FINS2624 Lecture Notes - Lecture 8: Efficient-Market Hypothesis, Abnormal Return, Fundamental Analysis

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18 May 2018

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8 – EMH & Behavioural Finance

Market Efficiency

• Curret pries should reflet iestors epetatios aout future perforae

• If investors consider all currently available information when forming expectations,

only NEW information can move price

 Could not have been inferred from currently available information – random

• Random Walks – market efficiency means stock prices follow random walk

 Price changes randomly and independently of history

 Only new information will move stock prices

 New information is random and independent of history

Efficient Markets Hypothesis (EMH): States that market prices reflect available

information (fully or to some degree)

• Weak Form – reflects info from historical public trading data (price/volumes)

• Semi-Strong Form – reflects all public information about a firm

• Strong Form – reflects all available information about firm, even non-public

• Stronger forms of EMH imply weaker forms

• Requires fast & correct incorporation of relevant information into prices

• Info enters prices via investors looking to make profits through info advantage

 Potential profit from information advantage is huge

 Chance of discovering unknown information is small

Power of Crowds – errors in guesses wash out and average guess may be correct without

any individual being able to make accurate valuation

According to EMH, trading strategies based on current info should not be profitable

• Profitable strategy means to generate an abnormal return (non-zero alpha)

Technical analysis: Using patterns in past stock prices/returns/trading to predict future

returns

• If profitable, violates weak EMH

Fundamental analysis: Using publicly available data to uderstad opaies

fundamentals & to make superior valuations

• If profitable, violates semi-strong EMH

Insider trading (illegal): Using non-public information to gain informational advantage

• If profitable, violates strong EMH

Tests of EMH

• Hard to get sufficient statistical power since volatility of prices is large compared to

presumed effect of individuals or strategies beating market (too much noise)

• Risk of reporting bias – i.e. only studies with interesting information get published

• To beat market, need to control for priced risk (e.g. using CAPM)

 Any test of EMH is test of validity of chosen pricing model

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Document Summary

Market efficiency: curre(cid:374)t pri(cid:272)es should refle(cid:272)t i(cid:374)(cid:448)estors(cid:859) e(cid:454)pe(cid:272)tatio(cid:374)s a(cid:271)out future perfor(cid:373)a(cid:374)(cid:272)e. If investors consider all currently available information when forming expectations, only new information can move price. Could not have been inferred from currently available information random: random walks market efficiency means stock prices follow random walk. Price changes randomly and independently of history. Only new information will move stock prices. New information is random and independent of history. Info enters prices via investors looking to make profits through info advantage. Potential profit from information advantage is huge. Chance of discovering unknown information is small. Power of crowds errors in guesses wash out and average guess may be correct without any individual being able to make accurate valuation. According to emh, trading strategies based on current info should not be profitable: profitable strategy means to generate an abnormal return (non-zero alpha) Technical analysis: using patterns in past stock prices/returns/trading to predict future returns.

Related Questions

Please write a 250 word response to the below discussion. Please provide 2 scholarly sources.

The efficient market hypothesis states that share prices reflect all relevant information, and that it is impossible to beat the market or achieve above-average returns on a sustainable basis. There are many critics of this theory, such as behavioral economists, who believe in inherent market inefficiencies. The Efficient Market Hypothesis (EMH) states that securities are very efficient in reflecting the truth about a stock. In other words, when new information is available about a stock, it is incorporated into the price adjustments of the stock quickly enough to reflect the actual state of the market (Malkiel, 2003). EHM debunks the idea that financial analysis and forecasting are useful tools to shop for stocks. In other words, because securities accurately reflect the status of that stock, it is not possible to use financial analysis to find an undervalued stock or predict trends in the market in order to buy low with the future insight to sell high. There are three levels, or degrees, of the efficient market hypothesis: weak, semi-strong, and strong. The weak form assumes that current stock prices reflect all available information, and that past price performance has no relationship with the future. In other words, this form of the hypothesis says that using technical analysis to achieve exceptional returns is impossible. The semi-strong form says that stock prices have factored in all available public information. Because of this, it's impossible to use fundamental analysis to choose stocks that will beat the market's returns. Finally, the strong form of the efficient market hypothesis says that all information, public as well as private, is incorporated into current stock prices. This form of the efficient market hypothesis essentially assumes a perfect market, and isn't plausible when there are insider trading restrictions (VanBergen, 2011). Yes, I believe that financial statement analysis can provide a significant advantage to an investor. The key is that empirical analysis, specifically statistical models aimed at forecasting future trends based on historical data, are simply another tool to be taken in to account. They are not a single source of the definitive answer. But, through the use of high power computing and mathematically rigorous modeling tools, forecasting is a powerful tool to provide additional insight to decision makers to include the financial industry. In support of my position, (Malkiel 2003) reports the findings of several empirical studies which demonstrate positive statistical correlations in short-run return analysis and the successful prediction of up to 40% of variation in long-term return analysis through time-series statistical models, which are able to take into account seasonal fluctuations (Malkiel 2003). One experimental study demonstrated with statistical significance that the Ohlson model provides predictive ability for future stock price movements (Lee, Chen and Tsai, 2014).

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FINS2624 Lecture Notes - Lecture 8: Efficient-Market Hypothesis, Abnormal Return, Fundamental Analysis

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