ECON7200 – Semester 2, 2021
Individual Project
Deadline: Monday 11 October 2021 16:00
Write an essay between 800 and 1200 words (excluding references), consisting of three tasks:
1. Read and summarize the following article:
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Malkiel, B. G. (2012). The Efficient-Market Hypothesis and the Financial Crisis. In A. S. Blinder, A.
W. Loh, and R. M. Solow (Eds.), Rethinking the Financial Crisis, Russell Sage Foundation, New
York.
2. Explain the efficient market hypothesis (EMH), behavioral finance, and their relevance in explaining asset
price bubbles.
3. In the end of the article, Malkiel concluded that“EMH and behavioral finance should not be considered as
competitive models”.
Discuss how he reached this view, and discuss your view on this statement.
Criteria and Marking:
The total mark for this assignment is 30, which consists of the following criteria:
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Article summary (4 marks)
EMH and behavioral finance (6 marks)
Discuss the author’s view (10 marks)
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Writing Quality (5 marks)
Presentation (2 marks)
Reference (3 marks)
In the Writing Quality criterion, your writing style, essay flow and grammar/spelling will be examined. The
Presentation criterion includes various details such as document format, identification and compliance of word
limit. In addition, there are some general points to consider:
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Your essay must have a cover page detailing your ID and word count.
Remember to give a total word count (excluding references) on the cover page.
You cannot use any bullet points in explanation or summary.
Any reference style is allowed, but it must be consistent (i.e. same style throughout the essay).
You cannot cite Wikipedia or Investopedia.
If graphs/figures are used, remember to quote the source.
Use academic writing style and not creative writing style.
Do not plagiarize! It will be dealt with very seriously in UQ and is simply not worth trying.
Submission:
Each student should submit a Word or PDF file through the Turnitin link on the Blackboard course website.
Submission through email will not be accepted. All submissions will be run through the Turnitin anti-plagiarism
software.
ECON7200 – Economics of Financial Markets Individual Project
An Essay on the Economic Behaviour Leading to Bubble Formation via the Lens of the Dot-Com
Boom and Today.
Name: Tyson Ruth
ID: 40800756
Word Count: 1299 (Ex references/footnotes)
ECON7200 – Individual Project
Tyson Ruth: 40800756
Throughout the existence of markets and tradeable commodities, asset bubbles and speculative
manias have formed and deflated ad-nauseum throughout recorded human history (Mackay,
1841). This essay aims to discuss several impactful factors with respect to asset price bubbles in
the context of the dot-com boom, the global financial crisis, and today’s rapidly rising
technology stock prices. This discussion will be framed in the lens of behavioural economics in
contrast to the efficient market hypothesis (EMH). Three elements will be of focus – Goodnight
and Green’s 2010 paper “Rhetoric, Risk and Markets: The Dot-Com Bubble”, behavioural
economics’ explanation of said bubble, and whether today’s technology company share prices
will result in another dot-com bubble crash.
The work of Goodnight and Green (2010) is of particular interest in the sense that it
approaches the breakdown of financial markets during the spectacular rise and subsequent fall
of technology asset prices in 1999-2000 from the perspective of speech, mimetics,
communicatory feedback loops, and an attention economy of worth. Their paper begins with a
discussion of the circumstances of the Dutch tulip bubble, referring to Mackay’s (1841) book
“Memoirs of Extraordinary Popular Delusions”, wherein speculation led tulip bulbs to
substantially elevated prices before a significant collapse.1 Goodnight and Green find that
bubbles spread in stark contrast to the efficient market hypothesis in circumstances where
investors switch from “imitating standard, rational, probability based-models to copying
arguably novel ventures with enticing uncertainties” (2010).
The authors further show that behavioural and post-conventional economic methods
attempt to explain such deviations in rationality. Goodnight and Green state that market
participants exist within a survival game and their self-motivated constituent agents utilise
mimetic isomorphism in order to imitate successful survival processes (DiMaggio & Powell,
1983). This feedback loop of (initially) rational imitation gradually converges with newer,
degenerate narratives when unconventional speculative agents gain an initial outsize success –
as communicative and mimetic elements disseminate their success to the greater market,
imitators become copiers – resulting in a self-fulfilling prophecy. The authors conclude that
economies of value eventually converge to economies of worth in bubble-environments. Upon
signs of significant weakness (rapidly perpetuated by communication innovations), the initial
rush to sell is further imitated by the copiers resulting in spectacular collapse. With a dramatic
financial shock, participants (who survive) return to rational methods post-hoc (Goodnight &
Green, 2010).
The findings of Goodnight and Green (2010) can be developed further by discussing
behavioural economics and its role in explaining the events of the dot-com bubble. Kahneman
(2011) found that, while attempting to be rational, self-interested humans do not have the
resources to pursue optimal rational decisions due to the time and mental cost of doing such.
Instead of pure rational-optimal decision making (homo-economicus, who seemingly exist only
in Economic theory), agents instead possess bounded-rationality – agents will make decisions at
the individual’s optimal curve of effort/perceived-reward, resulting in an effort saving number
of heuristics and biases. These heuristics and biases – while effective for surviving in the wild result in suboptimal decision making when applied to financial market dynamics. Kahneman
discusses the reoccurrence of asset bubbles wherein particular agents mentioned “knowing” an
asset collapse was inevitable – however, this post-hoc/hindsight approach to bubbles is another
behavioural bias – humans believe that the past is understood, “but in fact we understand the
This book was later reprinted as “Extraordinary Popular Delusions and the Madness of Crowds”. This
author highly recommends a careful study as it also covers the Mississippi Scheme and the South-Sea
Bubble from the worldview of the 1800s. Speculation is as old as time (Lefevre, 1923).
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past less than we believe we do” (Kahneman, 2011, pp. 201-202). It is these same agents who go
on to participate in future bubbles by nature of existing within the market.2
Thaler (2015, pp. 203-256) further develops Kahneman’s ideas by explicitly discussing
financial markets. An optimal-rational agent should, in theory, select the best candidate equities
based on fundamentals – a business most likely to generate excess future cashflows with strong
growth prospects should perhaps be rewarded with stock price appreciation. However, Thaler
finds that what occurs is Keynes’ (1936) “beauty-contest” of agent expectations – in order to
select a winner (using a win-on-consensus constraint), one should select the candidate that they
believe other competing agents will select – this is in stark contrast to the naïve selection of
what the agent themselves believes will win. Therefore, it becomes rational for other agents to
do the same – leading to the prior feedback loop discussed by Goodnight and Green (2010). It is
these very behavioural aspects that led to the formation of the events of the dot-com bubble.
In considering these findings with respect to the EMH, it would instead appear to be
that, while seemingly efficient on the aggregate level, the EMH inherently breaks down due to
the preponderance of the behavioural factor driving markets. Mandelbrot and Hudson dedicate
significant time in dissecting the standard EMH model, taking note that “the biggest edge you
can have is the private information on who’s buying what. We do not believe the market is
efficient” (2004, pp. 79-107). This directly contradicts portions of the EMH wherein any
profitable information should already be reflected within stock prices. Once again, this ties back
to Keynes’ “beauty-contest”.
With these behavioural components in mind, we can now discuss the question - “will
this be another dot-com bubble and crash?”. There are significant similarities between the dotcom bubble and today’s tech stock valuations – companies are priced based on future growth
potential instead of current business fundamentals (How does today’s tech, 2020). Market
conditions where investors are frontrunning future potential “winners” by extrapolating
significant growth or betting on outsize innovation/disruption by individual companies is
leading, once-again, to high tech valuations. However, other market factors are different –
labour and total-factor productivity were outsize in the 90s, enabling simultaneous wage and
corporate profit growth. This is in stark contrast to the 2010s, which were low by comparison.
However, it is noted that output per hour and total factor productivity have begun to accelerate
from 2019 onwards, with some noting that this “might presage an economic transformation in
the making” (How does today’s tech, 2020). Despite lower aggregate productivity (relative to
the 90s), the current zero-to-low interest rate environment has significantly reduced the
opportunity cost of borrowing money, leading to generalised asset (both equities and real
estate) price inflation in many markets worldwide. In further comparing the current tech boom
with the dot-com era, Mackenzie (2021) notes that investors are once-again willing to “put
aside doubts on business models to bet on the promise of innovation”. He specifically mentions
the Ark Innovation ETF which, as a significant holder of Tesla, has returned significantly outsize
returns in the past year.
With these in mind we can reach a number of conclusions - while it can be seen that
behavioural aspects are similar to those of the dot-com bubble - crowded tech trades,
indiscriminate purchasing of speculative stocks via ETF instruments, and significant outsize
returns (swaying traders from the imitate to the copy side) – the underlying interest rate
environment is significantly different. With almost no borrowing-cost of money, investors can
Of note is Kahneman’s findings regarding the asymmetrical pain/gain function humans feel when
trading – a loss is more painful to an agent in comparison to that of an equal monetary gain. This
potentially explains why “markets take the stairs up and the elevator down”.
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increase their market exposure at little cost. Keynes’ “beauty-contest” methodology of picking
winners before other participants has led to investors treating speculative stocks as “long-dated
bonds”, which hold significant interest rate risk (Mackenzie, 2021). It is also of note that, akin to
the dot-com crash, the current market also has significant concentrations in its primary indexes
(Apple and Microsoft comprise a significant portion of the S&P500). We can conclude that by
the metrics discussed in this paper, the current tech market is almost certainly in a bubble,
though by nature they are difficult to identify during (Shiller, 2005; Kahneman, 2011).
This author (ironically speculating by doing so) concludes that at some indeterminate
point in the future, a flight to quality will occur wherein strong tech companies (e.g. Google,
Amazon) will once again assimilate the remaining capital plied into that of pure speculative
plays (e.g. Pets.com, Peloton). However, this event will require potential catalysts which may
include quantitative tightening, unseen shocks (pandemics), political turmoil and traditional
inflation.
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References
DiMaggio, P., Powell, W. (1983). The Iron Cage Revisited: Institutional Isomorphism and
Collective Rationality in Organizational Fields. American Sociological Review. 48(2),
147-160.
Goodnight, T., Green, E. (2010). Rhetoric, Risk, and Markets: The Dot-Com Bubble. Quarterly
Journal of Speech, 96(2), 115-140.
Kahneman, D. (2011). Thinking Fast & Slow. New York: Farrar, Straus & Giroux.
Keynes, J. (1936). The General Theory of Employment, Interest and Money. United Kingdom:
Palgrave Macmillan.
Lefevre, E. (2005). Reminiscences of a Stock Operator. New Jersey: John Wiley & Sons.
Mackay, C. (1841). Memoirs of Extraordinary Popular Delusions. Great Britain: Harriman House.
Mackenzie, M. (2021, January 16). Tech stock booms: then and now. Financial Times.
Mandelbrot, B., Hudson, R. (2006). The Misbehaviour of Markets: A Fractal View of Financial
Turbulence. New York: Basic Books.
Shiller, R. (2005). Irrational Exuberance. New Jersey: Princeton University Press.
Thaler, R. (2015). Misbehaving: The Making of Behavioural Economics. Great Britain: Allen
Lane.
How does today's tech boom compare to the dotcom era? (2020, September 19). The Economist.
4
Rethinking the Financial Crisis
Blinder, Alan S., Lo, Andrew W., Solow, Robert M.
Published by Russell Sage Foundation
Blinder, Alan S., et al.
Rethinking the Financial Crisis.
Russell Sage Foundation, 2012.
Project MUSE.
muse.jhu.edu/book/22174.
For additional information about this book
https://muse.jhu.edu/book/22174
[ Access provided at 9 Sep 2021 06:01 GMT from University of Queensland ]
Chapter 4
The Efficient-Market Hypothesis
and the Financial Crisis
Burton G. Malkiel
The worldwide financial crisis of 2008 to 2009 left in its wake severely damaged
economies in the United States and Europe. The crisis also shook the foundations of modern-day financial theory, which rests on the proposition that our
financial markets are basically efficient. Critics have even suggested that the
efficient-market hypothesis (EMH) was in large part responsible for the crisis.
This chapter argues that the critics of EMH are using a far too restrictive
interpretation of what EMH means. EMH does not imply that asset prices
are always “correct.” Prices are always wrong, but no one knows for sure if
they are too high or too low. EMH does not imply that bubbles in asset prices
are impossible, nor does it deny that environmental and behavioral factors
cannot have profound influences on required rates of return and risk premiums. At its core, EMH implies that arbitrage opportunities for riskless gains
do not exist in an efficiently functioning market and that, if they do appear
from time to time, they do not persist. The evidence is clear that this version
of EMH is strongly supported by the data. EMH can comfortably coexist
with behavior finance, and the insights of Hyman Minsky are particularly
relevant in eliminating the recent financial crisis.
Bubbles, when they do exist, are particularly dangerous when they are financed
with debt. The housing bubble and the derivative securities associated with it left
both the consumer and financial sectors dangerously leveraged. Policymakers
are unlikely to be able to identify bubbles in advance, but they must be better
focused on asset-price increases that are financed with debt.
T
he worldwide financial crisis of 2008 to 2009 left in its wake severely damaged
economies in the United States and Europe. Unemployment rates soared up
to and in some cases above the double-digit level, and economies in Europe
and the United States were still operating in 2012 well below economic capacity.
Moreover, the high indebtedness of consumers, financial institutions, and governments made the severe recession unusually persistent and limited the fiscal policy
responses of governments throughout the world.
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The crisis also shook the very foundations of modern-day financial theory, which
rests on the hypothesis that our financial markets are basically efficient. Financial
writers and economists alike were ready to write obituaries for the efficient-market
hypothesis, or EMH, as it is widely known. The financial writer Justin Fox published a best-selling book in 2009 entitled The Myth of the Rational Market. The economist Robert Shiller (1984, 459) described EMH as “one of the most remarkable
errors in the history of economic thought.” Some professional investment managers went even further. Jeremy Grantham opined that EMH was “more or less
directly responsible” for the financial crisis.1 Paul Krugman (2009) agreed, writing
that “the belief in efficient financial markets blinded many if not most economists
to the emergence of the biggest financial bubble in history. And efficient-market
theory also played a role in inflating that bubble in the first place.”
In this essay, I describe what the efficient-market hypothesis implies for the functioning of our financial markets. I suggest that a number of common misconceptions about EMH have led some analysts to reject the hypothesis prematurely. I then
examine the abundant evidence that leads me to believe that our financial markets
are remarkably efficient and that reports of the death of EMH are greatly exaggerated. Finally, I indicate what I believe are the important lessons that policymakers
should learn from the financial crisis.
What the Efficient-Market Hypothesis
Means and What It Does Not Mean
Two fundamental tenets make up the efficient-market hypothesis. EMH first asserts
that public information is reflected in asset prices without delay. Information that
should beneficially (adversely) affect the future price of any financial instrument
is reflected in the asset’s price today. If a pharmaceutical company now selling at
$20 per share receives approval for a new drug that will give the company a value
of $40 tomorrow, the price will move to $40 right away, not slowly over time.
Because any purchase of the stock at a price below $40 will yield an immediate
profit, we can expect market participants to bid the price up to $40 without delay.
It is, of course, possible that the full effect of the new information is not immediately obvious to market participants. It is also likely that the estimated sales
and profits cannot be predicted with any precision and that the value of the discovery is amenable to a wide variety of estimates. Some market participants may
vastly underestimate the significance of the newly approved drug, but others may
greatly overestimate its value. In some cases, therefore, the market may underreact
to a favorable piece of news. But in other cases, the market may overreact, and it
is far from clear that systematic underreaction or overreaction to news presents an
arbitrage opportunity promising traders easy, risk-adjusted, extraordinary gains.
It is this aspect of EMH that implies its second, and more fundamental, tenet: in an
efficient market, no arbitrage opportunities exist.
This lack of opportunities for extraordinary profits is often explained by a joke
popular with professors of finance. A professor who espouses EMH is walking
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along the street with a graduate student. The student spots a $100 bill lying on the
ground and stoops to pick it up. “Don’t bother to try to pick it up,” says the professor. “If it was really a $100 bill, it wouldn’t be there.” Perhaps a less extreme telling
of the story would have the professor advising the student to pick the bill up right
away because it will not be lying around very long. In an efficient market, competition ensures that opportunities for extraordinary risk-adjusted gain do not persist.
EMH does not imply that prices are always “correct” or that all market participants are always rational. There is abundant evidence that many (perhaps
even most) market participants are far from rational and suffer from systematic
biases in their processing of information and their trading proclivities. But even
if price-setting were always determined by rational, profit-maximizing investors,
prices could never be “correct.” Suppose that stock prices are rationally determined as the discounted present value of all future cash flows. Future cash flows
can only be estimated and are never known with certainty. There will always be
errors in the forecasts of future sales and earnings. Moreover, equity risk premiums are unlikely to be stable over time. Prices are therefore likely to be “wrong”
all the time. What EMH implies is that we never can be sure whether they are
too high or too low at any given time. Some portfolio managers may correctly
determine when some prices are too high and others too low. But other times such
judgments are in error. And in any event, the profits that are attributable to correct
judgments do not represent unexploited arbitrage possibilities.
Complex financial investments are particularly susceptible to mispricing, especially when the loans that underlie the derivative are misrepresented. A full discussion of the causes of the financial crisis is beyond the scope of this essay, but there
is no doubt that the mispricing of mortgage-backed securities played an important
role in widening the crisis. Although the mispricing of the real estate securing the
mortgages may correctly be described as a classic bubble, there was far from a lack
of rationality throughout the market. Perverse incentives influenced both mortgage
originators and investment bankers. And the financial institutions that held excessive
amounts of the toxic instruments in highly leveraged portfolios were encouraged to
do so by asymmetric compensation policies and by a breakdown of regulation that
led to a failure to constrain excessive debt and inadequate liquidity. In any event,
while some hedge funds profited from selling some of these instruments short, there
were certainly no arbitrage opportunities that were obvious ex ante.
EMH and the Adjustment of Market Prices to Different
Types of New Information
Since Eugene Fama’s (1970) influential survey article, it has been customary to
distinguish between three versions of EMH depending on the type of information that is believed to be reflected in the current prices of financial assets (see
also Fama 1991). In the “narrow” or “weak” form of the hypothesis, it is asserted
that any information that might be contained in historical price series or trading
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volume is already reflected in current prices. Since past trading data are widely
available, any historical patterns that might have reliably predicted future price
movements will already have been exploited. If, for example, there has been a reliable “Santa Claus Rally” (suggesting that stock prices will rise between Christmas
and New Year’s Day), investors will act to anticipate the signal, and when they do,
the historical pattern will self-destruct. According to this version of EMH, “technical analysis”—the interpretation of historical price charts—will be nugatory.
Broader forms of the hypothesis have expanded on the types of information that
are reflected in current prices. According to the “semi-strong” form of the hypothesis,
any “fundamental” information about individual companies or about the stock
market as a whole will be reflected in stock prices without delay. Thus, investors
cannot profit from acting on some favorable piece of news concerning a company’s
sales, earnings, dividends, and so on, because all of the available publicity on the
subject will already be reflected in the company’s stock price. Profit-seeking traders
and investors can be expected to exploit even the smallest informational advantage,
and by so doing, they incorporate all information into market prices, thereby eliminating any profit opportunities. According to this version of the hypothesis, even
“fundamental analysis”—in-depth analysis of the financial situation and the prospects for individual companies—will prove fruitless because all favorable information will already have been reflected in market prices.
A third form of EMH suggests that not only anything that is known but also
anything that is knowable has already been assimilated into market prices. This
extreme version postulates that one cannot even benefit from “inside information.”
It is unlikely that this “strong” form of the hypothesis is ever completely satisfied.
But trading on inside information is illegal, and in the United States the Securities
and Exchange Commission (SEC) has been increasingly diligent in going after
company executives and hedge fund managers who are believed to have profited
from trading on inside information.
EMH and the Random Walk Hypothesis
All forms of EMH imply that market prices cannot be forecast. Much of the empirical literature has focused on the “random walk” hypothesis, a statistical description of unforecastable price changes. The term was apparently first used in an
exchange of correspondence that appeared in Nature in the early 1900s (Pearson
1905). The subject of the correspondence was the optimal search procedure for
finding a drunk who had been left in the middle of a field. The answer was quite
complex, but the place to start was simply the place where the drunkard had been
left. Paul Samuelson (1965) made a seminal contribution to the EMH literature in
his article entitled “Proof That Properly Anticipated Prices Fluctuate Randomly.”
If market prices fully incorporate the information and expectations of all market
participants, then price changes must be random. Prices will, of course, change
as new information is revealed to the market, but true news is random—it cannot
be forecast from past events. Thus, in an informationally efficient market, price
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changes are unforecastable. Samuelson’s contribution has been extended to allow
for risk-averse investors by Stephen LeRoy (1973) and Robert Lucas (1978) and in
many other directions by other researchers, including directions that allow for heterogeneous expectations. Random price movements do not imply that the stock
market is capricious. Randomness indicates a well-functioning and efficient market rather than an irrational one.
The earliest empirical work on the random walk hypothesis was performed by
Louis Bachelier (1900). He concluded that commodity prices follow a random walk,
although he did not use that term. Corroborating evidence from other time series
was provided by Holbrook Working (1960) and from U.S. stock prices by Alfred
Cowles and Herbert Jones (1937) and Maurice Kendall (1953). These studies generally found that the serial correlations between successive changes are essentially
zero. Harry Roberts (1959) found that a time series generated from a sequence of
random numbers has the same appearance as a time series of U.S. stock prices.
M. F. M. Osborne (1977) concluded that stock-price movements are similar to the
random Brownian motion of physical particles and that the logarithms of price
changes are independent of each other.
Other empirical work, using alternative techniques and data sets, has searched
for more complicated patterns in the sequence of prices in speculative markets.
Clive Granger and Oskar Morgenstern (1963) used the technique of spectral analy
sis but were unable to find any dependably repeatable patterns in stock-price
movements. Eugene Fama (1965a, 1965b) not only looked at serial correlation coefficients (which were close to zero) but also corroborated his investigation by examining a series of lagged prices and performing a number of nonparametric “runs”
tests. He also examined a variety of filter techniques—trading techniques where
buy (sell) signals are generated by some upward (downward) price movements
from recent troughs (peaks)—and found that they could not produce abnormal
profits. Other investigations have done computer simulations of more complicated
technical analysis of supposedly predictive stock chart patterns (such as “double
tops,” “inverted head and shoulders,” and so on) and found that profitable trading strategies cannot be undertaken on the basis of these patterns. Bruno Solnik
(1973) measured serial correlation coefficients for daily, weekly, and monthly price
changes in nine countries and concluded that profitable investment strategies
cannot be formulated on the basis of the extremely small dependencies found.
Although most of the earliest studies of the stock market supported a general
finding of randomness, more recent work has indicated that the random walk
model does not strictly hold. Some patterns appear to exist in the development of
stock prices. Over short holding periods, there is some evidence of momentum in
the stock market, while mean reversion appears to be present over longer holding
periods. Nevertheless, it is less clear that there are violations of the weak form of
EMH, which states only that unexploited trading opportunities should not persist
in any efficient market.
Andrew Lo and Craig MacKinlay (1999), in a book entitled A Non-random Walk
Down Wall Street, have found evidence inconsistent with the random walk model.
Calculating weekly and monthly holding period returns for various stock indexes,
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they find evidence of positive serial correlation, which implies some momentum
in stock prices. Moreover, exploiting the fact that return variances scale linearly in
a random walk market, they construct a variance ratio test that rejects the random
walk hypothesis. This rejection of the random walk hypothesis for stock indexes may
result, however, from the behavior of small company stocks that are infrequently
traded. New information about the market as a whole is likely to be factored into
the prices of large capitalization stocks first and into the prices of smaller stocks later.
Interestingly, Lo and MacKinlay are unable to reject the random walk hypothesis
when they perform tests on individual stocks. Narasimhan Jegadeesh and Sheridan
Titman (1993) have also found some evidence of momentum in stock prices.
Two possible explanations for the existence of momentum have been offered:
the first is based on behavioral considerations, the second on sluggish responses to
new information. Shiller (2000) emphasizes a psychological feedback mechanism
that imparts a degree of momentum into stock prices, especially during periods
of extreme enthusiasm. Individuals see stock prices rising and are drawn into the
market in a kind of “bandwagon effect.” The second explanation is based on the
argument that investors do not adjust their expectations immediately when news
arises—especially news of company earnings that have exceeded (or fallen short
of) expectations. Ray Ball and Phillip Brown (1968) and Richard Rendleman,
Charles Jones, and Henry Latané (1982) have found that abnormally high returns
follow positive earnings surprises as market prices appear to respond to earnings
information only gradually.
There is enough evidence in support of short-term momentum that researchers such as Mark Carhart (1997) have considered momentum to be a priced factor
in explaining the cross-section of security and mutual fund returns. And Clifford
Asness, Tobias Moskowitz, and Lasse Pederson (2010) have offered actual investment funds where stocks showing positive momentum are overweighted in the
portfolio. In these two analyses, positive momentum is considered to be strong
relative performance over the preceding twelve months (not including the most
recent month to allow for any short-term return reversals). As is the case with many
of the so-called predictable patterns in stock-price returns, investment strategies
based on these are predictive during some periods but not in others.
Although there is some evidence supporting the existence of short-term momentum in the stock market, many studies have shown evidence of negative serial
correlation—that is, return reversals—over longer holding periods. For example,
Eugene Fama and Kenneth French (1988) have found that 25 to 40 percent of the
variation in long holding period returns can be predicted in terms of a negative correlation with past returns. Similarly, James Poterba and Lawrence Summers (1988)
have found substantial mean reversion in stock market returns at longer horizons.
Some studies have attributed this forecastability to the tendency of stock market prices to “overreact.” Werner De Bondt and Richard Thaler (1985), for example,
argue that investors are subject to waves of optimism and pessimism that cause
prices to deviate systematically from their fundamental values and later to exhibit
mean reversion. They suggest that such overreaction to past events is consistent
with the implication of the behavioral decision theory of Daniel Kahneman and
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Amos Tversky (1974, 1979) that investors are systematically overconfident of their
ability to forecast either future stock prices or future corporate earnings (see also
Kahneman and Riepe 1998). These findings give some support to investment techniques that rest on a “contrarian” strategy—that is, buying the stocks, or groups of
stocks, that have been out of favor for long periods of time.
However, the finding of mean reversion is not uniform across studies and
is quite a bit weaker in some periods than it is for others. Indeed, the strongest
empirical results come from periods including the Great Depression, which may
have been a time with patterns that do not generalize well. Moreover, such return
reversals for the market as a whole may be quite consistent with the efficient functioning of the market, since they could result, in part, from the volatility of interest
rates and the tendency of interest rates to be mean-reverting. Since stock returns
must rise or fall to be competitive with bond returns, there is a tendency when
interest rates go up for prices of both bonds and stocks to go down, and for prices
of bonds and stocks to go up as interest rates go down. If interest rates revert to the
mean over time, this pattern tends to generate return reversals, or mean reversion,
in a way that is quite consistent with the efficient functioning of markets.
Moreover, it may not be possible to profit from the tendency for individual
stocks to exhibit return reversals. Zsuzsanna Fluck, Burton Malkiel, and Richard
Quandt (1997) simulated a strategy of buying stocks over a thirteen-year period
during the 1980s and early 1990s, and returns over smaller periods of three to five
years within that time span were particularly poor. They found that stocks with
very low returns over the past three to five years had higher returns in the next
period, and that stocks with very high returns over the past three to five years had
lower returns in the next period. Thus, they confirmed the very strong statistical evidence of return reversals. However, they also found that returns in the next period
were similar for both groups, so they could not confirm that a contrarian approach
would yield higher-than-average returns. There was a statistically strong pattern of return reversal, but not one that implied an inefficiency in the market that
would enable investors to make excess returns. Moreover, many of the predictable patterns mentioned in the finance literature seemed to disappear after they
were published. William Schwert (2003) suggests two possible explanations. First,
researchers have a normal tendency to focus on results that challenge conventional wisdom. It is likely that in some particular sample a statistically significant
result will emerge that appears to challenge EMH. Alternatively, practitioners may
learn quickly about any “dependable” profit-making opportunities and exploit
them until they are no longer profitable. In other words, if there are $100 bills
available, they will be picked up as soon as they are discovered. My own view
of the matter has been succinctly expressed by Richard Roll (1992, 28), an academic economist who also was a portfolio manager, investing billions of dollars
of investment funds:
I have personally tried to invest money, my client’s money and my own, in
every single anomaly and predictive device that academics have dreamed
up. . . . I have attempted to exploit a whole variety of strategies supposedly
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documented by academic research. And I have yet to make a nickel on any of
these supposed market inefficiencies. . . . But, I have to keep coming back to
my point . . . that a true market inefficiency ought to be an exploitable opportunity. If there’s nothing investors can exploit in a systematic way, time in
and time out, then it’s very hard to say that information is not being properly
incorporated into stock prices. . . . Real money investment strategies don’t
produce the results that academic papers say they should.
The Semistrong Form of EMH
The narrow or weak form of EMH suggests that any information contained in the
history of stock prices will have already been reflected in current prices. Hence,
“technical analysis,” the analysis of past price movements, cannot be employed
to produce above-average returns. But most professional investment managers are “fundamental analysts” rather than technicians. Fundamental analysts
study a wide range of information, including company sales, earnings, and asset
values, in forming portfolios that they hope will earn excess returns. Studies
attempting to determine whether publicly available information can be used
to improve portfolio performances are tests of the semistrong form of EMH.
Usually a finding that abnormal returns can be earned is referred to as an EMH
anomaly.
At the outset, it is important to note that any empirical test purporting to show
that abnormal returns can be earned is based on some model of risk adjustment.
For example, the capital asset pricing model (CAPM) is often used to adjust for
risk. Thus, an anomalous finding that excess returns can be earned by exploiting
publicly available “fundamental” information is actually a joint test of EMH and
the risk adjustment procedures employed. If the CAPM beta is an inadequate measure of risk (or if beta is measured with error), it will be inappropriate to consider
beta-adjusted excess returns to be inconsistent with EMH. Similarly, if market capitalization (size) and market-to-book factors are added to beta to account for risk,
abnormal returns will be identified only if this three-factor model fully describes
the cross-section of expected returns.
Tests of the semistrong form of EMH have looked at how rapidly new information
is reflected in market prices and whether the use of certain valuation metrics favored
by security analysts can generate abnormal returns. Studies seeking to examine the
rapidity of price responses to news announcements are called “event studies.” The
“events” used in such studies have included dividend changes, earnings reports
that have differed from estimates, and merger announcements.
Various tests have been performed to ascertain the speed of adjustment of market prices to new information. Fama and his colleagues (1969) looked at the effect
of stock splits on equity prices. Although splits themselves provide no economic
benefit, splits are usually accompanied or followed by dividend increases that do
convey information to the market concerning management’s confidence about the
future progress of the enterprise. Although splits usually result in higher market
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valuations, the market appears to adjust to such announcements fully and immediately. Substantial returns can be earned before the split announcement, but there is
no evidence of abnormal returns after the public announcement.
Similarly, merger announcements can raise market prices substantially, especially when premiums are being paid to the shareholders of the acquired firm, but it
appears that the market adjusts fully to the public announcements. Arthur Keown
and John Pinkerton (1981) found no evidence of abnormal price changes after the
public release of merger information. James Patell and Mark Wolfson (1984) examined the intraday speed of adjustment to earnings and dividend announcements.
They noted that the stock market assimilates publicly available information “very
quickly.” The largest portion of the price response occurs in the first five to fifteen
minutes after disclosure.
Although most event studies have supported EMH, some have not. Ball and
Brown (1968) found that stock-price reactions to earnings announcements are not
complete. He found that abnormal returns can be earned in the period after the
announcement date. Rendleman, Jones, and Latané (1982) also found that unexpected earnings announcements are not immediately reflected in stock prices and
that abnormal returns can be earned by purchasing shares of companies with positive earnings surprises. These studies of sluggish adjustment (or underreaction)
support the momentum arguments referred to earlier. However, the pattern of
underreaction to announcements is not consistent over time. Fama (1998) has
argued that overreaction to news announcements appears about as often as underreaction (see also Bernard and Thomas 1990). In any event, such anomalies tend
to be so small that only professional traders could have earned economic profits.
There has been considerable work on the use of a variety of valuation metrics
to isolate stocks that are expected to generate “excess” returns. An influential book
by Benjamin Graham and David Dodd (1934) entitled Security Analysis spawned
the development of a whole profession of security analysts who were trained to
examine “fundamental” financial data for firms, such as earnings and asset values, and find stocks that represented “good value.” The approach remains popular today, especially with the growing appeal of behavioral finance. Behaviorists
such as Daniel Kahneman and Amos Tversky (1974, 1979) have argued that investors tend to be overoptimistic and far more certain of their forecasts than is warranted. Thus, they tend to overestimate future growth and to pay more than they
should for “growth” stocks—those stocks promising above-average future growth.
Conversely, “value” stocks—those stocks that are less exciting and therefore sell
at more modest valuation metrics, such as low multiples of earnings and of book
value—are likely to generate excess returns.
Of all the predictable patterns that have been discovered, this so-called value
effect is among those most supported by the evidence. Basu (1977) found that portfolios of stocks with low price-to-earnings (P/E) multiples have tended to provide
higher returns than portfolios of stocks with high P/E ratios. Using a somewhat
different value criterion, Fama and French (1992, 1998) found that portfolios made
up of stocks with low ratios of price-to-book-value (P/BV) provide relatively
higher returns than the portfolios of high-P/BV firms. When the CAPM measure
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of risk was used to adjust for risk, the higher return from value stocks appeared to
represent an inefficiency.
Another pattern that has found empirical support is the size or small-firm effect.
Between 1926 and the present, an investor could have realized higher portfolio
returns by concentrating on stocks with relatively small market capitalizations (see
Banz 1981; Reinganum 1983; Ibbotson Associates). Fama and French (1998) have
demonstrated that this effect can be documented in international as well as U.S.
stock markets. In the United States, the excess returns from small-capitalization
stocks appear almost entirely in January—hence this size effect is often called “the
January Effect.”
Findings such as these have often been considered “anomalies” or “inefficiencies.” But again, we are driven back to the joint hypothesis problem. If CAPM is an
insufficient model for the measurement of risk, then the result does not represent
an inefficiency. Indeed, Fama and French (1993) have proposed that small company stocks and low-P/BV stocks are riskier. Small companies can be more vulnerable to economic shocks than larger firms, and low P/BV may be a reflection
of some form of economic distress. For example, during the recent financial crisis,
distressed bank stocks sold at unusually low prices relative to their book values.
Hence, any excess returns that were earned were simply some compensation for
risk. This interpretation has been vigorously disputed by Josef Lakonishok, Andrei
Schleifer, and Robert Vishny (1994), who argue that these patterns are evidence of
inefficiencies. Nevertheless, it has become standard to employ risk measurement
techniques that augment the beta risk measure of CAPM with the addition of size
and P/BV factors. In some models, a fourth factor, momentum, is added to the
Fama-French three-factor risk model (see, for example, Carhart 1997).
Predictable Time-Series Market Returns
Based on Valuation Parameters
Considerable empirical research has been conducted to determine whether future
returns for the overall market can be predicted on the basis of initial valuation
parameters. It is claimed that valuation ratios, such as the price-to-earnings multiple
or the dividend yield of the stock market as a whole, have considerable time-series
predictive power.
Formal statistical tests of the ability of dividend yields (that is, the ratio of dividends to stock prices) to forecast future returns have been conducted by Eugene
Fama and Kenneth French (1988) and by John Campbell and Robert Shiller (1998).
Depending on the forecast horizon involved, as much as 40 percent of the variance
of future returns for the stock market as a whole can be predicted on the basis of
the initial dividend yield of the market index.
This finding is not necessarily inconsistent with efficiency. Dividend yields of
stocks tend to be high when interest rates are high, and they tend to be low when
interest rates are low. Consequently, the ability of initial yields to predict returns
may simply reflect the adjustment of the stock market to general economic
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conditions. Moreover, the use of dividend yields to predict future returns has
been much less effective since the mid-1980s. One possible explanation is that the
dividend behavior of U.S. corporations has changed over time, as suggested by
Laurie Bagwell and John Shoven (1989) and by Fama and French (2001). During
more recent years, companies may have been more likely to institute a share
repurchase program than to increase their dividends. Changing compensation
practices—company executives are now more likely to be rewarded with stock
options than with cash bonuses—have encouraged such a change in behavior.
Buybacks tend to increase the value of executive stock options. The option holder
does not receive any dividends that are paid. Finally, it is worth noting that this
phenomenon does not work consistently with individual stocks. Investors who
simply purchase a portfolio of individual stocks with the highest dividend yields
in the market will not earn a particularly high rate of return (see, for example,
Fluck et al. 1997).
Time-series empirical studies have also found that price-to-earnings multiples
for the market as a whole have considerable predictive power. Investors have
tended to earn larger long-horizon returns when purchasing the market basket of
stocks at relatively low price-to-earnings multiples. Campbell and Shiller (1998)
have shown that initial P/E ratios explain as much as 40 percent of the variance of
future returns. They conclude that equity returns have been predictable in the past
to a considerable extent.
Consider, however, the experience during the past fifteen years of investors
who have attempted to undertake investment strategies based either on the level
of the price-to-earnings multiple or on the size of the dividend yield to predict
future long-horizon stock returns. Price-to-earnings multiples for the Standard
& Poor’s 500 stock index were unusually high in mid-1987 (suggesting very low
long-horizon returns). Dividend yields fell below 3 percent. The average annual
total return from the index over the next ten years was an extraordinarily generous
16.7 percent. Earnings multiples were also extremely high in the early 1990s, but
returns remained extremely high until the end of the decade. We need to be very
cautious in assessing the extent to which stock market returns are predictable on
the basis of valuation metrics. Studies by Amit Goyal and Ivo Welch (2003) and by
Kenneth Fisher and Meir Statman (2006) have found that neither dividend yields
nor price-to-earnings multiples are useful in generating timing strategies to shift
between stocks and bonds that would generate returns exceeding a simple buyand-hold strategy.
Variance Bound Tests
One kind of empirical test whose results have questioned market efficiency is
called a “variance bound test.” In an efficient market, all assets should be priced at
the discounted present value of all of their cash flows. In one well-known model
of stock valuation popularized by Myron Gordon (1959), the price of a share was
taken to be the discounted present value of the future stream of dividends. Stephen
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LeRoy and Richard Porter (1981), as well as Robert Shiller (1981), then compared
the realized variance of the dividend stream (the components of the ex post present value) with the variance of stock prices. They found that the variance of stock
prices dramatically exceeds the variance of ex post present values. Stock prices
are far too volatile to be explained by the variance of future dividends. Of course,
it is far from clear how much deviation from “true value” is necessary to declare
that stock prices are “too volatile.” In his influential article entitled “Noise,” Fischer
Black (1986) argued that a market should still be considered efficient even if prices
deviate in a range of plus-200 percent and minus-50 percent of fundamental value.
Nevertheless, Shiller concluded that the excess volatility of stock prices implies
that EMH must be false.
Shiller’s conclusion has been extremely controversial. Allan Kleidon (1986) and
Terry Marsh and Robert Merton (1986) showed that with the kinds of sample sizes
used in the tests, sampling variation alone could have generated the Shiller results.
But even if the LeRoy-Porter and Shiller findings survive the statistical critiques,
there are several reasons to be cautious about interpreting the results as inconsistent with EMH. For one thing, it is well established that managers tend to smooth
dividends; therefore, the ex post variance of dividends may understate the true
variance in the fortunes of individual companies. In addition, it is highly unlikely
that either real interest rates or required risk premiums are stable over time. Stock
prices should adjust with changes in required rates of return, and such price volatility may be entirely consistent with EMH.
There is no reason to believe that individual preferences and behavior are stable
over time. Required risk premiums are likely to be influenced by environmental
conditions, and when these conditions change, the behavior of investors can be
expected to change as well. This perspective suggests a more nuanced view of the
world of rational expectations. The approach has been championed by Andrew Lo
and is called the “adaptive markets hypothesis.” This view suggests a quite complicated process to explain the determination of equilibrium risk premiums (see
Farmer and Lo 1999; Lo 2004, 2005; Brennan and Lo 2009).
Bubbles in Asset Prices
Perhaps the most persuasive argument against market efficiency is that securities
markets have often experienced spectacular bubbles. During the so-called Internet
bubble that inflated in the late 1990s, any security associated with the “New
Economy” soared in price. Companies that changed their names to include “dot.
net” or a similar suffix would often double in price. When the bubble popped,
Internet-related stocks lost 90 percent or more of their value. During the housing
bubble in the first decade of the 2000s, the inflation-adjusted price of the median
single family house doubled after being flat for the entire past century. The associated mispricing of mortgage-backed securities had far-reaching consequences for
world financial institutions and for the entire world economy. Critics have considered these episodes to be obvious cases of market inefficiency.
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Bubbles often start with some exogenous factor that can be interpreted rationally
as presenting large future prospects for profit. In England in the early 1700s, it was
the formation of the promising new corporation, the South Sea Company, and the
rise of its stock price. The wave of new companies that followed was expected to
provide profitable investment outlets for the savings of individuals. In the United
States during the late 1990s, it was the promise of the Internet, which was expected
to revolutionize the way consumers obtained information and purchased goods
and services. The generation of sharply rising asset prices that followed, however,
seemed to have more to do with the behavioral biases emphasized by scholars
such as Kahneman and Shiller.2
Kahneman and Tversky (1974, 1979) argued that people forming subjective
judgments tend to disregard base probabilities and to make judgments solely in
terms of observed similarities to familiar patterns. Thus, investors may expect past
price increases to continue even if they know from past experience that all skyrocketing stock markets eventually succumb to the laws of gravity. This phenomenon
was certainly present during the great housing bubble of 2007 to 2008. Investors also
tend to enjoy the self-esteem that comes from having invested early in some “new
era” phenomenon, and they are overconfident of their ability to predict the future.
Shiller (2000) emphasizes the role of “feedback loops” in the propagation of bubbles. Price increases for an asset lead to greater investor enthusiasm, which then
leads to increased demand for the asset and therefore to further price increases.
The very observation that prices have been rising alters the subjective judgment
of investors and reinforces their belief that the price increases will continue. The
news media play a prominent role in increasing the optimism of investors. The
media are, in Shiller’s view, “generators of attention cascades” (60). One news story
begets another, and the price increases themselves (whether of common stocks or
single-family houses) appear to justify the superficially plausible story that started
the rise in the price of the asset(s). According to Shiller, bubbles are inherently a
social phenomenon. A feedback mechanism generates continuing rises in prices
and an interaction back to the conventional wisdom that started the process. The
bubble itself becomes the main topic of social conversation, and stories abound
about certain individuals who have become wealthy from the price increases. As
the economic historian Charles Kindleberger (1989) has stated, “There is nothing
so disturbing to one’s well-being and judgment as to see a friend get rich.”
The question naturally arises why the arbitrage mechanism of EMH does not
prick the bubble as it continues to inflate. Enormous profit opportunities were
certainly achievable during the Internet bubble for speculators who correctly
judged that the prices of many technology stocks were “too high.” But the kind of
arbitrage that would have been necessary was sometimes difficult to effect and in
any event, it was very risky. There appear to be considerable “limits to arbitrage”
(see, for example, Shleifer, Lakonishok, and Vishny 1992; DeLong et al. 1990). For
example, in one celebrated case during the Internet bubble, the market price of
Palm Pilot stock (which was 95 percent–owned by the company 3Com) implied
a total capitalization considerably greater than that of its parent, suggesting that
the rest of 3Com’s business had a negative value. But the arbitrage (sell Palm Pilot
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stock short and buy 3Com stock) could not be achieved because it was impossible
to borrow Palm Pilot stock to accomplish the short sale.
Arbitrage is also risky; one never can be sure when the bubble will burst. The
mantra of hedge fund managers (the natural arbitragers) in the United States
was “markets can remain irrational much longer than we can remain solvent.”
Moreover, some arbitragers may recognize that a bubble exists but are unable to
synchronize their strategies to take advantage of it (see Abreu and Brunnermeier
2003). They might prefer to ride the bubble for as long as possible. Indeed, one
empirical study by Markus Brunnermeier and Stefan Nagel (2004) has found that
rather than shorting Internet stocks, hedge funds were actually buying them during the late 1990s. Hedge funds were embarking on a strategy of anticipating that
the momentum of the price increases would continue and thus were contributing
to the mispricing rather than trading against it.
Critics consider the existence of spectacular bubbles in asset prices “damning”
evidence against the EMH. But even when we know ex post that major errors were
made, there were certainly no clear ex ante arbitrage opportunities available to
rational investors.
Equity valuations rest on uncertain future forecasts. Even if all market participants rationally price common stocks as the present value of all expected future
cash flows, it is still possible for excesses to develop. We know, with the benefit
of hindsight, that the outlandish claims regarding the growth of the Internet (and
the related telecommunications structure needed to support it) were unsupportable. We know now that projections for the rates of growth and the stability and
duration of those growth rates for “New Economy” companies were unsustainable. But neither sharp-penciled professional investors nor quantitative academics
were able to accurately measure the dimensions of the bubble or the timing of its
eventual collapse.
As indicated earlier, there is evidence that initial dividend yields for the market
as a whole have considerable predictive power to explain future long-horizon rates
of return. But during the early 1990s, dividend yields in the United States fell well
below 3 percent, implying very low rates of return for the next five to ten years. In
fact, the U.S. stock market generated unusually large double-digit rates of return
during the entire decade of the 1990s. In 1996, Campbell and Shiller presented a
paper (later published as Campbell and Shiller 1998) to the board of governors
of the U.S. Federal Reserve System showing that price-to-earnings multiples for
the overall market possessed substantial ability to predict future rates of return.
Since P/E multiples were extraordinarily high at that time, the work implied a
likelihood of very low or even negative rates of return. This work influenced Alan
Greenspan (1996), then chairman of the board of governors, to question whether
the stock market was at bubble levels and to suggest that investors were exhibiting “irrational exuberance.” The stock market rallied strongly for more than four
years thereafter. We know now (ex post) that market prices were at bubble levels
in late 1999 and early 2000. No one was able accurately to identify the timing of the
bubble in advance. And certainly no riskless arbitrage opportunities existed, even
at the height of the bubble.
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Hyman Minsky and the 2007 to 2008 Financial Crisis
The financial crisis of 2007 to 2008 reinforces two important lessons that may sometimes be overlooked by policymakers. First, it is critical to distinguish between
asset-price bubbles that are financed by debt and those that inflate without a major
increase in indebtedness. The former are far more dangerous than the latter. The
bursting of the Internet bubble in early 2000 did usher in a period of poor macro
economic performance in the United States and in other world economies. But the
recession that followed was moderate and relatively short-lived. The bursting of
the real estate bubble in 2007 had far more serious consequences. Because individual balance sheets as well as those of financial institutions had become over
extended in debt, there were serious adverse effects on consumer spending and on
the ability and willingness of financial institutions to lend.
The debt-to-income ratios of individuals, which have historically measured
about one-third, rose to a level well above 100 percent during the boom as people bought houses with lower and lower down payments and tapped the equity
in their houses by assuming second mortgages. The leverage ratios of financial
institutions also increased dramatically. The debt-to-equity ratios of investment
banks such as Bear Stearns and Lehman Brothers reportedly exceeded thirty-toone. Moreover, the debt was short-term rather than long-term. As investors in
the short-term paper of those institutions began to worry about the quality of the
mortgage-backed securities on the asset side of the investment banks’ balance
sheets, they refused to roll over their loans and we experienced a classic run on
the banks. Commercial banks also became dangerously overleveraged, and a collapse of the financial system was avoided only by extraordinary measures undertaken by government authorities.
These events give us a renewed appreciation of the work of Hyman Minsky
(1982, 2008), who stressed that stability itself breeds the seeds of instability in a
capitalist system. Periods of economic expansion and relative stability lead individuals and institutions to reduce the premiums they demand to hold risky assets
and to tolerate greater amounts of debt than they had previously accepted. The
increased willingness of borrowers to borrow and lenders to lend leads to a growth
in the availability and flow of credit, which in turn drives up asset prices to levels that may be inconsistent with their “fundamental valuations.” Precautionary
lending practices are replaced with what Minsky has called “Ponzi finance.” Ponzi
loans are characterized as loans to borrowers whose operating cash flow is insufficient to pay down principal so that the loans must continually be refinanced. The
process ends with what has been called a “Minsky Moment.”
Market participants begin to believe that asset prices are “unsustainably
high,” and they attempt to cash in their profits before prices collapse. Lenders are
reluctant to make new loans and refuse to renew the loans already outstanding.
Investors demand higher-risk premiums and attempt to alter the composition of
their portfolios to increase the liquidity of the instruments they hold. As a result of
a rush to exit risky holdings, there are “fire sales” of all risk assets. Prices decline
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dramatically, and markets become less liquid. In the extreme case, a full-fledged
financial crisis ensues.
There is little doubt that the Minsky model seems an especially good description of the recent financial crisis. Minsky’s “financial instability” hypothesis is
also consistent with the insights of behavioral finance and with the tendency of
market systems to experience periodic bubbles. But even when we know ex post
that asset prices were “wrong,” the fundamental characteristic of efficient markets remains valid. Markets can make “mistakes,” sometimes egregious ones, and
those mistakes can have extremely unfortunate macroeconomic consequences. But
there were no obvious ex ante arbitrage opportunities. While some hedge funds
did profit from selling short mortgage-backed securities, other investment funds
and financial institutions went bankrupt because they held long positions in these
same instruments and financed those positions exclusively with short-term debt.3
What Minsky’s work does make clear, however, is that policymakers need to be
very alert to increases in asset prices that are financed with debt. Both the amount
and the maturity of the debt on individual and institutional balance sheets are
crucial variables. It is debt-financed asset-price bubbles that have the most serious
macroeconomic effects.
The “mistakes” that markets sometimes make can also have undesirable microeconomic effects. We count on financial markets to allocate the economy’s scarce
capital resources to the most productive uses. We know that the overpricing of
Internet stocks in 1999 and early 2000 led to the financing of many fanciful business ventures and to an overinvestment in long-distance fiber-optic cable that
was sufficient to span the globe multiple times. We know that during the housing
bubble of the first decade of the 2000s far too many houses were built and, again,
investment capital was badly allocated. The more difficult question is evaluating
the costs and benefits of a market-based allocation system and determining what
it should be compared with. Certainly few would agree that a Soviet-type central
planning system is likely to make better allocation decisions.
The Performance of Professional Investors
Perhaps the most convincing tests of market efficiency are direct tests of the ability of professional investment managers to outperform the market as a whole. If
market prices are generally determined by irrational investors and it is easy to
identify predictable patterns in security returns or exploitable anomalies in security prices, then professional investment managers should be able to beat the
market. Direct tests of the actual returns earned by professionals, who are often
compensated with strong incentives to outperform the market, should represent
the most compelling evidence of market efficiency.
A large body of evidence suggests that professional investment managers are
not able to outperform index funds that buy and hold the broad stock market
portfolio. One of the earliest studies of mutual fund performance was undertaken by Michael Jensen (1968). He found that active fund managers are unable
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FIGURE 4.1
/ Returns for Surviving Funds Compared with Returns for
All Funds
800
Percent Change
700
600
All Funds
Surviving Funds
500
400
300
200
100
0
1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011
Year
Source: Author’s compilation of data from Lipper Analytic Services (various years).
to add value. Using a risk-adjustment model motivated by the capital asset pricing model, he found that actively managed mutual funds tend to underperform
the market by approximately the amount of their added expenses. I repeated
Jensen’s study with data from a subsequent period and confirmed the earlier
results (Malkiel 1995).
Carhart (1997) used a different method of risk adjustment in appraising the
performance of actively managed mutual funds. He measured risk in terms of a
four-factor model. In addition to the CAPM beta, he used the two Fama-French
risk factors of “value” (low price to book value) and “size” as well as a “momentum” factor. Carhart found that most mutual funds underperform the market on
a risk-adjusted basis. Although the best funds are able to earn back their expenses
with higher gross returns, net returns are no better than could be earned by a lowcost, broad-based index fund. Carhart’s study is consistent with previous work
suggesting that professional investors are unable to beat the market.
Studies of mutual fund returns must take account of certain biases in many
data sets. The degree of “survivorship bias” in the data is often substantial. Poorly
performing funds tend to be merged into other funds in the mutual fund’s family complex, thus burying the records of many of the underperformers. Figure 4.1
updates the study I performed during the mid-1990s through the first decade of
the 2000s. The analysis shows that the returns for surviving funds are considerably
better than the actual return for all funds, including funds liquidated or merged
out of existence. Data available for mutual fund returns generally show only
the returns for currently available funds—that is, for those funds that survived.
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TABLE 4.1
/ Percentage of U.S. Equity Funds Outperformed by Benchmarks,
2006 to 2010
Fund Category
Benchmark Index
All domestic equity
All large-cap funds
All mid-cap funds
All small-cap funds
All multi-cap funds
Global funds
International funds
Emerging market funds
S&P 1500
S&P 500
S&P Mid-Cap 400
S&P Small-Cap 600
S&P Small-Cap 1500
S&P Global 1200
S&P 700
S&P/IFCI Composite
Percentage Outperformed
57
62
78
63
66
59
85
86
Source: Author’s compilation based on data from Standard & Poor’s (various years).
Survivorship bias makes the interpretation of long-run mutual fund data sets very
difficult. But even using data sets with some degree of survivorship bias, one cannot sustain the argument that professional investors can beat the market.
Table 4.1 shows the percentage of actively managed mutual funds that have
been outperformed by their relevant passive benchmarks. In general, two-thirds
of actively managed funds are outperformed by their benchmark indexes. Similar
results can be shown for earlier five-year periods, as well as for ten- and twentyyear periods. Moreover, the funds that do have superior records in one base period
are not the same in the next. There is little persistence in mutual fund returns—
with the possible exception of very high-expense, poorly performing funds in one
period, which tend to do poorly in the next. Managed funds are regularly outperformed by broad index funds with equivalent risk. The median actively managed mutual fund underperforms its benchmark by about eighty to ninety basis
points (eight- to nine-tenths of 1 percent), which is approximately the additional
expenses charged by the fund’s management.
Of course, for any period one can find a number of fund managers who have
produced well-above-average returns. But there is no dependable persistence
in performance. During the 1970s, the top twenty mutual funds enjoyed almost
double the performance of the index. During the 1980s, those same funds underperformed the index. The best-performing funds of the 1980s failed to outperform
in the 1990s. And the funds with the best records during the 1990s, which tended
to be those with concentrations of “New Economy” stocks, had disastrous returns
during the first decade of the 2000s.
Figure 4.2 presents a forty-year record of actively managed mutual funds and
the Standard & Poor 500 (S&P 500) stock index, a benchmark frequently used to
measure overall market returns. It plots the performance of all mutual funds that
have been available over the entire period. In 1970 there were 358 equity mutual
funds in the United States. (Today there are thousands of funds.) Only 108 of those
original funds survived through the end of 2010. All we can do is measure the
relative performance versus the market for these surviving funds. We can be sure,
92
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Efficient-Market Hypothesis and the Financial Crisis
FIGURE 4.2
/ Returns of Surviving Mutual Funds Compared with S&P 500
Returns, 1970 to 2010
Number of Equity Funds
30
28
25
23
19
20
15
18
Number of Equity Funds
1970
353
2010
108
Nonsurvivors
250
12
10
5
2
3
1
2
0
G
pe 3 t
rc o
e 4
re nt
at
4 er
pe th
rc an
en
t
pe 2 t
rc o
en 3
t
pe 1 t
rc o
en 2
t
pe 0 t
rc o
en 1
t
Le
ss
t
pe han
rc –
en 4
t
–3
pe to
rc –
en 4
t
–2
pe to
rc –
en 3
t
–
pe 1 to
rc –
en 2
t
0
pe to
rc –
en 1
t
0
Worse than S&P
Better than S&P
Annualized Returns 1970 to 2010
Source: Author’s calculations based on data from Lipper Analytic Services (various years) and the
Vanguard Group (various years).
however, that the 250 funds that did not survive had even worse records. Yet even
though these data are tainted by survivorship bias, we find that the vast majority of
the mutual funds that have been in existence for forty years have underperformed
an index that has served as the basis for the most popular indexed mutual funds
and exchange-traded funds (ETFs). And one can count on the fingers of one hand
the number of professionally managed mutual funds that have outperformed the
S&P 500 index by two percentage points or more per year.
Similar kinds of results have been observed for other professional investors,
such as pension funds and insurance company portfolios (see, for example,
Swensen 2005, 2009). A variety of biases—such as inclusion bias, backfill bias, and
survivorship bias—make the interpretation of hedge fund returns problematic
(see, for example, Malkiel and Saha 2005). But it does not appear that hedge funds,
as a group, are able to produce abnormal returns for their clients.4 If markets were
dominated by irrational investors who make systematic errors in valuing equities,
we should expect that professional investors, who are well incentivized to beat the
market, would realize relatively generous returns. If persistent anomalies were
obvious and bubbles were easy to spot, a simple passively managed equity fund
that buys and holds all the stocks in the market would not display the degree of
superiority that it does.
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Large arbitrage opportunities do not persist. And while markets can and do
make mistakes—some of them horrendous—it is extraordinarily difficult to recognize such situations ex ante. Certainly such examples of mispricing that are recognized ex post do not provide opportunities for risk-adjusted extraordinary returns.
The wisdom of the market appears to produce a tableau of prices that is certainly not always correct but is hard to second-guess. It is therefore difficult for
me to resist the conclusion that our financial markets are remarkably efficient,
and that EMH remains a most useful hypothesis approximating how our financial
markets actually work.
Conclusion
In the final analysis, it is probably useful to think of the stock market in terms of
“reasonable market efficiency” or “relative market efficiency” rather than absolute efficiency. Andrew Lo (2008) has suggested that few engineers would even
contemplate performing a statistical test to determine whether a given engine is
perfectly efficient. But they would attempt to measure the efficiency of that engine
relative to a frictionless ideal. Similarly, it is unrealistic to require our financial
markets to be perfectly efficient in order to accept the basic tenets of EMH. Indeed,
as Sanford Grossman and Joseph Stiglitz (1980) argued, the perfect efficiency of
our financial markets is an unrealizable ideal. Those traders who ensure that information is quickly reflected in market prices must be able at least to cover their
costs. But it is reasonable to ask if our financial markets are relatively efficient, and
I believe that the evidence is very powerful that our markets come very close to
the EMH ideal.
Information does get reflected rapidly in security prices. Thus, to return to our
analogy of the $100 bill lying on the ground, it is highly unlikely that it will stay
there—that is, that we will find those prices persisting for any length of time. There
may well be some loose pennies around. They will be picked up only if justified
by the cost involved in exploiting the opportunities available. Thus, some professional managers may even earn the fees they charge. Their profits, in effect, reflect
economic rents. But what seems abundantly clear is that investors in actively managed funds do not reap any benefits over and above those they would earn from a
low-cost, broad-based, passively managed index fund.
I would draw one more conclusion from this discussion of the efficient-market
hypothesis. EMH and behavioral finance should not be considered as competitive models. Behavioral finance provides important insights into the formation of
expectations and the process by which valuations are determined. And as Hersh
Shefrin and Meir Statman (this volume) make clear, behavioral finance does not
argue that the behavioral biases of investors make the market “beatable.” Moreover,
the insights of Minsky help explain how required risk premiums are influenced
by environmental conditions. Policymakers will be well served by internalizing
Minsky’s central theses that financial markets—even if efficient in the sense I have
used the term—are able to inflict substantial damage on the real economy.
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Efficient-Market Hypothesis and the Financial Crisis
Notes
1. As quoted by Joe Nocera, “Poking Holes in a Theory of Markets,” New York Times,
June 5, 2009, p. 81.
2. For excellent surveys of the behavioral finance literature, see Statman (2010).
3. Highly complex derivatives invite asymmetries of information and therefore present
opportunities for large profits. Such opportunities are inconsistent with the strong form
of EMH, which I have suggested is unlikely to hold in practice. Moreover, Robert Jarrow (this volume) has suggested that some arbitrage opportunities arose from improper ratings published by the rating agencies.
4. Not even the upwardly biased Hedge Fund Research, Inc. (HFRI) hedge fund index has
outperformed the S&P 500 stock index, as shown by Jakub Jurek and Erik Stafford (2011).
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ECON7200 Individual Project– Semester 1, 2021
Student name:
Student ID number:
Total word count: 1242 (excluding references)
1
Introduction
Most industries and firms have been affected seriously by the COVID-19 epidemic. However,
the Nasdaq index of a tech-heavy stock has risen sharply again after the event of the dot-com
bubble from 1992 to 2002. This paper will first summarize the article about the previous dotcom bubble event and then discuss it based on the contents of behavioral economics and the
Efficient Market Hypothesis (EMH). Finally, this essay will compare the situation of the two
dot-com booms and give my opinion on the recent dot-com boom.
Article summary
Goodnight and Green (2010) examined the process of the dot-com bubble event from 1992 to
2002 in this article. The efficient market hypothesis is difficult to explain the development of
bubbles when new investments occur. This article first provides some ideas from three
perspectives of post-conventional economic theories which are new institutional theory,
behavioral economics and performative economics to explain bubbles by connecting imitation
with the markets and participants. Then the article mentioned that from the perspective of
rhetoric, mimesis is the strategic imitation and will have an important impact on the dot-com
bubble event.
Then the article shows the detailed situation of the dot-com bubble event. when the Federal
Reserve decreased the interest rates and the country had abundant credit, economic bubbles
appeared in the 1990s. From 1996, Internet companies and the country's overall economy
developed rapidly and more and more investors invested in these companies which expanded
the bubble to its peak in 2000. As the science of the human informational codes was soon faced
to the public, the commercial opportunities of the internet were limited and stock prices fell
sharply. However, there was a new enthusiasm for real estate after the economic effects of the
dot-com bubble faded in 2000. With the development of new technology and times, it is easier
to appear bubbles.
Explain behavioral economics and discuss the dot-com bubble
The following content will use behavioral economics and the EMH to discuss this dot-com
bubble event. Behavioral economics combines psychology and economics to research the
impact of individual biases and different factors on people's decision making (Camerer, 2014).
Although traditional economics assumes that people are rational, this is not always happening
in real life. Behavioural economics can explain why and how people sometimes make irrational
2
decisions. Besides, behavioral economics also explains how market decisions are made and the
mechanisms that drive public choice. The decisions made by behavioral economics are
different from those from traditional economics which can improve traditional economics.
Behavioural economics provides methods for explaining how the dot-com bubble event
happened. It can combine imitation with irrationality (Goodnight and Green, 2010). When the
initial offerings of Netscape and similar Internet companies highly succeeded around 1996, the
Internet industry was seen as a good investment opportunity that can not be missed. Under the
context of abundant state credit, more and more people joined in the investment which led to
the appearance of the bubbles. Then, more investors imitate others to support these popular
Internet companies directly without rational judgment and analysis as they noticed and trusted
the current high returns. And when most investors make more profit in Internet companies,
they think they made the right decision and persist with it. This is known as confirmation bias
in behavioral economics which people are more willing to support information or results that
are similar to their own. As the investors' interest and hopes for Internet companies are growing,
the bubble continued to expand. Besides, according to the status quo bias and the familiarity
bias in behavioral economics, people prefer to keep the status quo and the investments they are
familiar with rather than explore new alternatives, so the dot-com bubble lasted for a long time.
Until internet business opportunities were restricted and stock prices decreased quickly, the
dot-com bubble began to burst.
The EMH is hard to explain the process of the dot-com bubble event. The EMH means that
stock prices reflect all available information and people can use this information to make a
good estimation of intrinsic value. (Ţiţan, 2015) In this hypothesis, the market's expectation of
future prices is rational. However, new investments boom like the internet happened without
people’s rational valuation and analysis. Therefore, behavioral economics is more suitable to
explain the causes and persist situation of the dot-com bubble compared with the efficient
market hypothesis.
Compare the two booms
Despite the COVID-19 has a serious impact on the global economy from 2019, the share prices
of many technology companies are still largely increasing recently. But in my opinion, this
boom may not be another dot-com bubble and crash. Due to the policy of isolation and
restriction under the context of the COVID-19 epidemic in the world, many companies have
3
developed online businesses (Donthu and Gustafsson, 2020). Many digital and technology
types of companies have developed services businesses and people get more stable profits and
advantages from the digital services. This makes investors regard some Internet companies as
high-quality investment choices, such as Amazon, Microsoft and other technology companies
(The Economist, 2020). According to the status quo bias in behavioral economics, The situation
is similar to the event of the dot-com bubble. More investors are willing to insist on current
investments with high yields. And the country's subsidies and credit policies during the
COVID-19 epidemic also contributed to this technology boom. There are some common
features between these two booms as they are all related to the increase of new investment
capital inflows and the abundant credit policies. These will directly lead to a significant
increase in stock prices.
Although the two booms had similar features, the current boom may not be the same
uncontrollable as the dot-com bubble. In the 1990s, the growth rate of total factor productivity
was very high which also represents the rapid pace of technological progress (Chou, Hao-Chun
Chuang and Shao, 2014). And at that time, companies’ profits were growing fast and investors
grasped the profit opportunities well so that the investment in computer equipment and
software was rising quickly. While from the beginning of the 21st century, productivity growth
has been slowing down. Even the companies' current profits are higher than 20 years ago, the
investment in technology only accounts for a very small share of GDP around the 2010s
(Mackenzie, 2021).
Moreover, even large amount of companies’ economy was affected by the epidemic of
COVID-19, there still exist some internet and technology companies performed well during
the pandemic (Fang, Guo, Hu and Zhuang, 2020). This indicates that some of the companies
have better resistance to risks when special events occur. From the perspective of rational
investors, they should accept the risks during the investment. But, according to loss aversion
in behavioral economics, some investors are more likely to avoid losses than making
investment gains. So, they will choose to invest few companies with better resistance to risks
and this will reduce the possibility of the appearance of bubbles. So in my opinion, the current
boom may not be another dot-com bubble and crash.
4
Conclusion
During the dot-com boom, the processes of people's decision making and preferences all had a
huge impact on the happening of the dot-com bubble. This essay mainly analyzes the condition
of the two dot-com booms using the content of behavioral economics. Although there are some
similarities between these two booms, the recent boom will not be as serious and uncontrollable
as the previous boom. So this may not be another dot-com bubble and crash.
5
References
Camerer, C., 2014. Behavioral economics. Current Biology, 24(18), pp.R867-R871.
Chou, Y., Hao-Chun Chuang, H. and Shao, B., 2014. The impacts of information technology
on total factor productivity: A look at externalities and innovations. International Journal of
Production Economics, 158, pp.290-299.
Donthu, N. and Gustafsson, A., 2020. Effects of COVID-19 on business and research. Journal
of Business Research, 117, pp.284-289.
Fang, Y., Guo, G., Hu, Y. and Zhuang, L., 2020. Research on Opportunities of the Internet
Industry Under the COVID-19 Epidemic. Proceedings of the 2020 International Confere...
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