Learning Objectives
What does the Efficient Market Hypothesis imply?
Do prices reflect all available information?
There are a number of well known trading strategies that can generate positive returns
Are the returns to anomaly strategies compensation for risk?

BKM Ch. 11
Asness and Liew (2014)

Determination of prices
A share’s value is defined by the present value of expected future cash flows:

Present value model requires information on:
Future cash flows (i.e. dividends)
Risk via the cost of capital

Market bases expectations on this information
Prices respond to changes in expectations
Changes in expectations arise from new information
Therefore, prices change as new information arrives
Market efficiency
Two components to efficiency:
Informational efficiency
•Reflects speed at which new information in incorporated into prices
Market rationality

Market Efficiency
•New information is correctly incorporated into stock prices
–In an efficient market, new information is incorporated into prices in an instantaneous and unbiased manner
–Information set cannot be used to consistently earn excess returns
–A forecast about favourable future performance leads to favourable current performance, as market participants rush to trade on new information
•Result: Prices change until expected returns are exactly commensurate with risk
–New information is unpredictable; if it could be predicted, then the prediction would be part of today’s information
–Stock prices are unpredictable because information is also unpredictable
–Market efficiency
–What makes investors want to trade until prices reflect information?
–Information-gathering is motivated by desire for higher investment returns
–The marginal return on research activity may be so small that only managers of the largest portfolios will find them worth pursuing
–In an efficient market, the marginal cost of obtaining and acting on information should not exceed the marginal benefit derived from such actions
–Inefficiencies that are not economically exploitable can still be consistent with an efficient market
–If markets were inefficient, resources would be systematically misallocated
–Firm with overvalued securities can raise capital too cheaply
–Firm with undervalued securities may have to pass up profitable opportunities because cost of capital is too high
–Market efficiency
–Market efficiency does not imply predictability
–It is actually inefficiency that implies predictability
–Prices are not randomly set
–Information arrives randomly and therefore prices move randomly
–Large movements in prices can be consistent with efficiency
–If the rate of new information arrival is high and this information implies variations in prices, then it is expected that price changes would be frequent and of varying magnitude
–Expected return equals observed return
–There is nothing in the model or in the efficient markets hypothesis that requires actual return to equate to expected return given by the CAPM
–Investors will all perform equally
–Some investors will win and some investors will lose
–Classes of information
–Weak form
–Do current market prices fully reflect information contained in past prices and volume?
–Semi-strong form
–Do prices incorporate publicly available information?

•This information includes financial reports, press releases, stock exchange announcements, capital structure changes and takeover announcements
–Market Efficiency
–Strong form
–Does the possession of private information leads to excess returns?
–Insider trading?
–Equity Market Anomalies
–What is an anomaly?
–“Something that deviates from what is standard, normal, or expected”

–Schwert’s (2003) finance definition of an anomaly
–“Anomalies are empirical results that seem to be inconsistent with maintained theories of asset-pricing behavior”

–What do you think about the theories of asset pricing we have discussed in this course?

–Anomalies are about predicting future returns using a signal
–Calculating anomaly returns
–Calculating anomaly returns
–The joint test problem
–Any test of market efficiency necessarily compares returns against some benchmark model

–A benchmark for the expected return is required before we can measure excess returns
–One possible benchmark is a formal asset pricing model, such as the capital asset pricing model (CAPM)

–Hence, to define excess returns in order to test market efficiency, a model for expected return is first required

–Thus, any test of market efficiency is inherently a joint test of market efficiency and the model of expected return
–Anomalies based on past price information
–Price momentum
–Stocks with the highest returns in the past 3–12 months have higher returns in the future
–Jegadeesh and Titman (1993) find that the buy winners and sell losers trading strategy earned statistically significant positive returns averaging 12.01% p.a.
•This is based on a six-month past performance period and a six-month holding period

–Longer-term price reversals
–DeBondt and Thaler (1985)
–Stocks with the lowest returns over past 3-5 years (losers) outperform winners

–Returns on U.S. momentum portfolios
–Returns on U.S. momentum portfolios
–Momentum in Australia
–Firm size and returns
–Fama French SMB factor is based on size (market capitalisation)
–Small firms outperform large firms, on average

–Banz (1981)
–In USA, shares in small companies were found to outperform shares in larger companies on a risk-adjusted basis
–Evidence that a trading strategy of taking long positions in a portfolio of small firms and short positions in a portfolio of large firms can earn risk-adjusted profits of around 20% p.a.
–The relationship between firm size and abnormal returns is not linear and is concentrated in the smallest decile of firms
–Keim (1983) finds that about half of the annual size premium to small firms occurs in January and that about half of this effect occurs over the first five trading days of the new calendar year
–Firm size and returns in the U.S.

–Firm size and returns in the U.S.
–Growth shares versus value shares
–Growth shares have low book-to-market ratios while value shares have high book-to-market ratios

–The results of Fama and French (1992) show that forming portfolios ranked on the basis of book-to-market ratios yields a differential in annual returns between the value shares and growth shares

–In long-run, value outperforms growth

–Each year stocks can be ranked into 10 portfolios based on their book-to-market ratio to show the value premium
–Growth shares versus value shares in the U.S.

–Growth shares versus value shares in the U.S.
–Size and value in Australia
–Idiosyncratic volatility anomaly
–Ang, Hodrick, Xing and Zhang (2006) “The Cross-Section of Volatility and Expected Returns”, The Journal of Finance, Vol. LXI, No. 1, pp. 259-299.
–Idiosyncratic volatility is defined as the volatility of the residuals from the Fama French 3-factor model, estimated on daily data for each month.
–Stocks with high idiosyncratic volatility have lower returns, on average
–The return difference between the 20% highest ivol stocks and 20% lowest ivol stocks is -1.06% per month
–These low average returns to stocks with high idiosyncratic volatility cannot be explained by exposures to size, book-to-market, leverage, liquidity, volume, turnover, bid-ask spreads, coskewness, or dispersion in analysts’ forecasts characteristics.
–IVOL anomaly also exists internationally
–Idiosyncratic volatility anomaly
–Some international evidence on anomalies
–Fama and French (2012) “Size, value, and momentum in international stock returns”, Journal of Financial Economics, Vol. 105, pp. 457-472.
–Find that there is a value premium and momentum effects in North American, European, and Asia Pacific equities
–Both the value and momentum returns decrease from smaller to bigger stocks.

–Asness, Moskowitz and Pedersen (2013) “Value and Momentum Everywhere”, Journal of Finance, Vol. 68, pp. 929-985.
–Value and momentum anomalies exist in individual stocks in the United States, the United Kingdom, continental Europe, and Japan; country equity index futures; government bonds; currencies; and commodity futures
–Event studies
–Abnormal returns are the difference between actual returns and expected returns
–Abnormal returns are risk-adjusted returns

–If we were using the CAPM as our asset pricing model then expected returns would be related to a firm’s beta and the actual market risk premium

–The study of abnormal returns around announcements or particular events are called event studies
–Efficiency and news/information announcements
–Semi-strong form of EMH

–Information announcements

–Accounting information
–Earnings announcements
–Components of earnings
–Dividend information
–Post-earnings announcement drift

–Use event study methodology to evaluate anomaly
–CARs of target firms before takeover attempts
–Stock price reaction to CNBC reports
–Post-earnings announcement drift
–SUE is the earnings surprise
–Category 10 is the highest positive surprise
–Category 1 is the most negative surprise

–Source: Rendleman, Jones and Latane (1982)
–How do anomalies behave over time?
–Chordia, Subrahmanyam, and Tong (2014) “Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?”, Journal of Accounting and Economics, Vol. 58, pp. 41–58.
–The returns from anomaly trading strategies has reduced over time
–Improvements in liquidity have reduced trading frictions and improved market efficiency

–Is the past a good guide of the future?
–1-year moving average anomaly returns
–How good are academics? Just ask us…
–McLean, D.R. and J. Pontiff (2016) “Does Academic Research Destroy Stock Return Predictability?”, Journal of Finance, vol. LXXI, No. 1, pp. 5-31.

–They examine 97 anomaly signals published in academic journals and compare the returns generate from the trading strategy before and after publication
–They find that the anomaly return decreases after publication and attribute 26% of it to sample specific biases and 32% to the publication effect
–They argue that the decline from publication indicates mispricing rather than risk explains anomaly returns
–They also show that the decline in anomaly returns after publication is greatest for signals based on price and trading data

–Parting thoughts: Interpreting the anomalies
–Are these really anomalous or are they the result of data mining?
–Are the anomalies risk premiums or anomalies?
–Do studies adequately control for all other risk factors and other potential explanations?

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