Efficient Market Hypothesis
Efficient Market Hypothesis claims that the stock market prices reflect all the information available without any lag or time gap and does not allow any investor to benefit from abnormal gains. It is impossible to test the Efficient Market Hypothesis in its absolute form therefore it has been divided into three forms: –
- Strong Form – prices reflect all information, both private and public
- Semi Strong Form (Event studies, Fama 1991) – prices reflect all publicly available information
- Weak Form – prices reflect all past price movements
These differences raise the question of whether the prices reflect the true and trustworthy information or rumors that are misleading. In other words, any announcement either positive or negative garners an overreaction/under reaction from the investors only to follow the correction of these prices eventually. Markets are not able to distinguish between true and false information even if they react efficiently to the information. Yet it is possible to have prices to react efficiently even when the market reaction may not be efficient.
EMH rests on the following basis –
- All investors are rational investors and will react quickly to the new information given.
- Investors use this information to analyze the market and individual securities to make sound decisions
- No abnormal gains for the traders.
Random Walk
The EMH is connected to the random walk (espoused by Louis Bachelier, a French mathematician in 1900) theory explaining that the stock prices follow a random walk since the information is absorbed quickly into the prices. The more efficient the market is the more random is the successive change in the prices. However, it is not necessary that for prices to follow a random walk, the markets have to be efficient.
Random walk is explained by the fact that the stock price changes are independent of each other (Brealey et al, 2005) and the market is not following any trend or defined pattern to arrive at these stock prices. Additionally, the presence of random walk attracts foreign investment and encourages domestic savings (Arusha & Guneratne, 2007). The assumptions of the random walk model include randomness, no auto correlation, no leverage effect and no volatility clustering.
The Difference
EMH explains the stock prices as reflection of all the information of said security price. Random walk Theory is underpinned by EMH and can exist with or without efficient market hypothesis as a model.
Stock prices are the result of instantaneous equilibrium of supply and demand. However, it is not possible for the supply and demand to be exactly met with equally since the investors are not trading at every hour of the market hours nor can the number of suppliers and buyers be exact on a daily basis hence, there will be an imbalance of supply and demand causing a random movement of the stock prices throughout the day. Furthermore, even if in an inefficient market the investors do have an opportunity to derive abnormal gains from the researched information, they cannot predict how the prices are going to move throughout the day or the investing period, because their actions no matter how calculated will be offset by investors who are trading without keeping up with every bit of information causing the prices to move haphazardly.
The Bubble Effect
A bubble effect is basically subjected to short term distortions within the long-term trends therefore, they quickly self-correct. Markets are bound to go through these bubbles, like the real estate bubble and the dot.com bubbles that appeared and disappeared even more promptly.
For example – EMH fails to completely present reasons behind the market bubble in the second half of 1990’s. If the information is always reflected in the stock prices, then how did the companies not find that information to rule out the ambiguous bubbles that keep recurring every now and then.
In most of these cases, the asset prices have risen aggressively despite the paradoxical growth of those said companies. In all such cases, the analysts and the informed players with an eye for detail should have found said anomaly, yet we have seen that many conglomerates have been burnt by the brunt of these bubbles, they too were unable to catch these in time.
Another significant bubble was the commodities bubble where the price of oil barrel dropped from $147 in 2008 summer to $40 per barrel around 6 months later, this questions the efficiency of the stock market.
The January Effect
The phenomenon remains mainly a small-cap one, impervious to the 1986 Tax Reform Act. The incongruous seasonal buying and selling is consistent at the turn of the year and can be advocated to the behavioral explanations of the January Effect. Multiple institutions continue to adopt January-December as the reporting period despite the reformed tax period i.e. from November –October in order to prepare appealing reports for the investors or the information seekers, these reasons may be contributing to the January Effect after the 1980’s. Furthermore, certain investors tend to derive tax benefits before the end of the year by shorting the securities only to repurchase them in January when the right price strikes following the announcements and dividends or capital gains that add to the volatility of the stock prices during the month of January.
In this case, the prices move randomly even during the month of January despite the expected phenomenon that can be substituted for a pattern pointing towards the inefficiency of the market and its inability to remain independent of trends affecting prices. Such a phenomenon allows the investor to reap better returns despite the market claiming to be void of any arbitrage opportunities.
- Long Term Memory in Volatility
Long term memory in volatility of stock prices has been given enough attention in many national stock markets which points to dependence between distant observations by providing evidence against the random walk theory that encapsulates nonlinear dependence on the second point of distribution thereby marking a predictable component in the series dynamics.
According to a study conducted by Kim Hiang low (Department of real estate, NUS 2008), with his team on the long-term memory in volatility of the global securitized markets in real estate, the presence of this phenomenon implies the existence of autocorrelation. Therefore, providing evidence of certain predictability in the volatile behavior and is congruent with the weak form of efficient market hypothesis (no abnormal profits when analyzing through past performances).
The outcome of the study explained the presence of long-term memory volatility in Asian securitized real estate market explaining the presence of long range dependence albeit not spurious but may provide enough evidence for long term predictability in volatile indices identified. This allows investors to study the correlation in long term securitized real estate assets and eventually deriving abnormal profits through analyzed patterns in the public real estate market, which goes against the random walk theory in the efficient markets.
Short Run and Long Run Serial Correlation and Mean Reversion
It was suggested by Lo and MacKinley (1999) that short-run serial correlations of stock prices are not zero and can gain momentum as many traders tend to ‘jump on the bandwagon’ as they see same direction price movement in consecutive periods with a specific stock. The dot-com boom can be aligned with the same theory according to Shiller (2000)
Although in the long run we see evidence of negative autocorrelation and has been called ‘mean reversion’. Its existence is controversial even though Fama and French (1988) found evidence of it.
Data Mining is another concept that allows the technical analyst to manipulate the data to fit the findings or support the marginal profitable opportunities.
Conclusion
We can conceive financial markets as following a random walk model without being efficient from the former mentioned theories we can gauge that there can exist an inefficient market. However, most of these theories do not allow for major abnormal gains and are so marginal that they can go unnoticed without affecting the stock prices considerably. Random walk Hypothesis does not provide a precise description of the stock prices behavior and in the real world there are predictable opportunities, but they soon vanish without giving way to much profitability.
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