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Learn about the Efficient Market Hypothesis (EMH) in modern finance, its implications for investors, and opposing views from experts like Malkiel and Shiller. Discover how EMH relates to stock price predictability, market efficiency, and the concept of Martingale Process.
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Behavioral Finance Economics 437
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
Today’s Markets • US Market Down 8.5 % since year began • Other markets globally down more • What information changed? • Oil prices fell (from 35 to 30)? • Dollar rise? • Fed liftoff? • What about earnings?
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
Andrei Shleifer’s Chapter One • Explains why EMH dominated thinking for decades • Suggests reasons that EMH was undermined • Stock price predictability • Failure of news to correlate to prices • Excessive stock price volatility
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
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
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
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
Coin flip (Net Heads = Heads – Tails) 2 1 0 -1 -2
Can stock returns be a “Martingale Process?” (Accounting for Trend) • 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
Random Walk Around a Rising Trend (This is a martingale with trend subtracted out) 2 1 time
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” (not quite correctly, as Fama points out in his 1970 article)
