Chapter 12 – Some Lessons from Capital Market History
Chapter 12
SOME LESSONS FROM CAPITAL MARKET HISTORY
CHAPTER WEB SITES
Section Web Address
Introduction www.mhhe.com/rwj
12.1 finance.yahoo.com
www.marketwatch.com
12.2 www.globalfinancialdata.com
bigcharts.marketwatch.com
12.4 www.robertniles.com
www.morningstar.com
12.6 www.investorhome.com
CHAPTER ORGANIZATION
12.1 Returns
Dollar Returns
Percentage Returns
12.2 The Historical Record
A First Look
A Closer Look
12.3 Average Returns: The First Lesson
Calculating Average Returns
Average Returns: The Historical Record
Risk Premiums
The First Lesson
12.4 The Variability of Returns: The Second Lesson
Frequency Distributions and Variability
The Historical Variance and Standard Deviation
The Historical Record
Normal Distribution
The Second Lesson
2008: The Bear Growled and Investors Howled
Using Capital Market History
More on the Stock Market Risk Premium
12.5 More about Average Returns
Arithmetic versus Geometric Averages
Calculating Geometric Average Returns
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Chapter 12 – Some Lessons from Capital Market History
Arithmetic Average Return or Geometric Average Return?
12.6 Capital Market Efficiency
Price Behavior in an Efficient Market
The Efficient Markets Hypothesis
Some Common Misconceptions about the EMH
The Forms of Market Efficiency
12.7 Summary and Conclusions
ANNOTATED CHAPTER OUTLINE
1. Returns
A. Dollar Returns
Income component – direct cash payments such as dividends or
interest
Price change – loosely, capital gain or loss
Total dollar return = income component + prince change
The return is unaffected by the decision to sell or hold securities.
Lecture Tip: The issues discussed in this section need to be
stressed. Many students feel that if you don’t sell a security, you
won’t have to consider the capital gain or loss involved. (This is a
common investor’s mistake – holding a loser too long because of
reluctance to admit a bad decision was made.) Point out that non-
recognition is relevant for tax purposes – only realized income
must be reported. However, whether or not you have liquidated the
asset is irrelevant when measuring a security’s pre-tax
performance. Also, we need to annualize total returns so that we
can compare the performance of different securities available in
the market.
B. Percentage Returns
Percentage return = dollar return / initial investment
= dividend yield + capital gains yield
Dividend yield = Dt+1 / Pt
Capital gains yield = (Pt+1 – Pt) / Pt
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Chapter 12 – Some Lessons from Capital Market History
2. The Historical Record
The following are the basis for the nominal pretax rates of return reported by
Ibbotson and Sinquefield and presented in the figures throughout the chapter:
Large-company stocks – S&P 500 index, which contains 500 of the largest
companies in terms of total market value in the U.S.
Small-company stocks – Smallest 20% of stocks listed on the New York Stock
Exchange based on market value of outstanding stock
Long-term corporate bonds – High quality corporate bonds with 20 years to
maturity
Long-term government bonds – Portfolio of U.S. government bonds with 20
years to maturity
U.S. Treasury bills – Portfolio of T-bills with a three-month maturity
A. A First Look
Over the time period studied in this chapter, small-company stocks
performed the best. U.S. treasury bills performed the worst.
B. A Closer Look
The variability in returns is much larger for small-company stocks
than U.S. treasury bills.
3. Average Returns: The First Lesson
A. Calculating Average Returns
The arithmetic average return equals the sum of the observed
returns, divided by the number of observations
Lecture Tip: Some students may not recall their statistics, so a
brief review is in order. Security returns are examples of random
variables – categories of numbers for which in any particular
instance more things can happen than will happen – and the things
that can happen have an associated probability of occurrence.
Random variables are typically characterized by their probability
distributions (i.e., a graph, a table or function that relates the
potential values of the random variable to its associated
probabilities) along with measures of its central tendency and
dispersion (the deviation from that central tendency). The normal
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Chapter 12 – Some Lessons from Capital Market History
distribution is a common probability distribution; mean, median,
and mode measure central tendency, while variance and standard
deviation are common measures of dispersion.
B. Average Returns: The Historical Record
Lecture Tip: Suppose some risk-averse student asks what the
worst average annual return and total return figured on a calendar
year basis would have been for someone with five-, ten- and
twenty-year holding periods for large company stocks. What would
be the best holding period returns? Consider the 1926-2010
period:
Best 5-year period 1995 – 1999 28.63% 143.13%
Worst 5-year period 1928 – 1932 -8.22% -41.09%
Best 10-year period 1949 – 1958 21.32% 213.18%
Worst 10-year period 1965 -1974 2.48% 24.79%
Best 20-year period 1942 – 1961 17.81% 356.29%
Worst 20-year period 1929 – 1948 5.99% 119.89%
Note that over long periods of time, even the worst period still has
a positive average annual return.
C. Risk Premiums
Using the T–bill rate as the risk-free return and aggregate common
stocks as an average risk, define excess return as the difference
between an average-risk return and the return on T-bills.
Risk premium – reward for bearing risk, the difference between a
risky investment return and the risk-free rate.
D. The First Lesson
Risky investments earn a risk premium. For large company stocks,
the average annual risk premium has been approximately 8.2%
since 1926. For smaller (and presumably riskier) firms, the average
annual risk premium has been 13.0% over the same period.
4. The Variability of Returns: The Second Lesson
A. Frequency Distributions and Variability
Variance and standard deviation are the most commonly used
measures of volatility.
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Chapter 12 – Some Lessons from Capital Market History
B. The Historical Variance and Standard Deviation
Variance – the average squared deviation between actual returns
and their mean
Standard Deviation – square root of variance
Lecture Tip: Occasionally, students ask why we include the above-
mean returns in measuring dispersion, since these are desirable
from the investor’s viewpoint. This question provides a natural
springboard for a discussion of alternative variability measures.
Here we discuss semivariance as an alternative to variance.
In Portfolio Selection (1959), Harry Markowitz states:
“Analyses based on [semivariance] tend to produce
better portfolios than those based on [variance].
Variance considers extremely high and extremely
low returns equally undesirable. An analysis based
on [variance] seeks to eliminate extremes. An
analysis based on [semivariance] on the other
hand, concentrates on reducing losses.”
Semivariance is computed in a manner similar to the traditional
variance, except that if the deviation is positive, its value is
replaced by zero. We still tend to use variance instead of
semivariance because semivariance tends to complicate the risk-
return issue, and besides, if returns are symmetrically distributed,
then variance is two times semivariance.
C. The Historical Record
The standard deviation for small company-stocks is about 10 times
larger than that of U.S. treasury bills.
D. Normal Distribution
Historical returns on securities have probability distributions that
are approximately normal. The normal distribution is completely
described by its mean and variance. Since 1926, annual returns on
large company stocks have averaged about 11.9% with a standard
deviation of about 20.4%. An observation on a normally
distributed random variable has a 68% chance of being within plus
or minus one standard deviation from the mean, a 95% chance of
being within plus or minus two standard deviations from the mean,
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Chapter 12 – Some Lessons from Capital Market History
and a 99% chance of being within plus or minus three standard
deviations from the mean.
E. The Second Lesson
The greater the potential reward, the greater is the risk.
F. 2008: The Bear Growled and Investors Howled
Over the period 1926-2010, only the year 1931 had a lower return
than 2008 (-44% vs. 37%). From November 2007 to March 2009,
the S&P 500 lost 50% of its value; however, from March 2009 to
February 2011, the S&P 500 doubled in value.
In 2008, long-term U.S. treasuries were up almost 40%. A well-
diversified portfolio would have suffered much smaller losses in
2008 than an all-stock portfolio.
G. Using Capital Market History
Based upon the historical risk premium for large company
common stocks, an investment of “average risk” should return
about 8.2% above the T–bill rate.
Lecture Tip: It is sometimes difficult to get students to appreciate
the risk involved in investing in common stocks. They see the large
average returns and miss the variance. A simple exercise
illustrating the risk of the different securities can be performed
using Table 12.1. Each student (or the entire class) is given an
initial investment. They are then allowed to choose a security
class. Use a random number generator and the last two digits of
the year to sample the distribution. The initial investment is then
increased or decreased based on the return. This works best if the
trials are limited to between one and five.
H. More on the Stock Market Risk Premium
From 1900-2005, the U.S. had a stock market risk premium (7.4%)
near average (7.1%) for highly developed nations.
It is unclear what the true stock market risk premium is for the
U.S. The standard error from the 1900-2005 estimate is about 2%.
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Chapter 12 – Some Lessons from Capital Market History
5. More about Average Returns
A. Arithmetic versus Geometric Averages
Geometric average – average compound return earned per year
over multiple years
Arithmetic average – return earned in an average year over
multiple years
B. Calculating Geometric Average Returns
Arithmetic average is just the typical average that we are used to
computing: add the returns for each period and divide by the
number of periods
Geometric average = [(1+R1)*(1+R2)*…*(1+RT)]1/T – 1
Geometric means will always be smaller than arithmetic means
unless all the returns are equal.
C. Arithmetic Average Return or Geometric Average Return?
The geometric average tells you the return you earned per year
over the time period based on annual compounding. The arithmetic
average tells you what you earned in an average year. The
appropriate average depends on the question you are asking.
If you are using estimates of annual returns to determine future
values, then the arithmetic average is probably too high if you have
a long horizon and the geometric average is probably too low if
you have a short horizon. The arithmetic average is probably best
for short planning horizons and the geometric average is probably
best for very long planning horizons. If your planning horizon is
somewhere in the middle, say 25 to 40 years, then split the
difference between the two, which can be done more specifically
using Blume’s formula:
where T is our planning horizon, and N is the number of historical
data periods we have available.
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AverageArithmetic
N
TN
verageGeometricA
N
T
TR
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1
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Chapter 12 – Some Lessons from Capital Market History
6. Capital Market Efficiency
Efficient capital market – market in which current market prices fully reflect
available information. In such a market, it is not possible to devise trading
rules that consistently “beat the market” after taking risk into account.
A. Price Behavior in an Efficient Market
Lecture Tip: Is the degree of market efficiency increasing?
Consider the following:
–Investors today have virtually instantaneous access to financial
and economic information at low (or no) cost. A few years ago, the
same information was available only to professional managers.
–A substantial proportion of retail stock market trading is done
online. Virtually none was done online a few years ago.
–The average P/E ratios were at “historic” highs for many years.
They didn’t start to come down until late 2000 and early 2001 and
many companies still have “high” P/E ratios.
What does all of this mean? An efficient market is one in which
information is quickly and costlessly disseminated to all
participants. And while we aren’t there yet, the advent of the
Internet has resulted in being closer to that ideal than we have
been previously. Some analysts believe that required returns have
fallen because the cost of obtaining information has dropped so
dramatically. We can’t say for sure that markets are more efficient;
that is an empirical question. But, the changes in the last few years
seem to be moving us in that direction.
B. The Efficient Markets Hypothesis
Efficient markets hypothesis (EMH) – modern U.S. stock markets
are, in general, efficient. An important implication of the EMH is
that the expected return on securities equals their risk-adjusted
required return.
Key insight – competition among investors and traders makes a
market efficient.
C. Some Common Misconceptions about the EMH
Market efficiency does NOT imply that it doesn’t make a
difference how you invest, since the risk/return trade-off still
applies, but rather that you can’t expect to consistently earn excess
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Chapter 12 – Some Lessons from Capital Market History
returns using costless trading strategies.
Stock price fluctuations are evidence that the market is efficient
since new information is constantly arriving – prices that don’t
change are evidence of inefficiency.
The EMH doesn’t say prices are random. Rather, the influence of
previously unknown information causes randomness in price
changes. As a result, price changes can’t be predicted before they
happen.
Lecture tip: Although a full-blown discussion of efficient markets
goes beyond the scope of the typical introductory corporate
finance course, you may wish to ask students if they have ever
heard of a “hot tip.” Most students probably have heard a friend
claim to have such a tip, or have heard someone mention that a
broker recommended the purchase of a particular company. Then
question them concerning the value of this information. “If this
company was undervalued, why wouldn’t the investing community,
with all its high-paid security analysts, be purchasing the stock
since they would have access to this information before we receive
it from a stockbroker?”
Some point to the proliferation (and high cost) of investment
newsletters. The fact that rational investors subscribe suggests
that they contain valuable information, right? Point out that (a)
studies of performance generally don’t bear this out (with the
possible exception of Value Line) and (b) if someone really had the
power to forecast the direction of future stock prices, would he or
she really be willing to sell it to the public?
Lecture tip: Claims of superior performance in stock picking are
very common and often hard to verify. However, if markets are
semistrong form efficient, the ability to consistently earn excess
returns is unlikely. Discuss the following situation with students:
Suppose Mick Mannock runs the “High Flyers” Common Stock
fund, which is about as risky as the market. The fund has
outperformed the S&P 500 by 2 percent annually for the last four
years, and as a result, Mick has declared himself an “ace fund
manager.” Is this correct?
Assume that managers of other funds of equivalent risk have just
matched the market in terms of performance, and that the standard
deviation of excess returns has been about 6 percent over the last
four years. The t-stat for Mick’s performance is:
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Chapter 12 – Some Lessons from Capital Market History
(Mick’s excess return – average excess return )
standard deviation of excess returns
√
sample size
(2−0)
6 %/
√
4
The t-stat is .67, which is much less than that required at the 5
percent level of significance. Therefore, Mick’s performance is not
good enough to be declared “superior” based on the distribution
of returns.
Ethics Note: Program trading is defined as automated trading
generated by computer algorithms designed to react rapidly to
changes in market prices. Program trading enables traders to
quickly respond to up or down market movements or to changes in
price relationships across markets, e.g., between spot prices and
futures prices. Therefore, program trading occurs more quickly
than traditional floor trading. It has been argued that it is
unethical for investment banking houses to operate automated
trading programs for their own accounts. One reason is that the
bank may be trading ahead of its customers when it uses the
automated trading system. If this trading affects prices, then the
bank is not acting in the best interest of its customers.
Program trading can affect market prices. For example, a large,
erroneously executed sell order (which was literally a clerical
error) on March 25, 1992 resulted in a 12-point loss in the DJIA (a
.31% drop in value at that time). This trade occurred during the
final minute of trading. Had the error occurred earlier in the day,
this action could have caused a much larger drop in market value.
Further, many people attribute the stock market crash in October
1987, at least partially, to program trading.
Lecture Tip: Even the experts get confused about the meaning of
capital market efficiency. Consider the following quote from a
column in Forbes magazine: “Popular delusion three: Markets are
efficient. The efficient market [sic] hypothesis, or
EMH, would do credit to medieval alchemists and is about as
scientific as their efforts to turn base metals into gold.” The writer
is definitely not a proponent of EMH. Now consider this quote:
“The truth is nobody can consistently predict the ups and downs of
the market.” This statement is clearly consistent with the EMH.
Ironically, the same person wrote both statements in the same
column with exactly nine lines of type separating them.
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Chapter 12 – Some Lessons from Capital Market History
D. The Forms of Market Efficiency
Strong form efficiency – All information, both public and private,
is already incorporated in the price. Empirical evidence indicates
that this form of efficiency does NOT hold.
Semistrong form efficiency – All public information is already
incorporated in the price. It says that you cannot consistently earn
excess returns using available information to do fundamental
analysis. Evidence is mixed, but suggests that it holds for widely
held firms.
Weak form efficiency – All market information, including prices
and volume, is included in the price. It says that you cannot
consistently earn excess returns by looking for patterns in past
price and volume information, such as is done by technical
analysts. Evidence suggests that markets are weak form efficient
based on the trading rules that we have been able to test.
Ethics Note: Insider trading is illegal, but the determination of
what constitutes insider trading is difficult. Rule 10B-5 of the
Securities Exchange Act of 1934 states: “It shall be unlawful for
any person, directly or indirectly, by use of any means or
instrumentality of interstate commerce, or of the mails, or of any
facility on a national securities exchange, (1) to employ any
device, scheme, or artifice to defraud, (2) to make any untrue
statement of a material fact or omit to state a material fact
necessary in order to make the statements made, in light of the
circumstances under which they were made, not misleading, (3) to
engage in any act, practice, or course of business which operates
or would operate as a fraud or deceit upon any person, in
connection with the purchase or sale of any security.”
Additionally, several court cases have sought to more clearly
define insider trading. For insider trading to exist, there must be a
fiduciary relationship between the parties. Actions of the inside
trader do not have to meet the legal requirements of fraud; they
merely have to have the appearance of acting as a fraud or deceit.
Accidental discovery does not constitute a fiduciary relationship.
The court decided in Chiarella v. United States that an employee of
a printing firm, who was requested to proofread proxies that
contained unannounced tender offers (and unnamed targets) was
not guilty of insider trading because the employee determined the
identity of the target through his own expertise.
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Chapter 12 – Some Lessons from Capital Market History
Despite the passage of increasingly severe penalties for insider
trading (see the Insider Trading Sanctions Act of 1984 and the
Insider Trading and Securities Fraud Enforcement Act of 1988),
the evidence suggests that the practice persists in one form or
another. In December 1994, a Business Week cover story stated,
“insider trading is back.”
Evidence of this can be found in the high profile trial of Martha
Stewart. The gist of the case was that Martha’s friend and CEO of
Imclone told her that an important drug had not received approval
prior to the public announcement. Martha proceeded to sell her
stock in Imclone. Martha was not convicted of insider trading
however. She was convicted of obstructing justice and received a
relatively light sentence of 5 months in prison, 5 months of house
arrest, and 2 years of probation.
Ethics Note: Not all forms of insider trading are illegal. On
November 13th, 2011, the TV journalism program 60 Minutes ran a
piece investigating legal insider trading among U.S. Congressmen
(www.cbsnews.com/video/watch/?id=7388130n). Unlike CEO and
hedge fund managers often arrested for insider trading, sitting
member of Congress are not subject to laws regarding trading on
material non-public information that they gather as part of their
job. As members of key committees, such as defense, many
Congressmen are privy to information about legislation that is
certain to move stock prices. Should such activities be allowed?
Are the financial interests of Congressmen influencing the way
legislation is crafted? Are there any reasons why a bill banning
such activities would be a bad idea? As of February 19th, 2012,
both the House and Senate have passed versions of the Stock
Trading on Congressional Knowledge (STOCK) Act, but it has yet
to become law.
7. Summary and Conclusions
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