Capital Market Efficiency
The Empirics
4 basic traits of efficiency
•
An efficient market exhibits certain behavioral
traits. We can examine the real market to see if
it conforms to these traits. If it doesn’t, we can
conclude that the market is inefficient.
1.
2.
3.
4.
What if?
Act to new information quickly and accurately
Price movement is unpredictable (memory-less)
No trading strategy consistently beat the market
Investment professionals not that professional
Definitions
Implications
Price
Empirics
Empirical Strategies
• Look at the historical data. See if they
conform with the 4 traits
What if?
Definitions
Implications
Price
Empirics
Stock price ($)
1st trait: reaction to news
Early Reaction
Delayed Reaction
Days relative to announcement day
-t
0
+t
The timing for a positive news
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
Event study
“one type of test of the semi-strong form of market efficiency to see if
prices reflect all publicly available information or not.”
• To test this, event studies examine prices and returns over time
(particularly around the arrival of new information.)
• Test for evidence of [1] under reaction, [2] overreaction, [3] early
reaction, [4] delayed reaction around the event.
• If market is “semi-strong-form efficient”, the effects of an event will
be reflected immediately in security prices. Thus a measure of the
event’s economic impact can be constructed using security prices
observed over a relatively short time period.
• Some examples of events include mergers and acquisitions,
earnings announcements, issues of new debt or equity, and
announcements of macroeconomic variables such as the trade
figures.
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
Procedures of a event study:
• Identify the event of interest. (e.g., Merger
Acquistion)
• Define the event period? (e.g., -10 days and +10
days)
• Select the sample (e.g., firms which have in
common the incidence of the event of interest)
• Measure the impact by defining abnormal return
• Estimate the parameters needed to calculate
expected returns.
• Calculate cumulative abnormal returns
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
• The event period should start before you think the event has an
effect on the stock price. As an example, for merger
announcements, a typical choice is from 25 trading days before the
announcement day to 25 trading days after the announcement day
• The estimation period should be a period right before the event
period. For merger announcements, a typical choice is 100 trading
days before the start of the event period
-125
-25
Estimation Period
What if?
Definitions
Implications
0
+25
Event Period
Price
Empirics
1st trait: reaction to news
• Abnormal return = Actual realized return – Expected return
E.g.,
E(rj|rM,t) = a0 + ajrM,t
(Return on security j conditional on the return on market)
εj,t = rj,t – E((rj|rM,t)
•
Cumulative abnormal return:
CARj,t = ∑-Tt εj,t
•
(Aggregate abnormal returns from –T to t)
Average cumulative abnormal return over a sample of securities:
Average CARt = (∑j
CARj,t)/J
(where J = no. of securities in the sample)
•
Plot the graph, examine the pattern. Of course, perform hypothesis testing
as well.
What if?
Definitions
Implications
Price
Empirics
Cumulative abnormal returns
(%)
1st trait: reaction to news
Cumulative Abnormal Returns for Companies Announcing
Dividend Omissions
1
0.146 0.108
-8
-6
0.032
-4
-0.72
0
-0.244
-2 -0.483 0
-1
2
4
6
8
Efficient market
response to “bad news”
-2
-3
-3.619
-4
-5
-4.563-4.747-4.685-4.49
-4.898
-5.015
-5.183
-5.411
-6
Days relative to announcement of dividend omission
Source: Szewczyk, Tsetsekos and Santout (1997)
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
40
35
Cumulative
abnormal
return %
30
25
20
15
10
5
0
-29
0
30
Month relative to split
How stock splits affect value?
Source: Fama, Fisher, Jensen & Roll (1969)
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
Cumulative Abnormal Return (%)
Announcement Date
39
34
29
24
19
14
9
4
-1
-6
-11
-16
Days Relative to annoncement date
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
Average Cumulative abnormal return
Announcement Date for quarterly earnings reports
Days relative to Announcement Date
Source: Remdleman, Jones and Latane (1982)
What if?
Definitions
Implications
Price
Empirics
1st trait: reaction to news
• Event study methodology has been applied to a
large number of events including:
–
–
–
–
–
Dividend increases and decreases
Earnings announcements
Mergers
Capital Spending
New Issues of Stock
• The studies generally support the view that the
market is semi-strong from efficient.
• In fact, the studies suggest that markets may
even have some foresight into the future—in
other words, news tends to leak out in advance
of public announcements.
What if?
Definitions
Implications
Price
Empirics
2nd trait: Random price movements
• Studies of serial correlation
• Studies of seasonality
– Day of the week effect
– January effect
What if?
Definitions
Implications
Price
Empirics
2nd trait: Random price movements
Studies of serial correlation
H0: Cov(ΔPt, ΔPt-i) is significantly different from zero or not, for i ≠ 0
Alternatively,
H0: Cov(Δrt, Δrt-i) is significantly different from zero or not, for i ≠ 0
• Plot the following types of graph.
• Note: Statistically significant ≠ Economically significant
– If you are aware of the correlation, and attempt to trade on the
basis of it, brokerage commissions may make your expected
profits negative.
What if?
Definitions
Implications
Price
Empirics
Return on day t+1 (in %)
2nd trait: Random price movements
Return on day t (in %)
What if?
Definitions
Implications
Price
Empirics
2nd trait: Random price movements
Return on week t+1 (in %)
FTSE 100 (correlation = -0.08) Nikkei 500 (correlation = -0.06)
DAX 30 (Correlation = -0.03)
S & P Composite (correlation = -0.07)
Return on week t (in %)
What if?
Definitions
Implications
Price
Empirics
2nd trait: Random price movements
Studies of seasonality
– Day of the week effect
– French (1980) and Gibbons & Hess (1981)
– Using S&P 500 index to proxy returns of stocks for
each of the 5 trading days of the week.
– Found Monday returns are on average lower than
returns on other days.
– If transaction costs are taken into account, however,
trading rule based on this pattern fails to generate
abnormal returns consistently.
– But you may consider this effect in timing your own
purchases and sales.
What if?
Definitions
Implications
Price
Empirics
2nd trait: Random price movements
Studies of seasonality
–
–
–
–
The January effect
Keim (1983) and Roll (1983)
The most mystifying seasonal effect.
Stock returns, especially returns on small stocks, are
on average higher in January than in other months.
– Moreover, much of the higher January return on small
stocks comes on the last trading day in December
and the first 5 trading days in January.
What if?
Definitions
Implications
Price
Empirics
3rd trait: Superior trading strategy
• Caveat - Be careful here!!! It’s in the
interest of those who find such rules to
hide them rather than publicize them.
• Price-to-earning ratio. (P/E Ratios)
• Size effect
What if?
Definitions
Implications
Price
Empirics
3rd trait: Superior trading strategy
• Price-to-earning ratio. (P/E Ratios)
• The trading rule of “buying stocks that
have low price-to-earning ratios , and
avoiding stocks with high price-to-earning
ratios” seems to consistently outperform
the market.
• Question:
– 1) what does it mean by low P/E ratio?
– 2) Survivorship bias?
What if?
Definitions
Implications
Price
Empirics
3rd trait: Superior trading strategy
• Size Effect. (Banz (1981))
• Small firms tend to have higher returns as
compared to larger firms.
• The trading rule of “buying stocks of smaller
firms” seems to consistently outperform the
market.
• Question:
– 1) Is there any inherent risks of small firms not
captured by risk measures?
– 2) Is it because transaction cost of smaller firms’
stocks are more expensive (due to thinner market)?
What if?
Definitions
Implications
Price
Empirics
4th trait: professional investors?
• If the market is semi-strong form efficient, then no matter
what publicly available information mutual-fund
managers rely on to pick stocks, their average returns
should be the same as those of the average investor in
the market as a whole.
• We can test efficiency by comparing the performance of
professionally managed mutual funds with the
performance of a market index.
Evaluating mutual funds performance. (Jensen (1969))
• Managers of mutual funds are usually highly trained and
have access to broad sources of investment information.
• Thus, if their managed mutual funds consistently
outperform the market, then we conclude that such
evidence is against the market efficiency hypothesis
What if?
Definitions
Implications
Price
Empirics
4th trait: professional investors?
• Using S & P 500 as proxy for the market,
estimate the security market line.
• Estimate the beta for each mutual funds.
• Plot the mutual funds on the security
market line graph (NOTE: net of all
expenses)
What if?
Definitions
Implications
Price
Empirics
4th trait: professional investors?
What if?
Definitions
Implications
Price
Empirics
4th trait: professional investors?
Average Annual Return on 1493 Mutual Funds and the
Market Index
40
30
Return (%)
20
10
0
-10
Funds
Market
-20
-30
What if?
Definitions
Implications
19
92
19
77
19
62
-40
Price
Empirics