In the Solutions section of this

paper, we will suggest methods to circumvent overconfidence to attempt to avoid the negative impact this heuristic has on inves-

tor returns.

LOSS AVERSION AND MENTAL ACCOUNTING

Two important works were published on loss aversion and mental accounting, both of which were significant in advancing the

field of behavioral finance. In 1979, Daniel Kahneman and Amos Tversky wrote “Prospect Theory: An Analysis of Decision under

Risk.” Then in 1995, Shlomo Bernartzi and Richard Thaler authored “Myopic Loss Aversion and the Equity Premium Puzzle.”

These papers will be cited often throughout this section.

In prospect theory, loss aversion refers to the tendency for individuals to be more sensitive to a loss than they are to a gain. More

specifically, Kahneman and Tversky estimated that the pain from a loss is about twice as strong as the joy associated with a gain.

In other words, the dissatisfaction of losing $50 is about twice the utility of gaining $50. The following graph illustrates the impact

of loss aversion on investors. In a real world context, loss aversion causes investors to hold on to losing stocks for too long.

““In human decision making, losses loom larger than gains."

–Kahneman and Tversky

0

5

10

15

20

25

1 2 3 4 5 Avg. Individual

Ann

ualiz

ed R

etur

n %

Turnover Gross Return Net Return

EXHIBIT 13: RETAIL INVESTOR’S TURNOVER AND RETURN (FEBRUARY 1991 – JANUARY 1997)

Source: Brad Barber and Terrance Odean, “Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors” The

Journal of Finance (April 2000). Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index.

Lower Turnover ———————————-—————————— Higher Turnover

Decreasing

Net Return

Increasing

Turnover

S&P 500

Index Index Fund

Net Return Given Up By High Turnover

Page 15

One reason investors tend to hold onto losers is to avoid having to admit that they made a mistake. Daniel Kahneman explains,

“When you sell a loser, you don’t just take a financial loss; you take a psychological loss from admitting you made a mistake. You

are punishing yourself when you sell.” In fact, Jason Zweig cites a study in his book Your

Money and Your Brain that found Finnish investors are 32% less likely to sell a stock after

the price has fallen sharply than after a similar rise in price. Further, Israeli money manag-

ers hold onto losing stocks more than twice as long as they hold winners (an average of

55 days). This behavior is referred to as the disposition effect. Terrance Odean conducted

a study that observed investors’ behavior month-by-month in his article “Are Investors

Reluctant to Realize Their Losses.” He determined that investors were only willing to sell

their losers more than their winners in December. Every other month, they continued to

sell their winners and realize gains. While this behavior postpones regret, it also lowers

performance overall. By selling their winners and holding onto losing stocks, investors

increase their taxes and lower their future returns. Odean found that in the 10,000 inves-

tors he observed, the winning stocks that were sold (on average) went on to outperform

the losing stocks that investors held.

Underperformance due to loss aversion has been observed in other contexts. Kahneman, Tversky, Thaler, and Schwartz (1997)

evaluated the effect of myopic loss aversion on an investment’s performance to test Bernartzi and Thaler’s (1995) original thesis

that investors who are willing to go a longer time horizon without evaluating their investments are able to accept more risk (i.e.,

choose stocks over bonds). In this context, myopic loss aversion is the combination of a greater sensitivity to losses than gains

and a tendency to evaluate outcomes frequently. They found that investors who evaluated their accounts more frequently under-

performed over the long run; more precisely, they determined that “providing investors with frequent feedback about their out-

comes is likely to encourage their worst tendencies.”

The subjects of the experiment were 80 students from the University of California at Berkeley and each student was asked to im-

agine that he or she was a portfolio manager for a small college fund. The subjects were given a portfolio of 100 shares that they

could allocate between two investments: Fund A and Fund B. Fund A (Bond Fund) had the five-year return and volatility charac-

teristics of an aggregate bond index while Fund B (Stock Fund) had the characteristics of a value-weighted stock index over six

and a half weeks. The subjects were not told that Fund A was composed of bonds and Fund B was composed of stocks in order

to avoid preconceived biases. The subjects were also told that they would be paid at the end of the experiment based on the per-

formance of their portfolios (performance-based payment was offered to ensure that the subjects took the task seriously). The 80

students were then split into random groups and asked to evaluate their portfolio either “monthly,” “yearly,” or “five-yearly,” which

meant that they were asked to reevaluate their portfolio 200 times, 25 times, or five times, respectively, over a simulated 200

month period. The experimenters found that the subjects that evaluated their investments the most often performed the worst,

due to their portfolios holding less of Fund B (Stock Fund) than those of their counterparts. The experiment showed that not only

do frequent portfolio evaluations hinder performance, but such behavior also causes investors to become more risk averse.

EXHIBIT 14: KAHNEMAN’S VALUE

UTILITY GRAPH

-2

2

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

55.0%

30.7% 28.6%

45.0%

69.3% 71.4%

0%

20%

40%

60%

80%

Monthly Yearly Five-Years

Fund A (Bond Fund) Fund B (Stock Fund)

Source: Thaler, Tversky, Kahneman, Schwartz. “The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test.” The Quarterly Journal of

Economics (May 1997).

EXHIBIT 15: MEAN ALLOCATION IN SIMULATED INVESTMENT PORTFOLIO BASED ON FEEDBACK GROUP

Page 16

Another impact of loss aversion is that it can paralyze investors. Once investors realize they made one mistake, they are afraid to

act for fear that they will make another one. Jason Zweig refers to this effect as “portfolio paralysis.”

Closely related to loss aversion is mental accounting. According to Richard Thaler, mental accounting is “the set of cognitive op-

erations used by individuals and households to organize, evaluate and keep track of financial activities.” The following examples

of mental accounting were taken directly from Thaler’s paper “Mental Accounting Matters.”

A former colleague of mine, a professor of finance, prides himself on being a thoroughly rational man. Long ago he

adopted a clever strategy to deal with life's misfortunes. At the beginning of each year he establishes a target donation

to the local United Way charity. Then, if anything untoward happens to him during the year, for example an undeserved

speeding ticket, he simply deducts this loss from the United Way account. He thinks of it as an insurance policy against

small annoyances.

A few years ago I gave a talk to a group of executives in Switzerland. After the conference my wife and I spent a week

visiting the area. At that time the Swiss franc was at an all-time high relative to the US dollar, so the usual high prices in

Switzerland were astronomical. My wife and I comforted ourselves that I had received a fee for the talk that would easi-

ly cover the outrageous prices for hotels and meals. Had I received the same fee a week earlier for a talk in New York

though, the vacation would have been much less enjoyable.

A friend of mine was once shopping for a quilted bedspread. She went to a department store and was pleased to find a

model she liked on sale. The spreads came in three sizes: double, queen and king. The usual prices for these quilts

were $200, $250 and $300 respectively, but during the sale they were all priced at only $150. My friend bought the king

-size quilt and was quite pleased with her purchase, though the quilt did hang a bit over the sides of her double bed.

Mental accounting relates to loss aversion in that it explains how investors view gains and losses. For example, investors could

be loss averse over changes in total wealth, changes in the value of their stock portfolios alone or changes in just a few stocks

(Barberis and Huang). Thaler determined that in general, individuals prefer to aggregate losses and segregate gains. Mental ac-

counting may also explain why investors prefer dividend-paying stocks to companies that repurchase shares. Scholars have sug-

gested that investors like dividends because the regular cash payment provides a self-control mechanism: spend the dividends

and leave the principal alone so that the dividend acts as an allowance (Shefrin and Statman). If firms repurchase their shares,

investors would miss out on the income stream and have to dip into principal to meet expenditures.

SOLUTIONS

Investors cannot fight human nature, but steps can be taken to keep detrimental behavior in check. The first step is for investors

to admit they are fallible. Once investors acknowledge that they do not know what is going to happen for certain, processes can

be put in place to control the behavioral and emotional pitfalls of investing.

To avoid distractions investors need to focus on the big picture, which starts with a fundamentally sound investment strategy.

Without a definable, repeatable process, a positive outcome is just dumb luck. In his 1999 commencement address to New York

University, Treasury Secretary Robert Rubin outlined his four principles to decision making: “First, the only certainty is that there

is no certainty. Second, every decision, as a consequence, is a matter of weighing probabilities. Third, despite uncertainty we

must decide and we must act. And lastly, we need to judge decisions not only on the results, but on how they were made.”

Page 17

Forecasting and attempting to time the market are not fundamentally sound investment strategies. Nobel laureate William Sharpe

found that market timers have to be correct over 70 percent of the time just to keep pace with buy-and-hold investors. Since un-

certainty is inevitable, a fundamentally sound investment strategy should alleviate the necessity to be correct 70 percent of the

time. Moreover, we consider time as an asset. The longer the investment horizon, the more a portfolio can withstand temporary

setbacks in performance.

Most importantly, investors do not have to fight the fact they are human. Portfolios can and should be structured keeping behav-

ioral finance in mind. First, investors should keep savings and emergency funds separate from investment funds; this is simple

mental accounting. Savings and emergency funds are safe funds (i.e., cash). Investment funds are where risk should be taken,

and the amount of risk depends on the investor’s goals, objectives and constraints. Additionally, every investment strategy needs

a relief valve. A mid-mission oxygen tank rupture caused the Apollo 13 space craft to abort its mission before landing on the

moon. The only thing that saved the crew was a relief valve built into the tank which mitigated the damage and allowed the crew

to make it safely back. Investors cannot be right all the time, but they can build relief valves into their portfolios to allow them to

sleep at night. Consider the investor lifecycle chart (exhibit 16) as an example of a sound investment plan. The investor starts off

just out of school and his immediate goal is to build a cash reserve to cover emergencies. Once this reserve is built, the investor

can begin the accumulation phase by allocating to both strategic portfolios (relatively static portfolios based on long-term ex-

pected return characteristics) and tactical portfolios (more active portfolios that attempt to take advantage of perceived market

imbalances that create attractive short-term investment opportunities). However, throughout the life cycle, the cash reserve and

tactical allocation are considered the relief valves. We consider cash a relief valve in a mental accounting sense because it is al-

ways available to pay essential expenses, such as the mortgage and tuition. The tactical allocation is a relief valve because it can

satiate an investor’s cravings for action and play to an investor’s illusion of control and excessive optimism. Investors should allo-

cate more aggressively in the early stages of their lifecycle and gradually move to more conservative portfolios as they age. Addi-

tionally, tactical portfolios will have a proportionally higher weighting in the accumulation phase because investors have more time

to make up for the inevitable mistakes that result from investing tactically. Throughout the chart below, more color reflects less

aggressive investments and less color reflects more aggressive. Once the investor is approximately five to ten years from retire-

ment, he should begin the distribution phase and invest accordingly. This phase should include less aggressive strategic and tac-

tical allocations as well as more cash.

©2013 Stringer Asset Management.

EXHIBIT 16: HYPOTHETICAL CHART OF AN INVESTOR’S ALLOCATION LIFECYCLE

Page 18

ANCHORING

Anchoring is the tendency for individuals to focus on a single factor as the primary reason for a decision. This behavior was illus-

trated earlier in the paper with the example of using one’s phone number as a basis for determining when Attila the Hun was de-

feated. Investors tend to anchor their purchase price when judging the value of an investment. For example, if an investor pur-

chased a stock at $75 and the price then falls to $50, the investor believes the true value is $75 and waits to “break even” before

selling. However, the company may have been overvalued at the time of purchase, or a fundamental deterioration may have tak-

en the place causing the price to fall. The purchase price is an arbitrary number.

To avoid this pitfall, investors should seek out more information that is relevant to their decision. In their article entitled “Behavioral

Finance Can Teach Investors How to Avoid Mistakes,” the investment management firm Investor’s Capital Management recom-

mends overcoming the anchoring bias by evaluating each investment as if it were a new purchase. Knowing what they know

about the company today and disregarding the price at which they purchased the stock, would an investor buy the stock at to-

day’s price? If the answer is no, then the investor should sell.

Another downfall of waiting to break even is the opportunity cost to the investor. While the investor is sitting in a losing stock wait-

ing to break even, that capital could be deployed elsewhere in an investment that may be more profitable. Further, if held in a tax-

able account, there may be a tax benefit to taking the loss. Investors should consider all of their options and determine if holding

onto a losing stock is the best use of their money.

GROUPTHINK/HERDING BEHAVIOR

We demonstrated groupthink and herd behavior with the Asch Line Experiment example, in which a participant gave the wrong

answer to a question, despite the correct answer being obvious, due to the answers given by the rest of the participants. At the

investor level, several examples were given to illustrate how historically investor pessimism has peaked at major market bottoms,

and vice versa. While following the crowd may feel more comfortable and safe, it seems that in such cases, acting differently has

a more favorable outcome.

When attempting to overcome groupthink and herd behavior, it is important for investors to remember that stock prices can be

pushed up due to optimism or speculation and not fundamentals. An example is the dot com bubble, during which the stock pric-

es of companies with no earnings and poor balance sheets rose rapidly. Needless to say, when the bubble burst, many investors

who jumped on the dot com bandwagon were burned.

Investors may want to remember Warren Buffett’s quote before succumbing to herd behavior: “We simply attempt to be fearful

when others are greedy and to be greedy only when others are fearful.” Additionally, doing the appropriate amount of research

and due diligence prior to investing is necessary and the only way to know if prices are rising or falling as a result of a company’s

fundamentals or irrational investors buying or selling in droves. Finally, investors cannot let a strong market lead to excessive risk

taking, or a weak market lead to excessive fear.

CONFIRMATION BIAS

Confirmation bias is the idea that investors seek out and weigh too heavily evidence that agrees with their thesis. The Vanguard

study referenced earlier found that half of the investment committee members polled agreed that they seek opinions and evi-

dence that confirms their initial idea. The best solution to this heuristic is for investors to find their own Charlie Munger. Munger is

Page 19

the Vice-Chairman of Berkshire Hathaway and often described by Warren Buffett as his “partner.” To elaborate on this point, con-

sider the following quote taken from a 1996 Forbes article by Lenzner and Fondiller: “Buffett is the stock picker while Munger is

the doubter, the skeptic, the devil’s advocate, against whom Buffett tests his ideas. The simple fact is that you can’t tell whether

an idea is likely to work unless you consider all the possible negatives.” Listening to a devil’s advocate helps to overcome the in-

herent human biases by forcing investors to consider the other side of the argument.

BOUNDED RATIONALITY

The concept of bounded rationality is that the rationality of humans is limited to the available information, their cognitive limita-

tions, and the finite time in which they have to decide. Because it is impossible for human beings to have enough data points or

time to analyze all of the potentially relevant information in making choices, individuals develop behavioral biases and heuristics.

These short-cuts tend to result in satisfactory decisions rather than optimal decisions, and in some cases, the decisions are actu-

ally detrimental, as we demonstrated with the Challenger and Swedish Social Security plan examples.

Because investors are human, the solution to overcome bounded rationality cannot be to gather all data points before making

decisions. Attempting to do so can lead to what some call “paralysis by analysis,” when in fact, studies have actually concluded

that past a certain point, more information does not necessarily lead to more accurate decision-making; it does, however, in-

crease confidence. Collin Powell follows the rule of thumb, “Use the formula P=40 to 70, in which P stands for the probability of

success and the numbers indicate the percentage of information required.” In other words, investors must acknowledge the fact

that they cannot know everything, but they must accumulate a significant amount of relevant information to make successful deci-

sions. With respect to investment decision-making, investors cannot possibly gather all the necessary data points. Mohnish

Pabrai of Pabrai Funds follows a checklist of over 60 relevant questions before he makes a trade, and he never decides to buy a

security during trading hours. Simple tasks like this can serve investors extremely well in the long-run by avoiding small but far-

reaching mistakes.

Aside from considering all relevant information, investors can avoid suffering from bounded rationality by not making decisions

under stress. A 2004 study by Mark Staal at NASA determined that under stress, judgment and decision making become more

rigid, as individuals scan fewer alternatives. Understanding how stress affects decision making may allow them to avoid making

irrational decisions, like going to cash after a downturn and missing the subsequent market recovery.

OVERCONFIDENCE/BIAS TOWARDS ACTION

As we stated previously, overconfidence can generally be defined as the belief that one can interpret information better than the

average investor and subsequently choose superior investments. Needless to say, not everyone can be better than average.

However, suffering from overconfidence can lead to poor decision-making and the tendency to take action even when action is

not necessary.

The first way to avoid the negative effects of overconfidence is for investors to admit that they do not know for certain what is go-

ing to happen. Even seasoned economists make inaccurate forecasts. Second, consider multiple scenarios. Without using multi-

ple scenarios, investors are left trying to defend their original thesis instead of trying to understand the current environment. The

psychological cover given by considering several scenarios allows investors to react. For example, when building a portfolio, it is

important to diversify assets for different types of market environments. Or, as we mentioned before, building a cash reserve and

including tactical allocations as relief valves can save investors from making poor investment decisions.

Probability-based asset management is another solution to the overconfidence pitfall, especially when purchasing individual secu-

Page 20

rities. When doing so, investors should avoid price targets and focus on expected value (the sum of the potential prices multiplied

by the probability of the stock hitting each price). This method takes into account multiple scenarios and takes away the potential

detrimental effect of anchoring on one specific price.

Along the same lines, keeping a detailed investment journal can help investors track their investment decisions and whether the

desired outcome was achieved. Keeping an investment journal introduces some intellectual honesty into the investment process.

Tracking the actual results of investment decisions may help counteract overconfidence and may humble some investors. Fur-

thermore, a journal can help identify areas of the investment management process that an investor may be weak in that could be

outsourced and allow the investor to specialize in areas they perform well.

Finally, investors should avoid excessive trading. Terrance Odean’s study proved that excessive portfolio trading led to lower per-

formance. Doing the appropriate amount of leg work prior to investing can eliminate the bias toward selling an investment for the

wrong reasons.

LOSS AVERSION AND MENTAL ACCOUNTING

In prospect theory, loss aversion refers to the tendency for individuals to be more sensitive to a loss than they are to a gain. More

specifically, the pain of a loss is in some cases more than two times greater than the good feeling associated with a gain. When

investors combine the pain of loss with a bias towards action, investors can make poor decisions. Equity oriented investors

should understand how time and probabilities relate; this does not affect the fixed income market as the probabilities of a loss are

much smaller. For instance, on a daily basis the S&P 500 Index is just as likely to return a positive number as it is a negative.

However, if investors expand their timeframe, the probabilities of a positive return are greatly increased.

Combining the probabilities that the S&P 500 Index will be positive or negative with the sensitivity to a loss proves the preceding

statements true. The discontent and impact felt by investors with a negative return on a daily basis is almost twice the satisfaction

of a gain in their investment portfolio. The probabilities and resulting utility for equity investors does not turn to their favor until the

timeframe is expanded to about one year.

0%

20%

40%

60%

80%

100%

Daily Weekly Monthly 1yr 3yr 5yr 10yr

Pro

babi

lity

EXHIBIT 17: S&P 500 INDEX PROBABILITIES AND TIME

Source: Bloomberg.

Probability Positive

Probability Negative

Page 21

Investors cannot let their egos get in the way of their investment decisions. As we wrote earlier in the paper, Daniel Kahneman

said, “When you sell a loser, you don’t just take a financial loss; you take a psychological loss from admitting you made a mistake.

You are punishing yourself when you sell.” Investors should not be afraid to sell a losing position and should ignore the purchase

price when determining whether to sell an investment (for tax reasons, purchase price may be a factor). Saying, “I’ll wait until this

gets back to even,” can result in missing other more profitable investment opportunities. The pain of a loss and resulting reluc-

tance to sell an underperforming investment can cause investors to hold onto a losing position for too long while realizing the prof-

its from winning positions.

Furthermore, Thaler determined that the frequency of looking at an account can affect the portfolio’s return because investors

tend to panic and trade too frequently when they see a loss in their accounts (exhibit 19). Investors should understand that the

frequency of looking at an account can have an inverse relationship with portfolio performance, and then acknowledge that fact

by not looking at the account too often. Obviously investors need to monitor portfolios to make sure they are still in line with their

goals and constraints, but checking prices on a daily basis should be avoided in general. The panic from seeing losing positions

may result in investors allocating away from underperforming assets into investments that have recently performed better. This

may cause problems as the allocation moves too far away from the appropriate allocation given the investor’s goals and limita-

tions.

EXHIBIT 18: S&P 500 INDEX PROBABILITIES AND IMPACT ON INVESTORS

Probability Impact on Investors Overall Utility

Positive for one day 52.73% x1 52.73%

Negative for one day 46.45% x2 92.90%

Positive for one month 59.17% x1 59.17%

Negative for one month 40.69% x2 81.39%

Positive for one year 76.94% x1 76.94%

Negative for one year 23.06% x2 46.11%

Data Source: Bloomberg. Past performance is not indicative of future results. Indices are unmanaged; you cannot invest directly into an index. Utility is a measure of the relative satisfaction from, or desirability of, consumption of various goods and services.

0%

20%

40%

60%

80%

100%

Daily Weekly Monthly 1yr 3yr 5yr 10yr

Pro

babi

lity

EXHIBIT 16: S&P 500 INDEX PROBABILITIES AND TIME

Source: Bloomberg.

-0.04

0.00

0.04

0.08

0.12

0 2 4 6 8 10 12 14 16 18

Pro

spec

tive

Util

ity

Length of Evaluation Period (in months)

EXHIBIT 19: THE UTILITY OF PORTFOLIO EVALUATIONS

Source: Benartzi & Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle” The Quarterly Journal of Economics, (February,1995).

Fixed Income

Equity

13 months = Same Utility

Page 22

CONCLUSION

Behavioral finance is a subject that investors can no longer ignore in their investment process. While human behavior is not often

easily changed, becoming aware of human behavior is the first step. Investors are fallible but they can mitigate their behavior so

that it does not prohibit them from achieving their investment goals. This can be accomplished through learning from their mis-

takes, diversifying and segregating their money into safe and investment funds, and being patient with the market. Additionally,

creating mental checklists can help them stay on track as they invest. As the ancient Chinese philosopher Laozi wrote “When I let

go of what I am, I become what I might be.” Investors should try to maximize their efficiency and success in the market, and to do

this, they must understand and apply behavioral finance to their daily lives.

DISCLOSURES

Any forecasts, figures, opinions or investment techniques and strategies explained are Stringer Asset Management LLC’s

as of the date of publication. They are considered to be accurate at the time of writing, but no warranty of accuracy is giv-

en and no liability in respect to error or omission is accepted. They are subject to change without reference or notification.

The views contained herein are not be taken as an advice or a recommendation to buy or sell any investment and the

material should not be relied upon as containing sufficient information to support an investment decision. It should be not-

ed that the value of investments and the income from them may fluctuate in accordance with market conditions and taxa-

tion agreements and investors may not get back the full amount invested. Past performance and yield may not be a

reliable guide to future performance. Current performance may be higher or lower than the performance quoted.

Data is provided by various sources and prepared by Stringer Asset Management LLC and has not been verified or audit-

ed by an independent accountant.

The Dow Jones Industrial Average Index is a price-weighted average of 30 significant stocks traded on the New York

Stock Exchange and the NASDAQ.

The S&P 500 Index consists of 500 of the largest stocks in the U.S. stock market. It is a market value weighted index

(stock price times number of shares outstanding after float adjustment), with each stock’s weight in the index proportion-

ate to its market value. You cannot invest directly in an index.

The Barclays U.S. Aggregate Bond Index is a broad fixed income index that includes all issues in the Government/Credit

Index and mortgage-backed debt securities. Maturities range from 1 to 30 years with an average maturity of nearly 5

years.

Page 23

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