Efficient capital markets. Skema Business School. Portfolio Management 1. Course Outline

Transcription

1 Efficient capital markets Skema Business School Portfolio Management 1 Course Outline Introduction (lecture 1) Presentation of portfolio management Chap.2,3,5 Introduction to Bloomberg Modern Portfolio Theory (lectures 2-4) The risk return framework Chap.1 Efficient capital markets Chap.6 The price of risk Chap.7,8 Asset pricing models Chap.9 Fundamental Analysis (lectures 5-8) Analysis of financial statement Chap.10 Industry analysis Chap.12,13 Absolute and relative valuation analysis Chap.11, 14 Stock market valuation analysis Chap.12 Technical analysis (lecture 9) Chap.15 The asset management industry (lecture 10) Portfolio management strategies Chap.16 The different types of investment companies Chap.24 Evaluation of portfolio performance Chap.25 NB: chapters refer to Reilly & Brown 8th and 9th ed. Portfolio Management 2 1

2 Efficient capital markets Course Outline Defining efficiency Random walk Mean-variance paradigm Portfolio Management 3 Efficient market hypothesis (EMH) Informational efficiency in term of information An efficient capital market is one in which asset prices adjust instantaneously to the arrival of new information Rational efficiency in term of rational behavior A capital market is efficient if asset prices reflect its expected incomes. Portfolio Management 4 2

3 Informational efficiency Earning announcements: positive/negative surprises Portfolio Management 5 Earning surprise (Bloomberg SURP) Portfolio Management 6 3

4 Informational Efficiency and the premise of an efficient market A large number of competing profit-maximizing participants analyze and value securities, each independently of the others New information regarding securities comes to the market in a random fashion Profit-maximizing investors adjust security prices rapidly to reflect the effect of new information Portfolio Management 7 Nature of information and form of efficiency (Fama [1970]) Weak-Form EMH prices reflect all security-market information Semistrong-form EMH prices reflect all public information Strong-form EMH prices reflect all public and private information Portfolio Management 8 4

5 Tests of the EMH Weak-Form EMH: Mixed results Statistical Tests of Independence : Autocorrelation tests Trading Rules Example Let s build a moving average (MA) trading rule using Bloomberg backtesting command BTST and choosing MACD trading rule Portfolio Management 9 Trading rule Bloomberg backtesting: BTST on MACD rule Portfolio Management 10 5

6 Trading rule Bloomberg backtesting: BTST on MACD rule MACD for AAPL US Equity Entry Date Entry Price Exit Date Exit Price Position Shares Trade P&L Cumulative P&L 01/02/2013 $ 553,82 01/15/2013 $ 490,50 Long /10/2012 $ 525,00 01/02/2013 $ 553,82 Short /21/2012 $ 564,25 12/10/2012 $ 525,00 Long /25/2012 $ 688,26 11/21/2012 $ 564,25 Short /18/2012 $ 699,88 09/25/2012 $ 688,26 Long /06/2012 $ 673,17 09/18/2012 $ 699,88 Short /06/2012 $ 617,29 09/06/2012 $ 673,17 Long /24/2012 $ 607,38 08/06/2012 $ 617,29 Short /03/2012 $ 594,88 07/24/2012 $ 607,38 Long /27/2012 $ 575,00 07/03/2012 $ 594,88 Short /24/2012 $ 575,87 06/27/2012 $ 575,00 Long /30/2012 $ 608,77 05/24/2012 $ 575,87 Short /14/2012 $ 578,05 03/30/2012 $ 608,77 Long /07/2012 $ 536,80 03/14/2012 $ 578,05 Short /26/2012 $ 448,36 03/07/2012 $ 536,80 Long /25/2012 $ 454,44 01/26/2012 $ 448,36 Short Portfolio Management 11 Tests of the EMH Semi-strong-form EMH : mixed results Try to find abnormal return Return Prediction Studies of economics variables inconsistent with the EMH Dividend yield (D/P) Announcement of unanticipated results: earning surprise (see previous slide 5) The PER effect, the book-to-market value effect, the size effect, the January anomaly, the week-end effect Event studies consistent with the EMH investigate the immediate reaction of stock prices after a public announcement Stock split, IPOs Portfolio Management 12 6

7 Tests of the EMH Strong-form EMH: Mixed results Access to private information? The "insiders trading" have higher profits "Value Line studies: no profits after transactions costs Individual analyst recommendations seem to contain significant information Performance of professional money managers seem to provide support for strong-form EMH» over long-period most funds did not match the performance of a buy-and-hold strategy Portfolio Management 13 Implications of EMH If the market is efficient (Weak-form, semistrong form, strong-form) Adopt a passive management If the market is weak-form efficient Use of technical analyses is not possible Use only publicly available data If the market is not weak-form efficient Use of technical analyses is possible Use publicly available data Portfolio Management 14 7

8 Results generally support the weak-form EMH, but results are not unanimous Is it possible to outperform the market? Even after transaction costs? What about Warren Buffet? Portfolio Management 15 Random Walk EMH implications : the random walk P t = P t-1 + e t e t is a random variable Exercise : let s build a random walk with Excel use the excel function =RAND() ALEA() for French Excel Portfolio Management 16 8

9 Random Walk Bachelier [1900], Osborne [1959] «If the market, in effect, does not predict its fluctuations, it does assess them as being more or less likely, and this likelihood can be evaluated mathematically» => focus on the probability distribution Price changes (P t P t-1 ) or price returns (P t /P t-1 ) result from the sum of many small random variations due to the arrival of new information Portfolio Management 17 Random Walk The price changes result from the sum of small hazards Example APPLE tick by tick price changes : Portfolio Management 18 9

10 Bachelier [1900] Use of the central limit theorem : the sum of a sufficiently large number of independent identically distributed random variables each with finite mean and variance will be approximately normally distributed If the e t are independent identically distributed random variables, then the EMH implies that the price changes follow a normal distribution Portfolio Management 19 Example : SP500 returns distribution SP , , , , , , , , , , , , , , , , , , , ,12 Portfolio Management 20 10

11 Frequency Example : SP500 returns distribution SP500 monthly return 15,0% 10,0% 5,0% 0,0% -5,0% -10,0% -15,0% -20,0% -25,0% Portfolio Management 21 Example : SP500 returns distribution Returns distribution 0,3 0,25 0,2 SP500 Normal Law 0,15 0,1 0,05 0-9,3% -6,8% -4,3% -1,8% 0,7% 3,2% 5,7% 8,2% 10,7% Return Portfolio Management 22 11

12 SP500 return distribution 2004 to 2007 Portfolio Management 23 SP500 return distribution 2009 to 2012 Portfolio Management 24 12

13 Two parameters describe the probability distribution of returns which follow a normal distribution : The mean m (the expected return) The variance s² (the risk) Log[P(t+dt)] - Log[P(t)] ~ N(mdt,sdt) Osborne [1959] The asset returns probability distribution is completely characterized by the mean and the variance Portfolio Management 25 Homeworks Trading rule Pick a stock and apply the MACD trading rule (command BTST and then choose MACD) Modify the trading rule parameter (edit selected factors: period 1, 2 and signal) and find one set of paremeter that generates a positive return and anoter set of parameters that generates a negative return Return probability distribution Using your company and Bloomberg HRH, plot the distribution of your company s return (weekly over the past 10 years). Give the average annual return and the volatility. Is it normally distributed? Past 1 year daily Past 5 years weekly Past 30 years monthly (if available or 10 years monthly) Portfolio Management 26 13