1. Capital Market Efficiency The Empirics

2. 4 basic traits of efficiency
An efficient market exhibits certain behavioral traits. We can examine the real capital market to see if it conforms with these traits. If it doesn’t, we can conclude that the market is inefficient.
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
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3. Empirical Strategies
Look at the historical data. See if they conform with the 4 traits
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4. 1st trait: reaction to news
Early Reaction
Stock price ($)
Delayed Reaction
Days relative to announcement day -t +t
The announcement of a positive news
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5. 1st trait: reaction to news
Event study “One type of test of the semi-strong form efficient market hypothesis to see if prices reflect all publicly available information or not.”
Event studies examine prices and returns over time (particularly around the arrival of new information.)
Test for evidence of [1] under-reaction, [2] over-reaction, [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,
stock splits,
announcements of macroeconomic variables such as the trade figures.
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6. 1st trait: reaction to news
The mechanics of an event study:
First of all, select a sample of stockS.
[1] Identify the event of interest. (e.g., stock splits)
[2] Define the event period? (e.g., -10 to +10 days relative to the event date)
[3] Select the sample (e.g., firms which have in common the incidence of the event of interest)
[4] Measure the impact by means of abnormal return
[5] Estimate the parameters needed to calculate expected returns.
[6] Calculate cumulative abnormal returns
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7. 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
+25
Estimation Period
Event Period
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8. 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.
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9. 1st trait: reaction to news
Efficient market response to “bad news”
Source: Szewczyk, Tsetsekos and Santout (1997)
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10. 1st trait: reaction to news
-29 30
How stock splits affect value? Source: Fama, Fisher, Jensen & Roll (1969)
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11. 1st trait: reaction to news
Announcement Date
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12. 1st trait: reaction to news
Announcement Date for quarterly earnings reports
Average Cumulative abnormal return
Days relative to Announcement Date
Source: Remdleman, Jones and Latane (1982)
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13. 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 does that imply?)
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14. 2nd trait: Random price movements
Studies of serial correlation
Studies of seasonality
Day of the week effect
January effect
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15. 2nd trait: Random price movements
Studies of serial correlation
NULL HYPOTHESIS: H0: Cov(ΔPt, ΔPt-i) is significantly different from zero or not, for i ≠ 0
Or alternatively, the following null hypothesis:
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.
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16. 2nd trait: Random price movements
Return on day t+1 (in %)
Return on day t (in %)
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17. 2nd trait: Random price movements
FTSE 100 (correlation = -0.08)
Nikkei 500 (correlation = -0.06)
Return on week t+1 (in %)
DAX 30 (Correlation = -0.03)
S & P Composite (correlation = -0.07)
Return on week t (in %)
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18. 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.
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19. 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.
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20. 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
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21. 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?
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22. 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)?
3) What is small? What is large? Where is the cut-off?
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23. 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
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24. 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!!!)
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25. 4th trait: professional investors?
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26. 4th trait: professional investors?
Average Annual Return on 1493 Mutual Funds and the Market Index
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