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Understanding Efficient Market Hypothesis: Key Views and Implications

Explore the concept of Efficient Market Hypothesis (EMH) with insights from Malkiel, Shiller, and Shleifer. Learn about the implications, views, and implications of EMH in modern finance.

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Understanding Efficient Market Hypothesis: Key Views and Implications

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  1. Behavioral Finance Economics 437

  2. Immediate Reading (today, Jan 24) • Malkiel (online) • Shiller (online) • Shleifer (book, Ch 1)

  3. The Efficient Market Hypothesis(EMH) • Price captures all relevant information • Modern version based upon “No Arbitrage” assumption • Why do we care? • Implications • Only new information effects prices • Publicly known information has no value • Investors should “index” • Allocation efficiency

  4. Definition of EMH (Eugene Fama’s Definition) from Shleifer’s Chapter One • Weak Hypothesis: past prices and returns are irrelevant • Semi-Strong Hypothesis: all publicly known information is irrelevant • Strong Hypothesis: public and private information is irrelevant

  5. The Malkiel View • Burton Malkiel, author of “Random Walk Down Wall Street” • His view is that the evidence shows that money managers cannot beat simple indexes like the S&P500 over time • To Malkiel, that means the market is efficient

  6. Robert Shiller’s View • Prices should be based upon fundamental • Future cash flows (or dividends) and future interest rates • Prices are way too volatile as compared to the modest changes over time in expectations of future cash flows and interest rates • Thus, the market is not efficient – prices are too volatile to be consistent with efficiency

  7. A Martingale Process • Imagine a process X(t) over time • For any t, E[X(t)] is the “expected value of X at time” • Either: • ∑ Xi*P(Xi) for i: 1 to n if only discrete values of X • ∫X*f(X) dX where f(X) is a probability density function • “Expected value” is an average (weighted) • A Martingale Process is defined as a process with the following property: • E[X(t)] = X(s) for all t, s where s > t

  8. Example of a “Martingale Process” • Coin flip • X(t) where X(0) = 0 • X(t+1) = X(t) plus F(t) • Where F(t) = +1 if coin flip is heads • Where F(t) = -1 if coin flip is tails • If p(H) = P(T) = ½ • Then E[X(t+1)] = X(t) • And E[X(s)] = X(t) where s> t • Hence X(t) is a Martingale Process

  9. Can stock returns be a “Martingale Process?” • E[P(s)] = P(t) for all s > t ? • But shouldn’t stocks earn a return? • Suppose the mean return of a stock is r • Create a new variable, Q and assume s > t • Let Q (s) = P(s)*(1+r)-n where n = s – t • Then Q(t) = P(t) • E[Q(s)] = Q(t) for all s > t • Means that, after subtracting out a mean return of r, P(t) is a Martingale Process

  10. Modern Finance Assumes • Stock Prices (Adjusted) Follow a Martingale Process • This is the definition of EMH in the modern finance literature • Also known as “random walk”

  11. The End

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