Capital Market Efficiency The Empirics. 4 basic traits of efficiency An efficient market exhibits certain behavioral traits. We can examine the real capital

Capital Market EfficiencyThe Empirics

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.

1. Act to new information quickly and accurately

2. Price movement is unpredictable (memory-less)

3. No trading strategy consistently beat the market

4. Investment professionals not that professional

What if? Definitions Implications Price Empirics

Empirical Strategies

• Look at the historical data. See if they conform with the 4 traits


1st trait: reaction to news


00 +t+t-t-t

The announcement of a positive news

Days relative to announcement day

Sto

ck p

rice

($)

Early Reaction

Delayed Reaction

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.


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


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


0 +25-25

Event PeriodEstimation Period

-125


• 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.




Cumulative Abnormal Returns for Companies Announcing Dividend Omissions

0.146 0.108

-0.72

0.032-0.244-0.483

-3.619

-5.015-5.411-5.183

-4.898-4.563-4.747-4.685-4.49

-6

-5

-4

-3

-2

-1

0

1

-8 -6 -4 -2 0 2 4 6 8

Days relative to announcement of dividend omission

Cum

ulat

ive

abno

rmal

ret

urns

(%

)

Efficient market response to “bad news”

Source: Szewczyk, Tsetsekos and Santout (1997)



0

5

10

15

20

25

30

35

40

Month relative to split

Cumulative abnormal return %

-29 0 30

How stock splits affect value?Source: Fama, Fisher, Jensen & Roll (1969)



-16

-11

-6

-1

4

9

14

19

24

29

34

39

Days Relative to annoncement date

Cu

mu

lati

ve

Ab

no

rma

l Re

turn

(%

)

Announcement Date



Announcement Date for quarterly earnings reports

Days relative to Announcement Date

Ave

rage

Cum

ulat

ive

abno

rmal

ret

urn

Source: Remdleman, Jones and Latane (1982)


• 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?)


2nd trait: Random price movements

• Studies of serial correlation

• Studies of seasonality– Day of the week effect– January effect



Studies of serial correlationNULL 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.




Return on day t (in %)

Ret

urn

on d

ay t+

1 (i

n %

)



Ret

urn

on w

eek

t+1

(in

%)

Return on week t (in %)

FTSE 100 (correlation = -0.08) Nikkei 500 (correlation = -0.06)

DAX 30 (Correlation = -0.03) S & P Composite (correlation = -0.07)

http://www.ftse.com/indices_marketdata/uk_series/index_home.jsp#ftse100

http://www.nni.nikkei.co.jp/CF/FR/MKJ/list_nikkei_constituents.cfm?INDEX=N500

http://kurse.exchange.de/exchange/de/kurseumsaetze.htm

http://www2.standardandpoors.com/servlet/Satellite?pagename=sp/Page/IndicesIndexGroupPg&xcd=US&r=3&l=EN&b=4&s=6&ig=51&f=1


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.



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.


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



• 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?



• 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?


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



• 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!!!)






-40

-30

-20

-10

0

10

20

30

40

1962

1977

1992

Re

turn

(%

)

Funds

Market

Average Annual Return on 1493 Mutual Funds and the Market Index

Documents

Capital Market Efficiency The Empirics. 4 basic traits of efficiency An efficient market exhibits certain behavioral traits. We can examine the real capital