Efficient market hypothesis Totally Explained

Everything about Efficient Market Hypothesis totally explained

In finance, the efficient market hypothesis (EMH) asserts that financial markets are "informationally efficient", or that prices on traded assets, for example, stocks, bonds, or property, already reflect all known information. The efficient market hypothesis states that it's impossible to consistently outperform the market by using any information that the market already knows, except through luck. Information or news in the EMH is defined as anything that may affect prices that's unknowable in the present and thus appears randomly in the future.
The EMH was developed by Professor Eugene Fama at the University of Chicago Graduate School of Business as an academic concept of study through his published Ph.D. thesis in the early 1960s at the same school.

Historical background

The efficient market hypothesis was first expressed by Louis Bachelier, a French mathematician, in his 1900 dissertation, "The Theory of Speculation". His work was largely ignored until the 1950s; however beginning in the 30s scattered, independent work corroborated his thesis. A small number of studies indicated that US stock prices and related financial series followed a random walk model. Also, work by Alfred Cowles in the ’30s and ’40s showed that professional investors were in general unable to outperform the market.
The efficient market hypothesis emerged as a prominent theoretic position in the mid-1960s. Paul Samuelson had begun to circulate Bachelier's work among economists. In 1964 Bachelier's dissertation along with the empirical studies mentioned above were published in an anthology edited by Paul Coonter . In 1965 Eugene Fama published his dissertation arguing for the random walk hypothesis and Samuelson published a proof for a version of the efficient market hypothesis. In 1970 Fama published a review of both the theory and the evidence for the hypothesis. The paper extended and refined the theory, included the definitions for three forms of market efficiency: weak, semi-strong and strong (see below).

Theoretical background

Beyond the normal utility maximizing agents, the efficient market hypothesis requires that agents have rational expectations; that on average the population is correct (even if no one person is) and whenever new relevant information appears, the agents update their expectations appropriately.
Note that it isn't required that the agents be rational (which is different from rational expectations; rational agents act coldly and achieve what they set out to do). EMH allows that when faced with new information, some investors may overreact and some may underreact. All that's required by the EMH is that investors' reactions be random and follow a normal distribution pattern so that the net effect on market prices can't be reliably exploited to make an abnormal profit, especially when considering transaction costs (including commissions and spreads). Thus, any one person can be wrong about the market — indeed, everyone can be — but the market as a whole is always right.
There are three common forms in which the efficient market hypothesis is commonly stated — weak form efficiency, semi-strong form efficiency and strong form efficiency, each of which have different implications for how markets work.

Weak-form efficiency

Excess returns can't be earned by using investment strategies based on historical share prices.
Technical analysis techniques won't be able to consistently produce excess returns, though some forms of fundamental analysis may still provide excess returns.
Share prices exhibit no serial dependencies, meaning that there are no "patterns" to asset prices. This implies that future price movements are determined entirely by unexpected information and therefore are random.

Semi-strong form efficiency

Semi-strong form efficiency implies that share prices adjust to publicly available new information very rapidly and in an unbiased fashion, such that no excess returns can be earned by trading on that information.

Semi-strong form efficiency implies that neither fundamental analysis nor technical analysis techniques will be able to reliably produce excess returns.

To test for semi-strong form efficiency, the adjustments to previously unknown news must be of a reasonable size and must be instantaneous. To test for this, consistent upward or downward adjustments after the initial change must be looked for. If there are any such adjustments it would suggest that investors had interpreted the information in a biased fashion and hence in an inefficient manner.

Strong-form efficiency

Share prices reflect all information, public and private, and no one can earn excess returns.

If there are legal barriers to private information becoming public, as with insider trading laws, strong-form efficiency is impossible, except in the case where the laws are universally ignored.

To test for strong form efficiency, a market needs to exist where investors can't consistently earn excess returns over a long period of time. Even if some money managers are consistently observed to beat the market, no refutation even of strong-form efficiency follows: with hundreds of thousands of fund managers worldwide, even a normal distribution of returns (as efficiency predicts) should be expected to produce a few dozen "star" performers.

Arguments concerning the validity of the hypothesis

[[Image:Price-EarningsRatios as a Predictor of Twenty-Year Returns (Shiller Data).png|400px|right|thumb|Price-Earnings ratios as a predictor of twenty-year returns based upon the plot by Robert Shiller (Figure 10.1, [http://irrationalexuberance.com/shiller_downloads/ie_data.xls source]). The horizontal axis shows the as computed in Irrational Exuberance (inflation adjusted price divided by the prior ten-year mean of inflation-adjusted earnings). The vertical axis shows the geometric average real annual return on investing in the S&P Composite Stock Price Index, reinvesting dividends, and selling twenty years later. Data from different twenty year periods is color-coded as shown in the key. See also . Shiller states that "confirms that long-term investors—investors who commit their money to an investment for ten full years—did do well when prices were low relative to earnings at the beginning of the ten years. Long-term investors would be well advised, individually, to lower their exposure to the stock market when it's high, as it has been recently, and get into the market when it's low."
   It may be that professional and other market participants who have discovered reliable trading rules or stratagems see no reason to divulge them to academic researchers. It might be that there's an information gap between the academics who study the markets and the professionals who work in them. Some observers point to seemingly inefficient features of the markets that can be exploited for example, seasonal tendencies and divergent returns to assets with various characteristics. E.g., factor analysis and studies of returns to different types of investment strategies suggest that some types of stocks may outperform the market long-term (for example, in the UK, the USA, and Japan).
   Skeptics of EMH argue that there exists a small number of investors who have outperformed the market over long periods of time, in a way which is difficult to attribute to luck, including Peter Lynch, Warren Buffett, George Soros, and Bill Miller. These investors' strategies are to a large extent based on identifying markets where prices don't accurately reflect the available information, in direct contradiction to the efficient market hypothesis which explicitly implies that no such opportunities exist. Among the skeptics is Warren Buffett who has that the EMH isn't correct, on one occasion wryly saying "I'd be a bum on the street with a tin cup if the markets were always efficient" and on another saying "The professors who taught Efficient Market Theory said that someone throwing darts at the stock tables could select stock portfolio having prospects just as good as one selected by the brightest, most hard-working securities analyst. Observing correctly that the market was frequently efficient, they went on to conclude incorrectly that it was always efficient." Adherents to a stronger form of the EMH argue that the hypothesis doesn't preclude - indeed it predicts - the existence of unusually successful investors or funds occurring through chance. In addition, supporters of the EMH point out that the success of Warren Buffett and George Soros may come as a result of their business management skill rather than their stock picking ability.
   It is important to note, however, that the efficient market hypothesis doesn't account for the empirical fact that the most successful stock market participants share similar stock picking policies, which would seem to indicate a high positive correlation between stock picking policy and investment success. For example, Warren Buffett, Peter Lynch, and George Soros all made their fortunes exploiting differences between market valuations and underlying economic conditions. This notion is further supported by the fact that all stock market operators who regularly appear in the Forbes 400 list made their fortunes working as full time businesspeople, most of whom received college educations and adhered to a strict stock picking philosophy they developed at a relatively early age. If "throwing darts at the financial pages" were as effective an approach to investment as deliberate financial analysis, one would expect to see casual, part time investors appearing in rich lists as frequently as professionals like George Soros and Warren Buffett.
   The efficient market hypothesis also appears to be inconsistent with many events in stock market history. For example, the stock market crash of 1987 saw the S&P 500 drop more than 20% in the Month of October despite the fact that no major news or events occurred prior to the Monday of the crash, the decline seeming to have come from nowhere. This would tend to indicate that rather irrational behaviour can sweep stock markets at random. Burton Malkiel, a well-known proponent of the general validity of EMH, has warned that certain emerging markets such as China are not empirically efficient; that the Shanghai and Shenzhen markets, unlike markets in United States, exhibit considerable serial correlation (price trends), non-random walk, and evidence of manipulation.

The EMH and popular culture

Despite the best efforts of EMH proponents such as Burton Malkiel, whose book A Random Walk Down Wall Street achieved best-seller status, the EMH hasn't caught the public's imagination. Popular books and articles promoting various forms of stock-picking, such as the books by popular CNBC commentator Jim Cramer and former Fidelity Investments fund manager Peter Lynch, have continued to press the more appealing notion that investors can "beat the market."
EMH is commonly rejected by the general public due to a misconception concerning its meaning. Many believe that EMH says that a security's price is a correct representation of the value of that business, as calculated by what the business's future returns will actually be. In other words, they believe that EMH says a stock's price correctly predicts the underlying company's future results. Since stock prices clearly don't reflect company future results in many cases, many people reject EMH as clearly wrong.
However, EMH makes no such statement. Rather, it says that a stock's price represents an aggregation of the probabilities of all future outcomes for the company, based on the best information available at the time. Whether that information turns out to have been correct isn't something required by EMH. Put another way, EMH doesn't require a stock's price to reflect a company's future performance, just the best possible estimate of that performance that can be made with publicly available information. That estimate may still be grossly wrong without violating EMH.

An alternative theory: Behavioral Finance

Opponents of the EMH sometimes cite examples of market movements that seem inexplicable in terms of conventional theories of stock price determination, for example the stock market crash of October 1987 where most stock exchanges crashed at the same time. It is virtually impossible to explain the scale of those market falls by reference to any news event at the time. The explanation may lie either in the mechanics of the exchanges (for example no safety nets to discontinue trading initiated by program sellers) or the peculiarities of human nature.
Behavioural psychology approaches to stock market trading are among some of the more promising alternatives to EMH (and some investment strategies seek to exploit exactly such inefficiencies). A growing field of research called behavioral finance studies how cognitive or emotional biases, which are individual or collective, create anomalies in market prices and returns that may be inexplicable via EMH alone. However, how and if individual biases manifest inefficiencies in market-wide prices is still an open question. Indeed, the Nobel Laureate co-founder of the programme - Daniel Kahneman - announced his skepticism of resultant inefficiencies: "They're [investors] just not going to do it [beatthe market]. It's just not going to happen."
Ironically, the behaviorial finance programme can also be used to tangentially support the EMH - or rather it can explain the skepticism drawn by EMH - in that it helps to explain the human tendency to find and exploit patterns in data even where none exist. Some relevant examples of the Cognitive biases highlighted by the programme are: the Hindsight Bias; the Clustering illusion; the Overconfidence effect; the Observer-expectancy effect; the Gambler's fallacy; and the Illusion of control.

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