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Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
Efficient market hypothesis
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Efficient market hypothesis

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  • 1. Efficient Market Hypothesis
  • 2. • A model predicts with great confidence that a stock currently atRs.90 will rise in another 4 days to Rs.100• What would investors with access to the model do today ?• There would be a flurry of buy orders to cash in on this prospective• No one holding the stock would be willing to sell• So price would start to go up
  • 3. • And reach the target price of Rs.100 much earlier• So the price will immediately reflects the good news implicit in themodel• Forecast about favourable future performance leads instead tofavourable current performances• Why ?• Market participants try to cash in on the good news – Price Jump
  • 4. • Information – stock under priced – investors flock to buy in the stock– take it to the fair level• Only ordinary rates of return can be expected• If price is bid immediately to a fair level, what does it mean ?• All available information has been factored in• And any increase/decrease in price is only in response to newinformation
  • 5. Random Walk• What does this mean ?• New information by its very definition is unpredictable• If it was predictable it would have been a part of today’s information• Thus stock prices change in response to new information, so theymove unpredictably• Random Walk• But if it was as simple as that, why are there so many analysts in themarket looking for stocks which are under priced, so that they cansell them at a profit• This means that markets are not following random walk• Which means that markets are not efficient
  • 6. Caveat in Random walk• You see a pregnant women, you don’t know the Gender of the to beborn child. It is random walk to you.• But for the doctor, who is treating the women, the Gender of thebaby to be delivered can be known for certainty. Hence it is notrandom walk to the doctor.
  • 7. EMH• Fama introduced various versions of the EMH• But Lets Digress for a moment and understand what TechnicalAnalysis and Fundamental Analysis is?
  • 8. Technical Analysis• Technical Analysts search for recurrent and predictable patterns instock prices , so that an investor can ride that pattern and make aprofit out of it• Technical Analysts are also called chartists• Why ?• They study charts of past stock prices hoping to find patterns theycan exploit to make a profit
  • 9. Fundamental Analysis• Uses earnings and dividend prospects of firms, expectations offuture interest rates and risk evaluation of firm to determine properstock prices• Ultimately it represents an attempt to determine the presentdiscounted value of all payments a stockholder will receive fromeach share of stock• If that price > market price the fundamental analyst will recommendpurchasing of the stock
  • 10. Fundamental Analysis Contd..• Fundamental Analysis is usually started with the study of pastearnings and an examination of company balance sheets. This issupplemented by further detailed economic analysis, an evaluationof the firm’s management, the firm’s standing within industry and theprospects of the industry as a whole
  • 11. Fundamental Analysis• Discovery of good firms does no good to an investor if the rest of themarket also knows those firms are good• No abnormal returns• The trick is to find undervalued stocks before the market does• Poor performance firms can be great bargains if they are not as badas the market thinks they are
  • 12. Versions of Efficient Market Hypothesis(EMH)• Weak form hypothesis• Semi strong form hypothesis• Strong form hypothesis
  • 13. Weak form hypothesis• Stock prices already reflect all information that can be derived byexamining market trading data• History of past prices• Trading Volume• What does this mean ?• Technical Analysis is useless
  • 14. Weak form hypothesis• Past stock price data is publicly available and virtually cost less toobtain• All the information generated by technical analysis has already beenincorporated into the price• So a signal has lost its value
  • 15. Semi strong form hypothesis• All publicly available information is reflected in the stock price• So technical analysis is useless• And so is fundamental analysis• Every body has access to publicly available information• And they have already traded on it• So the information has already been incorporated into the price ofthe security• There are many well informed and well financed firms conductingsuch research and in face of such competition it will be difficult touncover data not also available to other analysts.• But this does not happen all the time
  • 16. Strong Form Hypothesis• Stock prices reflects all information relevant to the firm evenincluding information available to the company insiders• Public and private information• Tests
  • 17. Tests for weak form of efficiency• Auto Correlation test• To check whether past price is influencing present price• Pt = Pt-1 + ut• What does this mean ?• All information has not been incorporated into yesterday’s price• IS TODAYS price dependent on Yesterday’s Price?• Price changes to be independent( Unpredictable or Random Walk) ,Value of ‘r’ should be close to zero.
  • 18. Prices Change (Pt-Pt-1) Lag1 Lag2 Lag3650665 15665 0 15685 20 0 15685 0 20 0 15705 20 0 20 0708.5 3.5 20 0 20707 -1.5 3.5 20 0709.5 2.5 -1.5 3.5 20707 -2.5 2.5 -1.5 3.5706.5 -0.5 -2.5 2.5 -1.5718.25 11.75 -0.5 -2.5 2.5715 -3.25 11.75 -0.5 -2.5710 -5 -3.25 11.75 -0.5705 -5 -5 -3.25 11.75698 -7 -5 -5 -3.25691 -7 -7 -5 -5705 14 -7 -7 -5706.25 1.25 14 -7 -7709 2.75 1.25 14 -7715 6 2.75 1.25 14- 6 2.75 1.25Correlation Coeeficient -0.06047728 0.035792233 0.03577138
  • 19. Runs Test• What is a run ?• An uninterrupted sequence of same price changes.• (++,_ _,00) would be three runs.• It is a test of randomness of share prices.• a lower than expected number of runs indicates market’soverreaction to information, subsequently reversed,• while higher number of runs reflect a lagged response toinformation. Either situation would suggest an opportunity to makeexcess returns.
  • 20. Runs Test1-Apr 881.52-Apr 856.5 -253-Apr 859.75 3.254-Apr 918.5 58.755-Apr 1010.25 91.758-Apr 1072.25 629-Apr 1074.5 2.2510-Apr 1123.5 4911-Apr 1203.5 8012-Apr 1256.25 52.7515-Apr 1341.5 85.2516-Apr 1469.75 128.2517-Apr 1450.25 -19.518-Apr 1306.5 -143.7519-Apr 1258 -48.522-Apr 1332.75 74.7523-Apr 1315.75 -1724-Apr 1351 35.2525-Apr 1407.5 56.526-Apr 1547.75 140.2529-Apr 1521 -26.7530-Apr 1484.25 -36.75
  • 21. Runs Test• Runs Test• N1 = number of + symbols i.e. every time the price increases• N2 = number of - symbols i.e. every time the price decreases• N = N1 + N2 = total number of observations• Mean = (2 N1 N2 / N) + 1• Variance (σ2) = 2 N1 N2 (2 N1 N2 - N)/ N2( N -1)
  • 22. Runs Test• Null hypothesis is that prices are random (i.e. unpredictable)• Prob [ Mean - 1.96 σ ≤ R ≤ Mean + 1.96 σ ] = .95• Lets take an example• R = 3 , N1 = 19, N2 = 21• Mean = (2 N1 N2 / N) + 1 = 10.975∀ σ2= 2 N1 N2 (2 N1 N2 - N)/ N2( N -1) = 9.636∀ σ = 3.1134
  • 23. Runs Test• Mean - 1.96 σ = 10.975 – 1.96 ( 3.1134) = 4.8728• Mean + 1.96 σ = 10.975 + 1.96( 3.1134) = 17.0722• R=3 does not lie between this interval• So the null hypothesis is rejected• So prices are correlated or dependent
  • 24. Tests for Semi Strong Form
  • 25. Steps in Event Studies1. Collect a sample of stocks that had a surprise announcement ( Event)2. What causes a prices to change is an announcement that has a surprise.– Positive surprises has +ve price changes– Negative surprises has –ve price changes– Stock splits, bonus, Earnings announcements, mergers etc.,1. Determine the precise day of the announcement and designate this day asZero.2. Define the period to be studied( Event Period)– Usually -30 to + 30 days.1. For each of the firms in the sample, compute the return on each of the daysbeing studied2. Compute the abnormal returns for each day in the event period.3. Compute for each day in the event period the average abnormal returns forall the firms in the sample.4. Often the individual day’s abnormal return is added together to compute thecumulative abnormal return from the beginning of the period.
  • 26. ExampleA Ltd has announced a stock split. A fund manager in order to testthe consistency of the semi strong form of market efficiency,calculated the single index model taking into account the returns ofA Ltd and the market index for a period of one year on weekly basisup to three weeks before the stock split decision was announcedRi =1.33 + 1.1 Rm
  • 27. ExampleWeeks Actual return Market Return-3 14.75 12.15-2 14.4 11.95-1 14.82 12.20 15.01 12.351 14.92 12.32 14.68 12.23 14.38 11.95
  • 28. Example• Ri =1.33 + 1.1 Rm• We need find out the abnormal return• ei = Ri – (αi + βi Rm)
  • 29. ExampleWeeks Actual Return(AR) Market Return Expected Ret.(ER) Abnormal Ret.(%)-3 14.75 12.15 14.70 0.05-2 14.4 11.95 14.48 -0.07-1 14.82 12.2 14.75 0.070 15.01 12.35 14.92 0.091 14.92 12.3 14.86 0.062 14.68 12.2 14.75 -0.073 14.38 11.95 14.48 -0.09∑ Abnormal Return 0.04Ri = 1.33 + 1.1 Rm AR-ER
  • 30. Example• Very close to zero so market is semi strong form efficient• But what is the problem with this example ?
  • 31. Event Study• Examines the excess returns around a specific information event• Dividend announcement, earnings announcement , stock split• Steps
  • 32. Event Study• Step 1 : Identify the event• Pinpoint the announcement date• Markets react to the announcement of an event rather than theevent itselfAnnouncement Date
  • 33. Event Study• Step 2: Collect returns data around the announcement dateAnnouncement Date- n + n
  • 34. Event Study• Step 3: Calculate the abnormal return• Abnormal return = Ri – E(Ri)
  • 35. Event Study• Calculate the Average Abnormal return for each company for eachtrading period• Calculate the Cumulative Average Abnormal return (CAAR)• CAAR = ∑ ( Average Abnormal returns for all companies)• How we do it, we will see in the example
  • 36. Event Study - Example• Four companies , A Ltd., B Ltd., C Ltd. and D Ltd have increasedtheir level of cash dividends for the year 2001. A financial analyst ata mutual fund wanted to test the consistency of the semi strong formof market efficiency. He calculated the characteristic lines for aperiod of six years on weekly basis upto four weeks before theannouncement took place• The four equations are• rA = 1.70 + 1.05 rM• rB = 1.53 + 1.08 rM• rC = 1.92 + 1.02 rM• rD = 1.42 + 1.09 rM
  • 37. Event Study - ExamplePeriod Actual Return Market ReturnA B C D-4 13.47 13.61 13.35 13.6 11-3 11.9 12.04 11.82 12.03 10-2 13.5 13.67 13.42 13.71 11.15-1 12.97 13.12 12.88 13.13 10.880 13.43 13.6 13.31 13.59 10.91 12.5 12.62 12.41 12.63 10.052 13.09 13.25 12.99 13.27 11.053 14.51 14.71 14.37 14.76 12.154 14.8 14.99 14.65 15.02 12.67
  • 38. Event Study - ExamplerA = 1.70 + 1.05 rMPeriod Stock A Market Return Expected Return Abnormal Return-4 13.47 11 13.25 0.22-3 11.9 10 12.20 -0.30-2 13.5 11.15 13.41 0.09-1 12.97 10.88 13.12 -0.150 13.43 10.9 13.15 0.291 12.5 10.05 12.25 0.252 13.09 11.05 13.30 -0.213 14.51 12.15 14.46 0.054 14.8 12.67 15.00 -0.20
  • 39. Event Study - ExamplerB = 1.53 + 1.08 rMPeriod Stock B Market Return Expected Return Abnormal Return-4 13.61 11 13.41 0.20-3 12.04 10 12.33 -0.29-2 13.67 11.15 13.57 0.10-1 13.12 10.88 13.28 -0.160 13.6 10.9 13.30 0.301 12.62 10.05 12.38 0.242 13.25 11.05 13.46 -0.213 14.71 12.15 14.65 0.064 14.99 12.67 15.21 -0.22
  • 40. Event Study - ExamplerC = 1.92 + 1.02rMPeriod Stock C Market ReturnExpectedReturn Abnormal Return-4 13.35 11.00 13.14 0.21-3 11.82 10.00 12.12 -0.30-2 13.42 11.15 13.29 0.13-1 12.88 10.88 13.02 -0.140 13.31 10.90 13.04 0.271 12.41 10.05 12.17 0.242 12.99 11.05 13.19 -0.203 14.37 12.15 14.31 0.064 14.65 12.67 14.84 -0.19
  • 41. Event Study - ExamplerD = 1.42 + 1.09rMPeriodStock D(1) Market ReturnExpectedReturn (3)Abnormal Return(1-3)-4 13.6 11.00 13.41 0.19-3 12.03 10.00 12.32 -0.29-2 13.71 11.15 13.57 0.14-1 13.13 10.88 13.28 -0.150 13.59 10.90 13.30 0.291 12.63 10.05 12.37 0.262 13.27 11.05 13.46 -0.193 14.76 12.15 14.66 0.104 15.02 12.67 15.23 -0.21
  • 42. Event Study - Example• So we have abnormal returns for every period• We can calculate the average abnormal returns
  • 43. Event Study - Example(.22 +.20+ .21+ .19)/4Week AAR(%)-4 0.205-3 -0.295-2 0.113-1 -0.1500 0.2861 0.2442 -0.2063 0.0664 -0.208
  • 44. Event Study - ExampleWeek AAR(%) CAR(%)-4 0.205 0.205-3 -0.295 -0.090-2 0.113 0.023-1 -0.150 -0.1270 0.286 0.1591 0.244 0.4042 -0.206 0.1983 0.066 0.2644 -0.208 0.056CAAR 0.056
  • 45. -0.400-0.300-0.200-0.1000.0000.1000.2000.3000.4000.500-4 -3 -2 -1 0 1 2 3 4AARCAR
  • 46. Event Study - Example• Since the value of CAAR is close to zero we conclude that marketsare efficient in the semi strong form
  • 47. Strong form efficiency• Not easy to test• All investors who own more than a sufficient percentage ofoutstanding shares or at sufficiently higher levels of the organizationare known as insiders• Super strong form– Insiders of a company or specialists in stock exchanges– In NYSE it has been shown that specialists consistently makeabnormal profits.• Near Strong Form– Performance of mutual fund managers.– Mangers on an average failed to deliver abnormal returnsconsistently.
  • 48. Are Markets Efficient?• Magnitude Issue– Investment manager improving the performance by 0.1% on a500 crore fund translates into increase investment earnings of50 Lacs.– Can we statistically measure his/her contribution? Probably not.• The selection Bias Issue.– You know the technique/Investment scheme – You eitherpublish it or keep it a secret.– If you can really make money through the scheme you would liketo keep it a secret. You will be publishing it only if you cant makemoney out of it.
  • 49. Are Markets Efficient?– You can test whether you can make abnormal profits based onthe investment strategy or technique you know/publiclyavailable. The fact that it is publicly available may make ituseless.– Selection bias- here is the outcomes we are able to observehave been pre-selected in favor of failed attempts.• The lucky event Issue.– You could be fairly lucky
  • 50. Research• Overall we can say that Indian stock markets along with otheremerging Asian markets is not weak-form efficient (Worthington• H. Higgs(2005))• However there are many studies in the developed markets whichshow that the developed markets are semi-strong form efficient.• Academic world argues that Markets in US and other developedmarkets are usually efficient. They term all the evidence againstmarket efficiency as anomalies.• Most of the tests are Joint test of efficient market hypothesis and therisk adjustment procedure (CAPM). Usually it is the risk adjustmenttechnique that is questioned rather than the EMH.
  • 51. Evidence on Market EfficiencyReturns over short Horizon– Thomas and Patnaik(2002) found no serial correlation for nifty index. But forindividual stocks they observed serial correlation at 5 minute interval.– (3 to 12 month holding period) Jegadeesh and Titman(1993) found a momentumeffect in which good or bad recent performance of a particular stock continuesover time.• Returns over Long horizon– Debondt and Thaler(1985)• Rank order the performance of stocks over a 5 year period and group stocksbased on investment performance.• Loser portfolio defined as 35 stocks with worst performance• Winner portfolio defined as 35 stocks with best performance.• Loser portfolio out-performed the winner portfolio by an average of 25% inthe following 3 year period.• Reversal effect- Losers win and winners fade back suggest overreactionhypothesis
  • 52. Evidence on Market Efficiency• Calendar Effects– January Anomaly– Positive abnormal returns in the month of January.• Tax Effect– Weekday effect• Weekend effect ( Friday close to Monday open) –ve Returns• Monday Effect (Monday open to Monday close) +ve Returns– Intraday effect• U pattern in trading.• Price earnings ratios and returns.– Returns for stock with lower P/E ratios were superior to those withhigher P/E ratios.– Investors overestimate the growth potential and thus overvalue thegrowth companies.
  • 53. Evidence on Market Efficiency• The size effect.– small firms have significantly higher risk-adjusted returns thanlarger firms.– Portfolio of small firms outperforms the portfolio of large firms by4% per month. Beta of small firms is 1.17 as against beta of 0.85for a portfolio of large firms.– Even when returns were adjusted for risk using CAPM. Apremium of 3.6% existed in the market.• The small firm in January Effect– Later studies showed that small firm effect virtually occurs in themonth of January and in the first two weeks of January. The sizeeffect is in fact the “small firm in January effect”
  • 54. Evidence on Market Efficiency– Neglected firm effect• Arbel (1985) show that the small firm effect is because theyare neglected.• Divides the firms into Highly researched, moderatelyresearched and neglected stocks based on the number ofinstitutions holding the stock.• The January effect is in fact largest for the neglected firms.• Book value-Market value (BV/MV)– Significant positive relationship is found to exist between a firmshistorical BV/MV and future stock returns.
  • 55. Evidence on Market Efficiency• Amihud and Mendelson (1986)– Effect of liquidity on stock returns.– Investors will demand a premium in rate of return to invest instocks that entail higher trading costs.– Small and less analyzed stocks as a rule are less liquid.– Liquidity effect may partially explain the SIZE effect.

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