Bài giảng Financial Markets - Lecture 6: Efficient Markets and Excess Volatility

Lecture 6: Efficient Markets and Excess VolatilityThe Efficient Markets HypothesisHistory of the HypothesisReasons to think markets are efficientReasons to doubt markets are efficientTechnical analysisEmpirical evidence in literatureHomework assignment and regressionsEarliest Known Statement“When shares become publicly known in an open market, the value which they acquire there may be regarded as the judgement of the best intelligence concerning them.” - George Gibson, The Stock Exchanges of London Paris and New York, G. P. Putnman & Sons, New York, 1889Intuition of EfficiencyReuter’s pigeons and the telegraphBeepers & the internetMust be hard to get richTextbook Version TodayAs one of the six most important ideas in finance:“Security prices accurately reflect available information, and respond rapidly to new information as soon as it becomes available” Richard Brealey & Stewart Myers, Principles of Corporate Finance, 1996Harry Roberts, 1967Weak form efficiency: prices incorporate information about past pricesSemi-strong form: incorporate all publicly available informationStrong form: all information, including inside informationPrice as PDV of Expected DividendsIf earnings equal dividends and if dividends grow at long-run rate g, then by growing consol model P=E/(r-g), P/E=1/(r-g). (Gordon Model)So, efficient markets theory purports to explain why P/E varies across stocksPEG ratio is popular indicator = g’/(P/E), where g’ is short-run growth rate; popular rule of thumb: buy if PEG<0.5PEG rule of thumb makes sense only if g’ bears a certain relation to g; not a sensible rule.Efficient markets denies that any rule worksReasons to Think Markets Ought to Be EfficientMarginal investor determines pricesSmart money dominates tradingSurvival of fittestReasons to Doubt these ReasonsMarginal investor: wealth mattersSmart money: matter of degree. Limits to arbitrage theorySurvival of fittest: life cycle renewsPsychological FactorsGambling behaviorOverconfidenceSlowness to make money, futility of career trying to prove others of one’s abilitySiegel and Peter LynchPopular Doubters of EfficiencyPeter Lynch: Elementary school children beat professionalsBeardstown LadiesRobert Kiyosaki Rich Dad, Poor DadMotley FoolRaskob on the Market“Suppose a man marries at the age of twenty-three and begins a regular saving of fifteen dollars a month – almost anyone who is employed can do that if he tries. If he invests in good common stocks and allows the dividends to accumulate, he will have at the end of twenty years at least eighty thousand dollars. . .I am firm in my belief that anyone not only can be rich but ought to be rich.” John J. Raskob, Ladies Home Journal, 1929Raskob’s CalculationAnnuity formula (converted to terminal value) shows that Raskob assumed 26% per year returns:Technical AnalysisRobert D. Edwards & John Magee, Technical Analysis of Stock Trends, 1948.Hand drawing of charts, judgmental interpretation of patternsDifficult to test success of technical analysisHarry Mamaysky, SOM finds some success in their methods.Head & Shoulders PatternInitial advance attracts traders, upward momentum. Smart money begins to distribute stock, trying not to kill demand.Eventually downturn, but smart money comes in to support demand, manipulation. (left shoulder)Upward momentum resumes, ends when smart money has distributed all shares; market drops.New traders try to exploit well-known tendency to rally. New weak rally, right shoulder, then a breakout. (Edwards & Magee)Random Walk HypothesisKarl Pearson, Nature, 72:294, July 27, 1905. Aug 10, 1905, walk of drunkBurton Malkiel, A Random Walk Down Wall Street, 1973.Random Walk & AR-1 ModelsRandom Walk: xt=xt-1+tFirst-order autoregressive (AR-1) Model: xt=100+(xt-1-100)+t. Mean reverting (to 100), 0< <1.Random walk as approximate implication of unpredictability of returnsSimilarity of both random walk and AR-1 to actual stock pricesRandom Walk & AR-1(=.95)Obvious Examples of InefficiencyJeremy Siegel – Nifty-fifty did wellRebalancingMost closed outPolaroid and Edwin LandTulipmaniaHolland, 1630s. Peter Garber, Famous First BubblesMosaic virus, random-walk lookFree press began in Holland then.Dot Com BubbleToys.com: Had disadvantage relative to bricks & mortar retailers starting web sitesLastminute.com: travel agency, sales in fourth quarter of 1999 were $650,000, market value in IPO ins March 2000 was $1 billion.Problem Set #3: Forecast the MarketStep 1: Get stock price data on spreadsheet, as from yahoo.com.Step 2: Create new column showing percentage price changesStep 3: Create new Column(s) containing forecasting variablesStep 4: Test for significance and interpret results.Significance Test in RegressionUse the R2 which is the fraction of the variance of the dependent variable that is explained by the regression.Compute F statistic (k, n-k-1 degrees of freedom, and check that it is above critical value for significance at 5% level.Issues of data mining, etc.F StatisticF statistic with k, n-k-1 degrees of freedom, where k = number of independent (forecasting) variables and n = number of observations:Regression Output - ExcelIntercept, X Variable, X VariableT statistic, P valueF statistic, P valueR squared