Description
Investors are not always rational in the way they set expectations. These irrationalities may lead to expectations being set too low for some assets at some times and too high for other assets at other times.
Aswath Damodaran 1
Smoke and Mirrors: Price patterns,
charts and technical analysis
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The Random Walk Hypothesis
Current Next period
Stock price is an unbiased
estimate of the value of the
stock.
Information
Price
Assessment
Implications for
Investors
No approach or model will allow us to
identify under or over valued assets.
New information comes out about the
firm.
All information about the firm is
publicly available and traded on.
The price changes in accordance with the
information. If it contains good (bad)
news, relative to expectations, the stock
price will increase (decrease).
Reflecting the 50/50 chance of the news
being good or bad, there is an equal
probability of a price increase and a price
decrease.
Market
Expectations
Investors form unbiased
expectations about the
future
Since expectations are unbiased,
there is a 50% chance of good or
bad news.
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The Basis for Price Patterns
1. Investors are not always rational in the way they set expectations.
These irrationalities may lead to expectations being set too low for
some assets at some times and too high for other assets at other times.
Thus, the next piece of information is more likely to contain good
news for the ?rst asset and bad news for the second.
2. Price changes themselves may provide information to markets. Thus,
the fact that a stock has gone up strongly the last four days may be
viewed as good news by investors, making it more likely that the price
will go up today then down.
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The Empirical Evidence on Price Patterns
! Investors have used price charts and price patterns as tools for
predicting future price movements for as long as there have been
?nancial markets.
! The ?rst studies of market ef?ciency focused on the relationship
between price changes over time, to see if in fact such predictions
were feasible.
! Evidence can be classi?ed into three classes
• Studies that looks at the really short term (hourly, daily) price behavior
• studies that focus on short-term (weekly, monthly price movements) price
behavior and
research that examines long-term (annual and ?ve-year returns) price movements.
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Testing for price patterns
! Serial correlation, where you look at how price changes in a period are
correlated with price changes in prior periods
! Runs tests, where you look at sequences of “up” or “down” periods
and test them against randomness.
! Filter rules and relative strength, where you examine whether
investment strategies based upon past price performance beat the
market.
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Serial correlation
! Serial correlation measures the correlation between price changes
in consecutive time periods
! Measure of how much price change in any period depends upon price
change over prior time period.
0: imply that price changes in consecutive time periods are
uncorrelated with each other
>0: evidence of price momentum in markets
<0: Evidence of price reversals
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Serial Correlation and Excess Returns
! From viewpoint of investment strategy, serial correlations can be
exploited to earn excess returns.
• A positive serial correlation would be exploited by a strategy of buying after
periods with positive returns and selling after periods with negative returns.
• A negative serial correlation would suggest a strategy of buying after periods with
negative returns and selling after periods with positive returns.
• The correlations must be large enough for investors to generate pro?ts to cover
transactions costs.
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1. Serial Correlation in really short-term returns
! Low or no serial correlation: The earliest studies of serial correlation
all looked at large U.S. stocks and concluded that the serial correlation
in stock prices was small. Other studies con?rmed these ?ndings – of
very low correlation, positive or negative - not only for smaller stocks
in the United States, but also for other markets.
! Market liquidity effect: If markets are not liquid, you will see serial
correlation in index returns.
! Bid-ask spread effect: The bid-ask spread creates a bias in the opposite
direction, if transactions prices are used to compute returns, since
prices have a equal chance of ending up at the bid or the ask price. The
bounce that this induces in prices will result in negative serial
correlations in returns.
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And it is really dif?cult to make money off really short term
correlations..
Buy
Sell
Up X%
Down X%
Price
Time
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Returns on Filter Rule Strategies
Value of X Return with Return with No of Return
Strategy Buy & Hold Trades after costs
0.5% 11.5% 10.4% 12,514 -103.6%
1.0% 5.5% 10.3% 8,660 -74.9%
2.0% 0..2% 10.3% 4,764 -45.2%
3.0% -1.7% 10.1% 2,994 -30.5%
4.0% 0.1% 10.1% 2,013 -19.5%
5.0% -1.9% 10.0% 1,484 -16.6%
6.0% 1.3% 9.7% 1,071 -9.4%
8.0% 1.7% 9.6% 653 -5.0%
10.0% 3.0% 9.6% 435 -1.4%
12.0% 5.3% 9.4% 289 2.3%
14.0% 3.9% 10.3% 224 1.4%
16.0% 4.2% 10.3% 172 2.3%
18.0% 3.6% 10.0% 139 2.0%
20.0% 4.3% 9.8% 110 3.0%
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Results of Study
! The only ?lter rule that beats the returns from the buy and hold
strategy is the 0.5% rule, but it does so before transactions costs.
This strategy creates 12,514 trades during the period which generate
enough transactions costs to wipe out the principal invested by the
investor.
! While this test is dated, it also illustrates a basic problem with
strategies that require frequent short term trading. Even though
these strategies may earn excess returns prior to transactions costs,
adjusting for these costs can wipe out the excess returns.
! The advent of computerized high-frequency trading has opened one
possible window to making money off really small correlations in the
short term.
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2. Serial correlation in the short term
! As you move from hours and days to weeks or a month, there seems to
be some evidence that prices reverse. In other words, stocks that have
done well over the last month are more likely to do badly in the next
one and stocks that have done badly over the last month are more
likely to bounce back.
! The reasons given are usually rooted in market over reaction, i.e,. that
the stocks that have gone up (down) the most over the most recent
month are ones where markets have over reacted to good (bad) news
that came out about the stock over the month. The price reversaal than
re?ects markets correcting themselves.
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Returns from Momentum in short term
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3. Serial correlation in the medium term
! When time is de?ned as many months or a year, rather than a single
month, there seems to be a tendency towards positive serial
correlation.
! Jegadeesh and Titman present evidence of what they call “price
momentum” in stock prices over time periods of several months –
stocks that have gone up in the last six months tend to continue to go
up whereas stocks that have gone down in the last six months tend to
continue to go down.
! Between 1945 and 2008, if you classi?ed stocks into deciles based
upon price performance over the previous year, the annual return you
would have generated by buying the stocks in th the top decile and
held for the next year was 16.5% higher than the return you would
have earned on the stocks in the bottom decile.
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Annual returns from momentum classes (based upon most
recent year)
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More “evidence” on momentum
! Volume effect: Momentum accompanied by higher trading volume is
stronger and more sustained than momentum with low trading volume.
! Size effect: While some of the earlier studies suggest that momentum
is stronger at small market cap companies, a more recent study that
looks at US stocks from 1926 to 2009 ?nds the relationship to be a
weak one, though it does con?rm that there are sub periods
(1980-1996) where momentum and ?rm size are correlated.
! Upside vs Downside: The conclusions seem to vary, depending on the
time period examined, with upside momentum dominating over very
long time periods (1926-2009) and downside momentum winning out
over some sub-periods (such as the 1980-1996).
! Growth effect: Price momentum is more sustained and stronger for
higher growth companies with higher price to book ratios than for
more mature companies with lower price to book ratios.
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Long Term Serial Correlation
! In contrast to the studies of short term correlation, there is evidence of
strong correlation in long term returns.
! When long term is de?ned as months, there is positive correlation - a
momentum effect.
! When long term is de?ned as years, there is negative correlation -
reversal in prices. The effect is much stronger for smaller companies.
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Evidence of long term correlation
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The tipping point… Momentum works, until it does not..
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Extreme Momentum: Bubbles..
! Looking at the evidence on price patterns, there is evidence of both
price momentum (in the medium term) and price reversal (in the short
and really long term).
! Read together, you have the basis for price bubbles: the momentum
creates the bubble and the crash represents the reversal.
! Through the centuries, markets have boomed and busted, and in the
aftermath of every bust, irrational investors have been blamed for the
crash.
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Blooper versus Bubble
! Blooper
• Rational markets can make mistakes. Assessments of value are based upon
expectations, which are formed with the information that is available at the time of
the assessments.You will be wrong a lot of the time and very wrong some of the
time.
• It is therefore entirely possible and very likely, even in an ef?cient market, to see
signi?cant pricing errors.
! Bubble
• A bubble is a willful error, suggestive of irrational behavior at some level.
• This irrational behavior manifests itself as an unwillingness or incapacity on the
part of investors in the market to face up to reality.
! Separating bloopers from bubbles is dif?cult. There is a tendency on
the part of some (the anti-market ef?ciency crowd) to view all big
price adjustments as evidence of bubbles, just as there is a tendency on
the part of the others (the true believers in market ef?ciency) to view
all big price adjustments as evidence of bloopers.
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Is this a bubble?
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What about this one?
Figure 7.12: The Tech Boom
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Or this?
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Deconstructing a Bubble
! There are four phases in every bubble, though the length of each phase
may vary from bubble to bubble.
• The formation of the bubble
• The sustenance of the bubble
• The bursting of the bubble
• The after-math
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The Birth of a Bubble
! Most bubbles have their genesis in a kernel of truth. In other words, at
the heart of each bubble is a perfectly sensible story.
! The bubble builds as
• Positive reinforcement is provided to irrational or ill-thought out actions on the part
of some investors.
• News about the success of these investors is broadcast to the rest of the market.
• Other investors imitate the ?rst movers and create a self-ful?lling prophecy…
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The Sustenance of a Bubble
! Institutional Parasites: Institutions, individuals and other entities make
money off the bubble and develop vested interests in preserving and
expanding the bubble. These include
• Investment bankers
• Brokers
• Portfolio managers
! Support is provided for the bubble by academics and
intellectuals(well-meaning or otherwise) who
• Proclaim that the old rules no longer apply because
• Claim new paradigms…
• Disparage those who do not buy into the bubble as being old-fashioned
Aswath Damodaran 28
The Bursting of a Bubble
! There is usually no single precipitating event that causes bubbles to
burst, but a con?uence of factors.
• You run out of suckers. The investors who are your best targets are already fully
invested in the bubble.
• You become exhausted trying to explain the unexplainable…
• Each new entry into the bubble is more outrageous than the previous one and more
dif?cult to explain.
! The ?rst hint of doubt among the true believers very quickly turns to
panic as reality sets in…Well devised exit strategies break down as
everyone heads for the exit doors at the same time.
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The Aftermath
o “I was smarter than the average investor” : You cannot ?nd anyone
who lost money when the bubble burst. They all claim either that they
never invested in it ( denial) or that they saw the correction coming
and got out in time (hindsight).
! “It was the investment banker’s fault”: Investors look for someone to
blame and the forces that sustain the bubble (the bubble parasites and
intellectuals) become the obvious targets.
! “I will never invest in a bubble again”: Everyone claims that they have
learned their lessons and will not be taken in again.
Aswath Damodaran 30
Seasonal and Temporal Effects on Prices
! Empirical studies indicate a variety of seasonal and temporal
irregularities in stock prices. Among them are:
• The January Effect: Stocks, on average, tend to do much better in January than in
any other month of the year.
• The Weekend Effect: Stocks, on average, seem to do much worse on Mondays than
on any other day of the week.
• The Mid-day Swoon: Stocks, on average, tend to do much worse in the middle of
the trading day than at the beginning and end of the day.
! While these empirical irregularities provide for interesting
conversation, it is not clear that any of them can be exploited to
earn excess returns.
Aswath Damodaran 31
A.The January Effect
! Studies of returns in the United States and other major ?nancial
markets consistently reveal strong differences in return behavior
across the months of the year.
! Returns in January are signi?cantly higher than returns in any
other month of the year. This phenomenon is called the year-end or
January effect, and it can be traced to the ?rst two weeks in January.
! The January effect is much more accentuated for small ?rms than
for larger ?rms, and roughly half of the small ?rm premium, described
in the prior section, is earned in the ?rst two days of January.
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Returns in January
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Explanations for the January Effect
! A number of explanations have been advanced for the January effect,
but few hold up to serious scrutiny.
• Tax loss selling by investors at the end of the year on stocks which have 'lost
money' to capture the capital gain, driving prices down, presumably below true
value, in December, and a buying back of the same stocks in January, resulting in
the high returns.Since wash sales rules would prevent an investor from selling and
buying back the same stock within 45 days, there has to be some substitution
among the stocks. Thus investor 1 sells stock A and investor 2 sells stock B, but
when it comes time to buy back the stock, investor 1 buys stock B and investor 2
buys stock A.
• A second rationale is that the January effect is related to institutional trading
behavior around the turn of the years. It has been noted, for instance, that ratio of
buys to sells for institutions drops signi?cantly below average in the days before the
turn of the year and picks to above average in the months that follow.
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The Size Effect in January
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Institutional Buying/Selling around Year-end
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Returns in January vs Other Months - Major Financial
Markets
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B. The Weekend Effect
! The weekend effect is another phenomenon that has persisted over
long periods and over a number of international markets. It refers to
the differences in returns between Mondays and other days of the
week.
! Over the years, returns on Mondays have been consistently lower than
returns on other days of the week.
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Returns by Weekday
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The Weekend Effect in International Markets
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Has it held up?
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The Weekend Effect: Explanations
! First, the Monday effect is really a weekend effect since the bulk of
the negative returns is manifested in the Friday close to Monday
open returns. The returns from intraday returns on Monday are not
the culprits in creating the negative returns.
! Second, the Monday effect is worse for small stocks than for larger
stocks.
Third, the Monday effect is no worse following three-day weekends
than two-day weekends.
! There are some who have argued that the weekend effect is the result
of bad news being revealed after the close of trading on Friday and
during the weekend. Even if this were a widespread phenomenon, the
return behavior would be inconsistent with a rational market, since
rational investors would build in the expectation of the bad news over
the weekend into the price before the weekend, leading to an
elimination of the weekend effect.
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The Holiday Effect: Is there one?
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Volume and Price: The Evidence
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Foundations of Technical Analysis: What are the
assumptions?
(1) Price is determined solely by the interaction of supply & demand
(2) Supply and demand are governed by numerous factors both
rational and irrational. The market continually and automatically
weighs all these factors. (A random walker would have no qualms
about this assumption either. He would point out that any irrational
factors are just as likely to be one side of the market as on the other.)
(3) Disregarding minor ?uctuations in the market, stock prices tend
to move in trends which persist for an appreciable length of time.
( Random walker would disagree with this statement. For any trend to
persist there has to be some collective 'irrationality')
(4) Changes in trend are caused by shifts in demand and supply.
These shifts no matter why they occur, can be detected sooner or
later in the action of the market itself. (In the ?nancial economist's
view the market (through the price) will instantaneously re?ect any
shifts in the demand and supply.
Aswath Damodaran 45
On why technical analysts think it is futile to estimate
intrinsic values
! "It is futile to assign an intrinsic value to a stock certi?cate. One share
of US Steel , for example, was worth $261 in the early fall of 1929,
but you could buy it for only $22 in June 1932. By March 1937 it was
selling for $126 and just one year later for $38. ... This sort of thing,
this wide deivergence between presumed value and intrinsic value, is
not the exception; it is the rule; it is going on all the time. The fact is
that the real value of US Steel is determined at any give time
solely, de?nitely and inexorably by supply and demand, which are
accurately re?ected in the transactions consummated on the ?oor
of the exchange.” (From Magee on Technical Analysis)
Aswath Damodaran 46
I. Markets overreact: The Contrarian Indicators
Basis: Research in experimental psychology suggests that people tend to
overreact to unexpected and dramatic news events. In revising their
beliefs, individuals tend to overweight recent information and
underweight prior data.
Empirical evidence: If markets overreact then
(1) Extreme movements in stock prices will be followed by subsequent
price movements in the opposite direction.
(2) The more extreme the price adjustment, the greater will be the
subsequent adjustment
Aswath Damodaran 47
Issues in Using Contrarian Indicators
(1) Why, if this is true, is is that contrarian investors are so few in number
or market power that the overreaction to new information is allowed to
continue for so long?
(2) In what sense does this phenomenon justify th accusation that the
market is inef?cient?
(3) Is the market more ef?cient about incorporating some types of
information than others?
Aswath Damodaran 48
Technical trading rules: Contrarian Opinion
1. Odd-lot trading: The odd-lot rule gives us an indication of what the
man on the street thinks about the stock (As he gets more enthusiastic
about a stock this ratio will increase).
2. Mutual Fund Cash positions: Historically, the argument goes, mutual
fund cash positions have been greatest at the bottom of a bear market
and lowest at the peak of a bull market. Hence investing against this
statistic may be pro?table.
3. Investment Advisory opinion: This is the ratio of advisory services that
are bearish. When this ratio reaches the threshold (eg 60%) the
contrarian starts buying.
Aswath Damodaran 49
II. Detecting shifts in Demand & Supply: The Lessons in
Price Patterns
Aswath Damodaran 50
1. Breadth of the market
Measure: This is a measure of the number of stocks in the market which
have advanced relative to those that have declined. The broader the
market, the stronger the demand.
Related measures:
(1) Divergence between different market indices (Dow 30 vs NYSE
composite)
(2) Advance/Decline lines
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2. Support and Resistance Lines
A common explanation given by technicians for market movements is that
markets have support and resistance lines. If either is broken, the
market is poised for a major move.
Aswath Damodaran 52
Possible Rationale
(1) Institutional buy/sell programs which can be triggered by
breakthrough of certain well de?ned price levels (eg. Dow 1300)
(2) Self ful?lling prophecies: Money managers use technical analysis for
window dressing.
Aswath Damodaran 53
3. Moving Averages
! A number of indicators are built on looking at moving averages of
stock prices over time. A moving average line smooths out ?uctuations
and enables the chartist to see trends in the stock price. How that trend
is interpreted then depends upon the chartist.
Aswath Damodaran 54
4. Volume Indicators
Some technical analysts believe that there is information about future
price changes in trading volume shifts.
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5. Point and Figure Charts
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III. Market learn slowly: The Momentum Investors
Basis: The argument here is that markets learn slowly. Thus, investors
who are a little quicker than the market in assimilating and
understanding information will earn excess returns. In addition, if
markets learn slowly, there will be price drifts (i.e., prices will move
up or down over extended periods) and technical analysis can detect
these drifts and take advantage of them.
The Evidence: There is evidence, albeit mild, that prices do drift after
signi?cant news announcements. For instance, following up on price
changes after large earnings surprises provides the following evidence.
Aswath Damodaran 57
Momentum Indicators
! Relative Strength: The relative strength of a stock is the ratio of its
current price to its average over a longer period (eg. six months). The
rule suggests buying stocks which have the highest relative strength
(which will also be the stocks that have gone up the most in that
period).
! Trend Lines: You look past the day-to-day movements in stock prices
at the underlying long-term trends. The simplest measure of trend is a
trend line.
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IV. Following the Smart Investors: The Followers
! This approach is the ?ip side of the contrarian approach. Instead of
assuming that investors, on average, are likely to be be wrong, you
assume that they are right.
! To make this assumption more palatable, you do not look at all
investors but only at the smartest investors, who presumably know
more than the rest of us.
Aswath Damodaran 59
Specialist Short Sales
! The assumption is that specialists have more information about future
price movements than other investors. Consequently, when they sell
short, they must know that the stock is overvalued.
! Investors who use this indicator will often sell stocks when specialists
do, and buy when they do.
Aswath Damodaran 60
Insider Buying and Selling
! You can look up stocks where insider buying or selling has increased
the most.
! The ratio of insider buying to selling is often tracked for stocks with
the idea that insiders who are buying must have positive information
about a stock whereas insiders who are selling are likely to have
negative information.
Aswath Damodaran 61
V. Markets are controlled by external forces: The Mystics
The Elliot Wave: Elliot's theory is that the market moves in waves of
various sizes, from those encompassing only individual trades to those
lasting centuries, perhaps longer. "By classifying these waves and
counting the various classi?cations it is possible to determine the
relative positions of the market at all times". "There can be no bull of
bear markets of one, seven or nine waves, for example.
The Dow Theory:" The market is always considered as having three
movements, all going at the same time. The ?rst is the narrow
movement (daily ?uctuations) from day to day. The second is the short
swing (secondary movements) running from two weeks to a month
and the third is the main movement (primary trends) covering at least
four years in its duration.
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The Dow Theory
Upward
primary trend
Downward
primary trend
Upward
primary trend
Secondary
movements
Time
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The Elliott Wave
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Determinants of Success at Technical Analysis
• If you decide to use a charting pattern or technical indicator, you need to be
aware of the investor behavior that gives rise to its success. You can modify or
abandon the indicator if the underlying behavior changes.
• It is important that you back-test your indicator to ensure that it delivers the
returns that are promised. In running these tests, you should pay particular
attention to the volatility in performance over time and how sensitive the
returns are to holding periods.
• The excess returns on many of the strategies that we described in this chapter
seem to depend upon timely trading. In other words, to succeed at some of
these strategies, you may need to monitor prices continuously, looking for the
patterns that would trigger trading.
• Building on the theme of time horizons, success at charting can be very
sensitive to how long you hold an investment.
• The strategies that come from technical indicators are generally short-term
strategies that require frequent and timely trading. Not surprisingly, these
strategies also generate large trading costs that can very quickly eat into any
excess returns you may have.
doc_215258639.pdf
Investors are not always rational in the way they set expectations. These irrationalities may lead to expectations being set too low for some assets at some times and too high for other assets at other times.
Aswath Damodaran 1
Smoke and Mirrors: Price patterns,
charts and technical analysis
Aswath Damodaran
Aswath Damodaran 2
The Random Walk Hypothesis
Current Next period
Stock price is an unbiased
estimate of the value of the
stock.
Information
Price
Assessment
Implications for
Investors
No approach or model will allow us to
identify under or over valued assets.
New information comes out about the
firm.
All information about the firm is
publicly available and traded on.
The price changes in accordance with the
information. If it contains good (bad)
news, relative to expectations, the stock
price will increase (decrease).
Reflecting the 50/50 chance of the news
being good or bad, there is an equal
probability of a price increase and a price
decrease.
Market
Expectations
Investors form unbiased
expectations about the
future
Since expectations are unbiased,
there is a 50% chance of good or
bad news.
Aswath Damodaran 3
The Basis for Price Patterns
1. Investors are not always rational in the way they set expectations.
These irrationalities may lead to expectations being set too low for
some assets at some times and too high for other assets at other times.
Thus, the next piece of information is more likely to contain good
news for the ?rst asset and bad news for the second.
2. Price changes themselves may provide information to markets. Thus,
the fact that a stock has gone up strongly the last four days may be
viewed as good news by investors, making it more likely that the price
will go up today then down.
Aswath Damodaran 4
The Empirical Evidence on Price Patterns
! Investors have used price charts and price patterns as tools for
predicting future price movements for as long as there have been
?nancial markets.
! The ?rst studies of market ef?ciency focused on the relationship
between price changes over time, to see if in fact such predictions
were feasible.
! Evidence can be classi?ed into three classes
• Studies that looks at the really short term (hourly, daily) price behavior
• studies that focus on short-term (weekly, monthly price movements) price
behavior and
research that examines long-term (annual and ?ve-year returns) price movements.
Aswath Damodaran 5
Testing for price patterns
! Serial correlation, where you look at how price changes in a period are
correlated with price changes in prior periods
! Runs tests, where you look at sequences of “up” or “down” periods
and test them against randomness.
! Filter rules and relative strength, where you examine whether
investment strategies based upon past price performance beat the
market.
Aswath Damodaran 6
Serial correlation
! Serial correlation measures the correlation between price changes
in consecutive time periods
! Measure of how much price change in any period depends upon price
change over prior time period.
0: imply that price changes in consecutive time periods are
uncorrelated with each other
>0: evidence of price momentum in markets
<0: Evidence of price reversals
Aswath Damodaran 7
Serial Correlation and Excess Returns
! From viewpoint of investment strategy, serial correlations can be
exploited to earn excess returns.
• A positive serial correlation would be exploited by a strategy of buying after
periods with positive returns and selling after periods with negative returns.
• A negative serial correlation would suggest a strategy of buying after periods with
negative returns and selling after periods with positive returns.
• The correlations must be large enough for investors to generate pro?ts to cover
transactions costs.
Aswath Damodaran 8
1. Serial Correlation in really short-term returns
! Low or no serial correlation: The earliest studies of serial correlation
all looked at large U.S. stocks and concluded that the serial correlation
in stock prices was small. Other studies con?rmed these ?ndings – of
very low correlation, positive or negative - not only for smaller stocks
in the United States, but also for other markets.
! Market liquidity effect: If markets are not liquid, you will see serial
correlation in index returns.
! Bid-ask spread effect: The bid-ask spread creates a bias in the opposite
direction, if transactions prices are used to compute returns, since
prices have a equal chance of ending up at the bid or the ask price. The
bounce that this induces in prices will result in negative serial
correlations in returns.
Aswath Damodaran 9
And it is really dif?cult to make money off really short term
correlations..
Buy
Sell
Up X%
Down X%
Price
Time
Aswath Damodaran 10
Returns on Filter Rule Strategies
Value of X Return with Return with No of Return
Strategy Buy & Hold Trades after costs
0.5% 11.5% 10.4% 12,514 -103.6%
1.0% 5.5% 10.3% 8,660 -74.9%
2.0% 0..2% 10.3% 4,764 -45.2%
3.0% -1.7% 10.1% 2,994 -30.5%
4.0% 0.1% 10.1% 2,013 -19.5%
5.0% -1.9% 10.0% 1,484 -16.6%
6.0% 1.3% 9.7% 1,071 -9.4%
8.0% 1.7% 9.6% 653 -5.0%
10.0% 3.0% 9.6% 435 -1.4%
12.0% 5.3% 9.4% 289 2.3%
14.0% 3.9% 10.3% 224 1.4%
16.0% 4.2% 10.3% 172 2.3%
18.0% 3.6% 10.0% 139 2.0%
20.0% 4.3% 9.8% 110 3.0%
Aswath Damodaran 11
Results of Study
! The only ?lter rule that beats the returns from the buy and hold
strategy is the 0.5% rule, but it does so before transactions costs.
This strategy creates 12,514 trades during the period which generate
enough transactions costs to wipe out the principal invested by the
investor.
! While this test is dated, it also illustrates a basic problem with
strategies that require frequent short term trading. Even though
these strategies may earn excess returns prior to transactions costs,
adjusting for these costs can wipe out the excess returns.
! The advent of computerized high-frequency trading has opened one
possible window to making money off really small correlations in the
short term.
Aswath Damodaran 12
2. Serial correlation in the short term
! As you move from hours and days to weeks or a month, there seems to
be some evidence that prices reverse. In other words, stocks that have
done well over the last month are more likely to do badly in the next
one and stocks that have done badly over the last month are more
likely to bounce back.
! The reasons given are usually rooted in market over reaction, i.e,. that
the stocks that have gone up (down) the most over the most recent
month are ones where markets have over reacted to good (bad) news
that came out about the stock over the month. The price reversaal than
re?ects markets correcting themselves.
Aswath Damodaran 13
Returns from Momentum in short term
Aswath Damodaran 14
3. Serial correlation in the medium term
! When time is de?ned as many months or a year, rather than a single
month, there seems to be a tendency towards positive serial
correlation.
! Jegadeesh and Titman present evidence of what they call “price
momentum” in stock prices over time periods of several months –
stocks that have gone up in the last six months tend to continue to go
up whereas stocks that have gone down in the last six months tend to
continue to go down.
! Between 1945 and 2008, if you classi?ed stocks into deciles based
upon price performance over the previous year, the annual return you
would have generated by buying the stocks in th the top decile and
held for the next year was 16.5% higher than the return you would
have earned on the stocks in the bottom decile.
Aswath Damodaran 15
Annual returns from momentum classes (based upon most
recent year)
Aswath Damodaran 16
More “evidence” on momentum
! Volume effect: Momentum accompanied by higher trading volume is
stronger and more sustained than momentum with low trading volume.
! Size effect: While some of the earlier studies suggest that momentum
is stronger at small market cap companies, a more recent study that
looks at US stocks from 1926 to 2009 ?nds the relationship to be a
weak one, though it does con?rm that there are sub periods
(1980-1996) where momentum and ?rm size are correlated.
! Upside vs Downside: The conclusions seem to vary, depending on the
time period examined, with upside momentum dominating over very
long time periods (1926-2009) and downside momentum winning out
over some sub-periods (such as the 1980-1996).
! Growth effect: Price momentum is more sustained and stronger for
higher growth companies with higher price to book ratios than for
more mature companies with lower price to book ratios.
Aswath Damodaran 17
Long Term Serial Correlation
! In contrast to the studies of short term correlation, there is evidence of
strong correlation in long term returns.
! When long term is de?ned as months, there is positive correlation - a
momentum effect.
! When long term is de?ned as years, there is negative correlation -
reversal in prices. The effect is much stronger for smaller companies.
Aswath Damodaran 18
Evidence of long term correlation
Aswath Damodaran 19
The tipping point… Momentum works, until it does not..
Aswath Damodaran 20
Extreme Momentum: Bubbles..
! Looking at the evidence on price patterns, there is evidence of both
price momentum (in the medium term) and price reversal (in the short
and really long term).
! Read together, you have the basis for price bubbles: the momentum
creates the bubble and the crash represents the reversal.
! Through the centuries, markets have boomed and busted, and in the
aftermath of every bust, irrational investors have been blamed for the
crash.
Aswath Damodaran 21
Blooper versus Bubble
! Blooper
• Rational markets can make mistakes. Assessments of value are based upon
expectations, which are formed with the information that is available at the time of
the assessments.You will be wrong a lot of the time and very wrong some of the
time.
• It is therefore entirely possible and very likely, even in an ef?cient market, to see
signi?cant pricing errors.
! Bubble
• A bubble is a willful error, suggestive of irrational behavior at some level.
• This irrational behavior manifests itself as an unwillingness or incapacity on the
part of investors in the market to face up to reality.
! Separating bloopers from bubbles is dif?cult. There is a tendency on
the part of some (the anti-market ef?ciency crowd) to view all big
price adjustments as evidence of bubbles, just as there is a tendency on
the part of the others (the true believers in market ef?ciency) to view
all big price adjustments as evidence of bloopers.
Aswath Damodaran 22
Is this a bubble?
Aswath Damodaran 23
What about this one?
Figure 7.12: The Tech Boom
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Aswath Damodaran 24
Or this?
Aswath Damodaran 25
Deconstructing a Bubble
! There are four phases in every bubble, though the length of each phase
may vary from bubble to bubble.
• The formation of the bubble
• The sustenance of the bubble
• The bursting of the bubble
• The after-math
Aswath Damodaran 26
The Birth of a Bubble
! Most bubbles have their genesis in a kernel of truth. In other words, at
the heart of each bubble is a perfectly sensible story.
! The bubble builds as
• Positive reinforcement is provided to irrational or ill-thought out actions on the part
of some investors.
• News about the success of these investors is broadcast to the rest of the market.
• Other investors imitate the ?rst movers and create a self-ful?lling prophecy…
Aswath Damodaran 27
The Sustenance of a Bubble
! Institutional Parasites: Institutions, individuals and other entities make
money off the bubble and develop vested interests in preserving and
expanding the bubble. These include
• Investment bankers
• Brokers
• Portfolio managers
! Support is provided for the bubble by academics and
intellectuals(well-meaning or otherwise) who
• Proclaim that the old rules no longer apply because
• Claim new paradigms…
• Disparage those who do not buy into the bubble as being old-fashioned
Aswath Damodaran 28
The Bursting of a Bubble
! There is usually no single precipitating event that causes bubbles to
burst, but a con?uence of factors.
• You run out of suckers. The investors who are your best targets are already fully
invested in the bubble.
• You become exhausted trying to explain the unexplainable…
• Each new entry into the bubble is more outrageous than the previous one and more
dif?cult to explain.
! The ?rst hint of doubt among the true believers very quickly turns to
panic as reality sets in…Well devised exit strategies break down as
everyone heads for the exit doors at the same time.
Aswath Damodaran 29
The Aftermath
o “I was smarter than the average investor” : You cannot ?nd anyone
who lost money when the bubble burst. They all claim either that they
never invested in it ( denial) or that they saw the correction coming
and got out in time (hindsight).
! “It was the investment banker’s fault”: Investors look for someone to
blame and the forces that sustain the bubble (the bubble parasites and
intellectuals) become the obvious targets.
! “I will never invest in a bubble again”: Everyone claims that they have
learned their lessons and will not be taken in again.
Aswath Damodaran 30
Seasonal and Temporal Effects on Prices
! Empirical studies indicate a variety of seasonal and temporal
irregularities in stock prices. Among them are:
• The January Effect: Stocks, on average, tend to do much better in January than in
any other month of the year.
• The Weekend Effect: Stocks, on average, seem to do much worse on Mondays than
on any other day of the week.
• The Mid-day Swoon: Stocks, on average, tend to do much worse in the middle of
the trading day than at the beginning and end of the day.
! While these empirical irregularities provide for interesting
conversation, it is not clear that any of them can be exploited to
earn excess returns.
Aswath Damodaran 31
A.The January Effect
! Studies of returns in the United States and other major ?nancial
markets consistently reveal strong differences in return behavior
across the months of the year.
! Returns in January are signi?cantly higher than returns in any
other month of the year. This phenomenon is called the year-end or
January effect, and it can be traced to the ?rst two weeks in January.
! The January effect is much more accentuated for small ?rms than
for larger ?rms, and roughly half of the small ?rm premium, described
in the prior section, is earned in the ?rst two days of January.
Aswath Damodaran 32
Returns in January
Aswath Damodaran 33
Explanations for the January Effect
! A number of explanations have been advanced for the January effect,
but few hold up to serious scrutiny.
• Tax loss selling by investors at the end of the year on stocks which have 'lost
money' to capture the capital gain, driving prices down, presumably below true
value, in December, and a buying back of the same stocks in January, resulting in
the high returns.Since wash sales rules would prevent an investor from selling and
buying back the same stock within 45 days, there has to be some substitution
among the stocks. Thus investor 1 sells stock A and investor 2 sells stock B, but
when it comes time to buy back the stock, investor 1 buys stock B and investor 2
buys stock A.
• A second rationale is that the January effect is related to institutional trading
behavior around the turn of the years. It has been noted, for instance, that ratio of
buys to sells for institutions drops signi?cantly below average in the days before the
turn of the year and picks to above average in the months that follow.
Aswath Damodaran 34
The Size Effect in January
Aswath Damodaran 35
Institutional Buying/Selling around Year-end
Aswath Damodaran 36
Returns in January vs Other Months - Major Financial
Markets
Aswath Damodaran 37
B. The Weekend Effect
! The weekend effect is another phenomenon that has persisted over
long periods and over a number of international markets. It refers to
the differences in returns between Mondays and other days of the
week.
! Over the years, returns on Mondays have been consistently lower than
returns on other days of the week.
Aswath Damodaran 38
Returns by Weekday
Aswath Damodaran 39
The Weekend Effect in International Markets
Aswath Damodaran 40
Has it held up?
Aswath Damodaran 41
The Weekend Effect: Explanations
! First, the Monday effect is really a weekend effect since the bulk of
the negative returns is manifested in the Friday close to Monday
open returns. The returns from intraday returns on Monday are not
the culprits in creating the negative returns.
! Second, the Monday effect is worse for small stocks than for larger
stocks.
Third, the Monday effect is no worse following three-day weekends
than two-day weekends.
! There are some who have argued that the weekend effect is the result
of bad news being revealed after the close of trading on Friday and
during the weekend. Even if this were a widespread phenomenon, the
return behavior would be inconsistent with a rational market, since
rational investors would build in the expectation of the bad news over
the weekend into the price before the weekend, leading to an
elimination of the weekend effect.
Aswath Damodaran 42
The Holiday Effect: Is there one?
Aswath Damodaran 43
Volume and Price: The Evidence
Aswath Damodaran 44
Foundations of Technical Analysis: What are the
assumptions?
(1) Price is determined solely by the interaction of supply & demand
(2) Supply and demand are governed by numerous factors both
rational and irrational. The market continually and automatically
weighs all these factors. (A random walker would have no qualms
about this assumption either. He would point out that any irrational
factors are just as likely to be one side of the market as on the other.)
(3) Disregarding minor ?uctuations in the market, stock prices tend
to move in trends which persist for an appreciable length of time.
( Random walker would disagree with this statement. For any trend to
persist there has to be some collective 'irrationality')
(4) Changes in trend are caused by shifts in demand and supply.
These shifts no matter why they occur, can be detected sooner or
later in the action of the market itself. (In the ?nancial economist's
view the market (through the price) will instantaneously re?ect any
shifts in the demand and supply.
Aswath Damodaran 45
On why technical analysts think it is futile to estimate
intrinsic values
! "It is futile to assign an intrinsic value to a stock certi?cate. One share
of US Steel , for example, was worth $261 in the early fall of 1929,
but you could buy it for only $22 in June 1932. By March 1937 it was
selling for $126 and just one year later for $38. ... This sort of thing,
this wide deivergence between presumed value and intrinsic value, is
not the exception; it is the rule; it is going on all the time. The fact is
that the real value of US Steel is determined at any give time
solely, de?nitely and inexorably by supply and demand, which are
accurately re?ected in the transactions consummated on the ?oor
of the exchange.” (From Magee on Technical Analysis)
Aswath Damodaran 46
I. Markets overreact: The Contrarian Indicators
Basis: Research in experimental psychology suggests that people tend to
overreact to unexpected and dramatic news events. In revising their
beliefs, individuals tend to overweight recent information and
underweight prior data.
Empirical evidence: If markets overreact then
(1) Extreme movements in stock prices will be followed by subsequent
price movements in the opposite direction.
(2) The more extreme the price adjustment, the greater will be the
subsequent adjustment
Aswath Damodaran 47
Issues in Using Contrarian Indicators
(1) Why, if this is true, is is that contrarian investors are so few in number
or market power that the overreaction to new information is allowed to
continue for so long?
(2) In what sense does this phenomenon justify th accusation that the
market is inef?cient?
(3) Is the market more ef?cient about incorporating some types of
information than others?
Aswath Damodaran 48
Technical trading rules: Contrarian Opinion
1. Odd-lot trading: The odd-lot rule gives us an indication of what the
man on the street thinks about the stock (As he gets more enthusiastic
about a stock this ratio will increase).
2. Mutual Fund Cash positions: Historically, the argument goes, mutual
fund cash positions have been greatest at the bottom of a bear market
and lowest at the peak of a bull market. Hence investing against this
statistic may be pro?table.
3. Investment Advisory opinion: This is the ratio of advisory services that
are bearish. When this ratio reaches the threshold (eg 60%) the
contrarian starts buying.
Aswath Damodaran 49
II. Detecting shifts in Demand & Supply: The Lessons in
Price Patterns
Aswath Damodaran 50
1. Breadth of the market
Measure: This is a measure of the number of stocks in the market which
have advanced relative to those that have declined. The broader the
market, the stronger the demand.
Related measures:
(1) Divergence between different market indices (Dow 30 vs NYSE
composite)
(2) Advance/Decline lines
Aswath Damodaran 51
2. Support and Resistance Lines
A common explanation given by technicians for market movements is that
markets have support and resistance lines. If either is broken, the
market is poised for a major move.
Aswath Damodaran 52
Possible Rationale
(1) Institutional buy/sell programs which can be triggered by
breakthrough of certain well de?ned price levels (eg. Dow 1300)
(2) Self ful?lling prophecies: Money managers use technical analysis for
window dressing.
Aswath Damodaran 53
3. Moving Averages
! A number of indicators are built on looking at moving averages of
stock prices over time. A moving average line smooths out ?uctuations
and enables the chartist to see trends in the stock price. How that trend
is interpreted then depends upon the chartist.
Aswath Damodaran 54
4. Volume Indicators
Some technical analysts believe that there is information about future
price changes in trading volume shifts.
Aswath Damodaran 55
5. Point and Figure Charts
Aswath Damodaran 56
III. Market learn slowly: The Momentum Investors
Basis: The argument here is that markets learn slowly. Thus, investors
who are a little quicker than the market in assimilating and
understanding information will earn excess returns. In addition, if
markets learn slowly, there will be price drifts (i.e., prices will move
up or down over extended periods) and technical analysis can detect
these drifts and take advantage of them.
The Evidence: There is evidence, albeit mild, that prices do drift after
signi?cant news announcements. For instance, following up on price
changes after large earnings surprises provides the following evidence.
Aswath Damodaran 57
Momentum Indicators
! Relative Strength: The relative strength of a stock is the ratio of its
current price to its average over a longer period (eg. six months). The
rule suggests buying stocks which have the highest relative strength
(which will also be the stocks that have gone up the most in that
period).
! Trend Lines: You look past the day-to-day movements in stock prices
at the underlying long-term trends. The simplest measure of trend is a
trend line.
Aswath Damodaran 58
IV. Following the Smart Investors: The Followers
! This approach is the ?ip side of the contrarian approach. Instead of
assuming that investors, on average, are likely to be be wrong, you
assume that they are right.
! To make this assumption more palatable, you do not look at all
investors but only at the smartest investors, who presumably know
more than the rest of us.
Aswath Damodaran 59
Specialist Short Sales
! The assumption is that specialists have more information about future
price movements than other investors. Consequently, when they sell
short, they must know that the stock is overvalued.
! Investors who use this indicator will often sell stocks when specialists
do, and buy when they do.
Aswath Damodaran 60
Insider Buying and Selling
! You can look up stocks where insider buying or selling has increased
the most.
! The ratio of insider buying to selling is often tracked for stocks with
the idea that insiders who are buying must have positive information
about a stock whereas insiders who are selling are likely to have
negative information.
Aswath Damodaran 61
V. Markets are controlled by external forces: The Mystics
The Elliot Wave: Elliot's theory is that the market moves in waves of
various sizes, from those encompassing only individual trades to those
lasting centuries, perhaps longer. "By classifying these waves and
counting the various classi?cations it is possible to determine the
relative positions of the market at all times". "There can be no bull of
bear markets of one, seven or nine waves, for example.
The Dow Theory:" The market is always considered as having three
movements, all going at the same time. The ?rst is the narrow
movement (daily ?uctuations) from day to day. The second is the short
swing (secondary movements) running from two weeks to a month
and the third is the main movement (primary trends) covering at least
four years in its duration.
Aswath Damodaran 62
The Dow Theory
Upward
primary trend
Downward
primary trend
Upward
primary trend
Secondary
movements
Time
Aswath Damodaran 63
The Elliott Wave
Aswath Damodaran 64
Determinants of Success at Technical Analysis
• If you decide to use a charting pattern or technical indicator, you need to be
aware of the investor behavior that gives rise to its success. You can modify or
abandon the indicator if the underlying behavior changes.
• It is important that you back-test your indicator to ensure that it delivers the
returns that are promised. In running these tests, you should pay particular
attention to the volatility in performance over time and how sensitive the
returns are to holding periods.
• The excess returns on many of the strategies that we described in this chapter
seem to depend upon timely trading. In other words, to succeed at some of
these strategies, you may need to monitor prices continuously, looking for the
patterns that would trigger trading.
• Building on the theme of time horizons, success at charting can be very
sensitive to how long you hold an investment.
• The strategies that come from technical indicators are generally short-term
strategies that require frequent and timely trading. Not surprisingly, these
strategies also generate large trading costs that can very quickly eat into any
excess returns you may have.
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