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FINS 2624 ASSIGNMENT 2
EXECUTIVE SUMMARY
To sum up, based on the discussion above, it seems that for the US market, it appeared to
be working under a weak-form of inefficient market considering market anomalies of
momentum effects and pricing-earning ratio and intrinsic measure anomalies. However,
for Australia market, due to the characteristics difference of Australia market, the portfolio
of Australian shares are consistent with the weak-form of market efficiency, however, it
does not apply to the individual stocks. Besides, no intrinsic measure anomalies were
found for the long horizon. Overall, Australia market is more efficiency compared to US
market in terms of weak-from of market efficiency.
In addition, after examining the small firm in January effect anomaly, book to market ratio
anomaly and post earnings announcement price drift anomaly, we could see that Australian
market especially for small firms was influenced by January effect, book to market ratio
and post earnings announcement anomaly, which indicate market inefficiency of Australia
market. On the other hand, for the US market, the last three anomalies all applied to US
market also, and it seems that US market is also not market efficiency.
PART 1
An efficient market exists when the stock price fully reflects all the information available .
There are several ways to test whether the market is efficient. The main tests include event
study, the patterns in stock return, predictors of broad market return, test of anomalies and
the inside information . These tests are conducted by using risk models to calculate the
excess return of the market , the market is said to be efficient if there is no excess return to
be found .
Event study exams the influence on stock price by a specific event and a single-index
model is used :
rt = a + brmt + et => et = rt *ƒ²*ƒ"€š( a + brmt )
rt : stock return during the period t
a : average rate of return during period t with no market return
brmt : sensitivity to market return
et : part of stock return due to firm-specific event
And we call et abnormal return.
There are also other tests, such as patterns in stock return and predictors of broad market
return which are early tests for the market efficiency and are included in the weak form
tests .
The first test is conducted by exam the past stock prices to find the trend . It will need to
calculate the serial correlation of stock market return . If it is positive serial correlation,
then it means what followed by the positive returns is a positive return, otherwise, it is a
negative return . There are problems associated with this test. For the short horizons, a
research conducted by Conrad and Kaul and Lo and Mackinlay found that the correlation
coefficient is quite small for the weekly return which suggested that there is no strong
evidence of the existence of patterns in stock returns over short horizons . However, for the
long run, researchers do find that it tends to have a negative correlation coefficient .
The second test is based on the variables such as dividend/price ratio, earnings yield, and
spread between yields on corporate bonds, and using these variables to predict market
returns . If we can say that market returns are predicted by predicting risk- adjusted
abnormal returns then probably the market is inefficient . However, the difficulty here is
uncertainty about whether the return we get is result from the predictability by using the
indicator in risk premium or risk- adjusted abnormal returns .
Test of anomalies are included in semi strong tests . These tests are normally conducted by
applying the CAPM model that is used to adjust the risk . That is, the risk adjusted returns
are gained by testing both risk adjustment and market efficiency hypothesis .
There are several examples of anomalies. The small firm in January Effect is about the
effect of size . It found that smallest sized firms tend to have a higher return despite the
risk are already been adjusted by using CAPM, and it most occurred in January .
The neglected firm Effect is actually another way to explain the above example of
anomalies, as established by Arbel and Strebel that due to less information available
related to small firms so it makes small firms more risky . Besides the liquidity effects is
another reason to explain the abnormal return of the small firms . When the stocks are ness
liquid it may incur a high cost for transactions, and for small firms it is likely that their
stocks will not be as liquid as big firms .
The other examples include Book to Market Ratio. Researches by Fama and French and
Reinganum found that there is a relationship between book to market ratio and returns so it
is possible for investors to look at the book to market ratio to predict the return and own
abnormal return . The finding makes a big doubt as to the market efficiency .
The last one is Post Earnings Announcement Price Drift. For an efficiency market the price
of stock should reflect the information immediately after the release, however, what in
reality is that price continued to rise or fall through a period after the announcement date
and so was the abnormal returns . It is called Post Earning Announcement Drift.
The last test is inside information. In a case that insiders make big return because of
knowing the inside information, the market is said to be inefficient . As the price of stock
is not fully reflect all the available information.
Besides, for all the tests above, there is one common problem related to the tests. That is,
as the test conducted by using risk models, it may then need to take into account of the
efficiency of the models used .
PART 2
A market anomaly exists where findings cannot be reconciled with the efficient market
hypothesis or when returns when risk adjusted are still abnormal. Prior to examining the
anomalies in the US and Australian markets, it is necessary to examine the different forms
of market efficiency, rendering the violation of specific anomalies to the level of market
efficiency. The weak form of market efficiency maintains that all current prices reflect all
past information, including information derived from technical analysis, history of prices,
trading volumes or short term interest rates. The semi-strong form hypothesis maintains
that all publicly available information should already be impounded into the stock price;
this would obviously include everything that is known about the firm, eg, product line, and
earning forecasts made should reflect in current prices. Strong form efficient hypothesis
asserts that all information, including insider information should be impounded into stock
prices. Thus if markets are efficient, then only new information should affect the future
movement of prices, and since new information is unpredictable , prices should follow a
random walk - that is, they are equally likely to move up or down.
There are five prominent market anomalies highlighted in past research for US markets.
1. Momentum and reversal Effects anomaly also known as the winner-loser anomaly
Momentum effects are observed, where in the short horizon of typically less than three
months, stock that perform well continue to perform well, while stocks performing poorly
continuing to perform poorly. Thus in the short horizon, stock prices exhibit a positive
serial correlation. Serial correlation refers to a tendency for future performance to be
related to the past. Here the price of stock can be (weakly) predicted based on the last
twelve months of past performance. Conrad and Kaul (1988) and Lo and MacKinlay
(1988) observed positive serial correlations for the short horizon. They observed weekly
stock returns on the NYSE , and found the correlations for small stock to be medium,
while correlation for large stocks were small. Overall, while a correlation exists, the
authors found no definite presence of trading opportunity as the correlation was relatively
small. On the other hand, Jegadeesh and Titman (1993) found that instead of looking at
individual stocks, portfolios of the best performing stocks tend to reliably outperform other
stocks on an intermediate horizon of three to twelve months. Many studies have
investigated how individual prices are slow to reaction, possibly explain the weak
correlation for individual stocks, however aggregate information have a quick, and often
overreaction on stock prices, explaining why the momentum effect would be stronger for
portfolios of stocks rather than individual stocks. The positive momentum for these
portfolios was significant so much so that they offered a tangible opportunity for profit
using this strategy. Jagedeesh and Titman (2001) substantiated their early finding in 1993
of the momentum effect providing a favourable investment strategy. Jegadeesh et al.
(2004) observe that analyst often inherently favour high momentum stocks and make
recommendations on that basis. In fact, Burch and Swaminathan (2001) point out a high
proportion of up to 50% of actively managed funds utilize momentum investing. Bird et al.
(2005) point out that investors make no reference to valuation for their decision in the
above regards, that is that investors are irrational, and hence play no importance to correct
