1. Efficient Market Hypothesis

2. Efficient Market Hypothesis Derived from Random Walk Hypothesis With a few modifications The concept may first be traced to writings of Bachellier (1900) However, in modern finance, has been developed on the basis of research during1953-1965 As a theory, the concept can be traced to Paul A Samuleson (1965) Eugene Fama through his research papers in 1965 & 1970 established it as aproper theory and came up with the nomenclature Though several interpretations and Fama himself diluted the concept to a large extent later on Became the central piece of finance during 1970-1980 Research thereafter especially 1987 started to question the validity of EMH Emergence of Behavioral Finance Heavily bruised but not yet knocked out; the fight continues The shifting goal-posts strategy has helped it to be from knocked out and may rather saveit in one form or other The EMH of 1970 is however dead even in the writings of its proponent. 3. Concept of Efficiency When prices fully reflect all available information and informationis discounted immediately, market is said to be informationallyefficient. When security prices are determined in a way investible capitalresources are optimally allocated in favour of best return provider(for a given level of risk), market is said to be allocationallyefficient; 4. Concept of Efficiency in EMH 1st proposition of FAMA (1965,1970) Informational Efficient Which breeds Allocational Efficiency Means money will go to the best return provider Later on diluted to Informational Efficiency only Latest There might be anomalies like Momentum, Mean-reversion,etc. [Tactical Avoidance of Inefficient Word] But not possible to earn superior return 5. Lets hear from Horses Mouth Fama (Jan. 1965: The behaviour of stock-market prices):an efficient market for securities, that is, a market where, given the available information,actual prices at every point in time represent very good estimates of intrinsic values.[Intrinsic Implies Fundamental] Echoes Samuleson When stock prices equal the present expected value of their payoffs (theirfundamental value given a discount factor) they are unpredictable. Fama (1970):A market in which prices always fully reflect available information is called efficient. Jensen (1978):A market is efficient with respect to information set t if it is impossible to make economic profitsby trading on the basis of information set t [By economic profits, we mean the risk adjustedreturns net of all costs.] Fama (1991):I take the market efficiency hypothesis to be the simple statement that security prices fully reflectall available information. A weaker and economically more sensible version of the efficiencyhypothesis says that prices reflect information to the point where the marginal benefits of acting oninformation (the profits to be made) do not exceed marginal costs (Jensen (1978). Fama (1998):market efficiency (the hypothesis that prices fully reflect available information)...the simple market efficiency story; that is, the expected value of abnormal returns is zero, butchance generates deviations from zero (anomalies) in both directions. 6. Foundations of EMH There is an intrinsic value of financial assets The huge army of traders keep prices near that intrinsic value No Arbitrage Opportunity Sophisticated traders keep the value near to its intrinsic value Though chance of noise, dependence and bubbles The sophisticated traders will move to take benefit and keep bubbles to burstbefore they build up to a sizeable extent. Price change will be subject to flow of new information which was not earlieranticipated Information shall be absorbed immediately If there is a lag (delay) in absorption, that will be random Sometimes before the news, sometimes after the news Joint Hypothesis As information absorption and its efficiency can be determined only withreference to equilibrium pricing A test of efficiency will always be a joint test of Market Efficiency Pricing Model [CAPM, APT etc] So one can never be sure which one to blame 7. Forms of EMH Fama (1970)[Taxonomy (Naming) suggested by Roberts(1967) Weak-Form [later on Dubbed as Test for Return Predictability Fama 1991] the information set includes only the history of prices or returns themselves A capital market is said to satisfy weak-form efficiency if it fully incorporate the information inpast stock prices. If true, past prices alone would not be useful in making money. Technical analysis is of no use. Semi-Strong Form [later on Dubbed as Event Studies] the information set includes all information known to all market participants (publiclyavailable information). A market is semi-strong efficient if prices reflect all publicly available information. Past & Future expected performance, results, dividends etc are not useful in finding under-valuedstocks. Fundamental analysis is of no use Strong Form [later on Dubbed as Test for private information] the information set includes all information known to any market participant (privateinformation). This form says that anything that is pertinent to the value of the stock and that is known to atleast one investor is, in fact fully incorporated into the stock value. Even private information with insiders, market makers etc is of no use in generating excessreturns.Taxonomy is relative and does not really shows something as weak or strong in absolute sense.Momentum, a pure technical phenomenon, remains the biggest anomaly [Fama, 1997] 8. Implications of EMH Deviation from value is not ruled out,however Equal chance that prices willover-valued or under-valued For instance, stocks with lower PE ratios shouldbe no more or less likely to be under valued thanstocks with high PE ratios. As a result, no investor shall be consistently ableto find under-valued stockNo investor or group of investorsshould be able to consistentlyoutperform the market 9. Support for Efficient Walk Hypothesis Came in the form of various test results pre 1980 The major tests in that era were in the form of Serial Correlation Run Tests Filter Test Buy if price moves up by X% & sell if moves down by X% Absurd level of simplification to reject technical analysis rules Event Studies Impact on price of events such as stock split, earnings announcements etc. It was found that market usually anticipated, absorbed and adjusted to theinformation quickly As a result Weak Form was readily accepted; Semi-Strong form was accepted as well Strong form was not supposed to be possible EMH became THE GOD of modern finance for next 15-20 years Such a powerful God that blasphemous papers (those criticizing EMH) were notaccepted by journals for publication [Until one of the big priests intervened] 10. Inherent Theoretical Flaws with EMH Assumes only news flow causes change in market price [Exogamous Markets] In Reality, Markets may change just in response to change in price Bubbles are built up in such a fashion only Market is an endogamous as well as exogamous entity Theoretical support from French & Roll (1986) who found that return volatility is high fordays when markets are open for trading than on holidays; Dubbed noise by Black (1986) 1987 Crash when Portfolio Insurance (based upon EMH & CAPM) failed and the resultingchaos caused 22% fall in a single trading session is an important example The housing market bubble of 2001-2007 is just another example Assumes equilibrium but markets are in a constant dynamic environment [Bernstein 1999] From the above two, EMH concludes that prices are always right and hence determineallocational efficiency If that is the case, how bubbles and their bursting is explained Not testable in itself and the pricing models can always be blamed As Fama & French did in 1992 Ketch-Up Economics [Summers] If markets are efficient due to presence of large no. of rational investors And they cant beat the market Then they will stop looking for beating market and will thus market will become inefficient Also, there is no incentive for anybody to do research as its a costly activity[Grossman & Stiglitz 1980] Fisher answered that Noise traders subsidize this cost 11. EMH- Negative Tests Test of Fundamental Value Variance Bound tests [Shiller 1981] Price fluctuations in the long term are too large to be justified by variation individend payments It means that people over-react Shiller rejected EMH altogether Equity Risk Premium Puzzle The average risk premium in US between 1889-1978 was 7% However, as per models of consumer behaviour; for average RFR of 0-4%, riskpremium shall not exceed by 0.35% [ Mehra & Presscot 1985] Anomalies Behavioral Criticism 12. Anomalies Anomaly means deviation from Norm So the results which are not in confirmation with EMH are dubbed as anomalies But what if EMH is an anomaly itself? With Specific reference to EMH, anomalies are defined as a regular pattern in an assets returns which is reliable, widely known, andinexplicable [Andrew Lo, 2007] Some of the Anomalies are Size Effect Calendar & Seasonal Effect ---Like January Effect, Monday Effect etc. Momentum Mean Reversion 13. Momentum For price Movement in Months (3-12) Jagdeesh & Teetman [2001] Significant Positive correlation What Goes Up-Goes Further Up (next 3-12 month) What Goes Down Goes Further Down The phenomenon is known as Momentum Much more in vogue in US & European markets Lower in Emerging Markets However, post 2003-2008 rally, its expected that momentum tests willshow positive correlation even in emerging markets now. Due to significant inflow of money For price Movement in Weeks Andrew Lo [1997] A non-random walk down wall street Significant Positive correlation Momentum has been part of Technical Analysis Literature for long Trend Relative Strength Moving Averages, MACD etc 14. Long-Term Correlation Mean Reversion For price Movement in Years (5 year returns) French & Fama [1988] Study Period [1945-1985] Significant Negative correlation (Reversion) Much More on 5 years basis than 1 year basis 25-40% change could be explained by past data [Andrew Lo] 15. January Effect-I Seasonal & Temporal Patterns The January Effect 16. January Effect-II Seasonal & Temporal Patterns The January Effect Contd.[1935-1986] [50% of alpha is generated in January] 17. January Effect-III Seasonal & Temporal Patterns The January Effect Contd.[1935-1986] [50% of alpha is generated in January] 18. Monday Effect-I Day of Week Effect 19. Monday Effect - II Monday Effect Some Nicities The Monday effect is really a weekend effect Bulk of the negative returns are manifested in the Friday close to Mondayopen returns. Stocks tend to open lower on Mondays The returns from intraday returns on Monday (the price changes from opento close on Monday) are not the culprits in creating the negative returns. The Monday effect is worse for small stocks than for larger stocks. Thismirrors findings on the January effect. The Monday effect is no worse following three-day weekends than two-dayweekends. Monday returns are more likely to be negative if the returns on the previousFriday were negative. In fact, Monday returns are, on average, positive following positive Fridayreturns Are negative 80% of the time following negative Friday returns. 20. Monday Effect III Monday Effect International 21. Monday Effect IV Monday Effect Holiday Contrast Cant be ascribed to negative news over the weekend 22. Volume Behaviour Volume Patterns [Lee & Swaminathan 1998] For Momentum effect documented by Jagadeesh & Titman More Pronounced for high volume stocksInsistence of technicianson High Volume BreakoutsprovenWinners do better with averagevolume as extreme volumesare usually sign of reversal(In Technical Analysis) 23. Behavioral Finance Recognizes that there is no Homo Economicus [Rational Man] Brings psychological studies to the field of finance Some key Themes Heuristics: People often make decisions based on approximate rules of thumb, not strictlogic Bounded Rationality Loss Aversion Heard Mentality Deviation from rationality Over-reaction Overconfidence Optism Extrapolation Loss Aversion Mental Accounting 24. Anomalies that can be explained byBehavioural Finance Momentum Based upon heard mentality Mean-reversion When Over-reaction peters out Loss Aversion Holding onto Losers Bubbles Heard Mentality 25. Reality of Markets [Own Thoughts] Markets are dynamic entity, equilibrium in the long run is a foreign concept I.e., relationship between Risk & Return is unstable Is guided by investor preferences and regulatory environment Like the environment of Low Interest Rates in 2001-2007 may have fuelled the mortgage bubble. The relationship between Risk & Reward is not as quantitative as EMH assumes For Example, even a large no of so called intelligent analysts could not gauge the risk in CDO Securities in the US Risk in Sectoral Funds In India Risk in Zero Cost Options In India Equity Risk premium is time and path dependent Arbitrage opportunities exist from time to time They disappear but only after long period of time : - Pair Trading, Bond Spreads, ValueArbitrage Bubbles, Cycles, Trends, Crashes, Manias all are part of market Even with passage of time, they wont be driven away as EMH assumed Investment activities will be able to generate super returns but not forever, innovationis the key One needs to adapt to the change in market inefficiencies, behaviour, trading environment etc. But said that for most of the investors, its difficult to generate super-normal returns As its not easy to identify and also act upon profitable opportunities before they are toocommon 26. EMH- Current Status Not the Singular God anymore Behavioural Finance especially post 2007-Crisisis gaining momentum Still hotly debated Opinions have shifted more to No than Yes Even Greenspan has confessed to congress thatthe market models he relied upon do not work Move towards assimilation With acceptance of low probability ofoutperformance 27. Appendix I - Concept of Random (Stochastic) processes inFinancial Literature [Adopted/Modified from Probability Theory] Random Walk As explained in RWH Independent Steps , i.e., successive changes are independent & Price changes confirm to an identical probability distribution Normal Distribution was assumed in 1953-1965 [named Heterogeneous RW RW] Implies, random behaviour, no relationship with supply/demand and is like a casino, toss, dice,etc. If RW is correct, then price of green pea and price of IBM will move similarly Random Walk With Drift Independent Steps but bias to a particular direction For example, in stock market prices dont go below zero, Thats a positive drift. MartingaleFor a given set of information:-Pt+1 = Pt +at Such that Mean of at is zero over long runThat means best forecast of price tomorrow is todays price if there is no change in informationAssumption is that that price changes on information concerning a security or market &nothing else Fair game [aka Martingale Difference] Zero Expected Gain from forecasting tomorrows price based upon todays information [ForExample 500 heads so far, Next can still be head or tail] That will mean Expected Return = Realized return [On probability basis in long term]or Expected Return Realized Return =0Implies Price will move randomly around its intrinsic Value 28. Appendix II - From Random Walk to Martingale Random Walk [1900; 1953-1963] Variable and its moments (Mean, Variance, Skewness, Kurtosis, etc.) also shall berandom Further information flow and expectations about market shall also be random Kendell (1953) found that though wheat price/mean was random, its variance hadincreased post WW I That means it was a time-dependent function and not a random thing Moved to heterogeneous RW, which is not RW in real sense of term The tests of RW, i.e., serial correlation & run tests were found to be deficient oncertain statistical parameters. Post 1980 RW/HRW models were finally rejected. MartingaleMandelbort (1963), Samuelson (1965); Assumes Risk Neutrality;Variable and only its mean not higher moments are random. Fair game Fama (1965) came up with Fair Game model of EMH where deviation fromexpected result is zero. He argued that Info. Flow and expectations may not bepurely random and that expected returns will not be stationery over time; hencerejected random walk which is a rigid theory than fair game and concluded that formarket efficiency fair game is a sound enough model, RW not needed. 29. Appendix III - Preference to Risk 30. Thanks! Contact:-www.phynedge.comcorporate@phynedge.com