Unformatted text preview: 14/04/2009 Empirical findings
Empirical findings Market
Market Efficiency Hypothesis
– Weak form
– Semi-strong form
Semi– Strong form Research
Research findings By Quach Manh Hao – More applicable to developed financial markets
– Implying the role of security analysis in developing
The lecture today presents the empirical findings as
evidence of anomalies to the MEH and the risk
models studied in the previous lectures. Filter rule
In the absence of no new information, a stock price
trades within certain, established bounds.
In other words, we should expect no “special
movement” in the stock price.
– On new news, if stock rises above the established ceiling,
– On new news, if stock falls below the established floor, sell
This works because the stock is in process of
establishing new a trading range.
This filter rule is used today by many investment
professionals. In consideration: what
Remember: if the Weak form version of the EMH is
correct, one should not be able to earn excess
return on the basis of known, observable
information on a firm
But, there are a number of such factors that have
received a great deal of attention over the years.
These are the empirical findings and they may be
changed from time to time depending on data
But they do serve as a practical guide to the
For academic purpose, you can also think of them
as the tests of the hypotheses that we have
studied. Firm characteristics
The CAPM says that only market risk was
relevant in explaining the cross-section of
Evidence began to emerge that other, firmfirmspecific information seemed to be able to
enhance the explanation.
This means the firm specific information is
indeed important to explain the stock
return/price. The size effect
Banz (1981) was the first to identify an apparent
anomalous relationship between firm size and
Excess returns between 1936 and 1977 on US stock
market could be earned by investing in small stocks
An additional 19.8% per year!
Banz found that small cap betas were lower on
average than large cap betas
Implying that higher return had been achieved by
incurring lower (CAPM) risk
This study calls into question both the EMH and the
CAPM 1 14/04/2009 The size effect explanations
CAPM Betas biased downwards for small stocks because of
infrequent trading (Roll (1981) Journal of Finance) i.e. small
stocks seem less liquid.
Perhaps CAPM is an inappropriate model, do the results hold
for APT risk-adjustment?
In their research, Chen, Chan and Hsieh (1985) found that
APT could explain most of the difference between large and
In the CCH results, size seemed to be proxying for additional
default risk not captured by CAPM i.e. not explained by the
Liquidity and transactions costs, not considered explicitly by
the CAPM, may also play a part in the higher return of small
stocks A practical guide
Complicated portfolio formation process often used
to disentangle the effects
You should try this for forming your own portfolio:
– Identify high and low PE stocks. Assign ranking e.g. 1 to
– Identify size (market capitalization). Assign second ranking
based upon size e.g. from 1 to 3. Form
Form portfolios based upon PE and size:
Case 1: Sort stocks by PE and form portfolios based
upon PE – see what you get?
– Expected: Low PE outperforms. Market to Book Effect
Fama and French, and Chan, Hamao and
Lakonishok found that high book to market
value firms outperform low book to market
High book to market value firms = Value
Low book to market value firms = Growth
Using US stock return data, the premium
(i.e. the excess return) found to be around
7.8% per year. PE Effect
Basu (1977) found relationship between expected returns and
High PE stocks subsequently underperform low PE stocks
Or “Value outperforms Growth”
Value stocks: low PE, low PB and high dividend.
Growth stocks: high PE, high PB and low dividend.
Investors get overexcited about growth prospects and then
subsequently revise down expectations
Other researchers found that once one controls for “Size” the
In short, “value stocks” are better but the “size effect” is
important. A practical guide
Case 2: Sort stocks by Size and form portfolios
based upon PE – see what you get?
– Expected: Small stock outperforms Case
Case 3: Sort by PE and then by Size. Compare large
low PE portfolio returns with large high PE portfolio
– Expected: The findings are mixed but we would expect a
smallsmall-low PE to outperform. But largeBut if large-low PE portfolio outperforms large-high
largePE portfolio then possibly due to PE effect i.e. you
might find it useful to focus on PE analysis. Firm characteristics and
Another consequence of anomalies is “Style
Fund manager picks a style:
– Recovery: stocks that have experienced a
downward movement. The notion is simple that
“things that have gone down must go up”. Then
Then manages the portfolio according to
this style 2 14/04/2009 Seasonality
Seasonal patterns in stock market
returns have been identified by
Patterns of this kind are the most
obvious challenge to the weak form
version of the EMH.
The ‘January Effect’ is probably the
most famous of these anomalies UK Evidence The January Effect
“Sell in May and go away”
Informally recognised by participants that
January was typically a good month for
Researchers confirmed this in 1970s and
The January effect seemed to be prevalent
– different markets
– and for both bonds and equities Empirical evidence
Gultekin (1983) find evidence of January
effect in 17 different stock markets
Further tests showed that the January Effect
was concentrated in first few days of
In the UK Clare et al (1994) find evidence
for an “April effect” too (see previous slide)
But surely seasonal patterns like those
documented in many papers would be
traded away in an efficient market ? January and the size
“It’s the size effect in disguise”
A number of researchers have found that it is small
firms in particular that display the January effect.
It means that the January effect is “no problem”
with large stocks.
The implication is simple: if you are holding small
stocks, when would be the best time to sell them?
The answer is “January”
I write the January in brackets to implicitly mean
that it should be considered better as a name. January and tax effects
“It’s something to do with tax”
In US and in many countries the tax year ends in
In order to minimise their tax bill, investors realise
losses on losing investments, i.e. selling poorly
performing stocks in December.
‘Losers’ with depressed prices in December are then
snapped up in January leading to an increase in
Known as the “tax loss selling hypothesis” 3 14/04/2009 January and tax effects cont
If tax loss selling is the cause then we might expect
to find other features in returns:
We can observe lower than average returns in
December – particularly end of December. This
explains the downward movement of the market at
the end of December.
We can expect high proportion of small ‘loser’
stocks at the end of the year.
The seasonal pattern is changed where the tax
regime is different:
– no capital gains tax
– different fiscal year end January and tax effects cont
In the UK there is an April effect (see Clare et al
It is because the UK’s tax year ends on April 5th
But why does the UK (Australia, Japan) have a
January effect then?
Also, in US there is a January effect evident before
the introduction of income tax
So taxation may only be a partial explanation, at
There must be something else to explain this effect
… but we haven’t found! Other seasonalities
Returns are higher on Fridays than on Mondays
Similar effect over bank holidays?
Bad company news released over the weekend?
The “turn-of-theThe “turn-of-the-month” effect (Ariel 198X): see
the new finding at
20- January and tax effects cont
What should we do then?
Buy security that reaches its annual
low in last week of December
Hold until end of January
Researchers have found that this
strategy produced significant excess
returns January and window
In some markets (US) there will be the
added benefit of a lower tax bill - explaining
why losers are sold at end of the year
Once the portfolio is dressed and bonuses
have been banked the fund managers can
buy the cheap looking losers in early
Explaining why losers become winners.
Please note, this is also why the notion
“things have gone down must go up” to be
true. The overreaction
Debondt and Thaler (1985) find evidence to
suggest that losing stocks over a 3-5 year period
3subsequently turn out to be winners in the next 3-5
Also find that the response is asymmetric and
prominent in January
The reason: investors overreact to bad news and
underreact to good news – a psychological
Clare and Thomas (1994) find similar effect in the
UK, but attribute it to a manifestation of the firmfirmsize effect (yet again) 4 14/04/2009 Conclusions
This lecture does not give you any
theory but the empirical findings
This is a good practical guide to your
The findings may be different due to
data mining, and many other factors.
You can apply but don’t blame me for
your losses!!! Big lessons
Filter rule: there are always bounds for a stock price. Sell
when it tends to go down and buy when it goes up.
Size effect: Small outperforms big.
Value outperforms growth. Specifically:
– Low PE > High PE
– Low MB > High MB
– High Div > Low Div “January”
“January” effect: buy in December and sell in January or “Buy
late sell early”.
The seasonal effect is found with: Weekends, Turn of the
You may expect the tax year end is an important turn”.
Don’t tell others this whole story as you may make a fortune. Thank you!
Unfortunately, all of us know and it’s
But hope is still alive…market is not
efficient. 5 ...
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This note was uploaded on 01/16/2012 for the course ECON 101 taught by Professor Tom during the Spring '11 term at FH Joanneum.
- Spring '11