Diversification Beta and the CAPM Diversification • We saw in the previous week that by combining stocks into portfolios we can create an asset with a better ris by mdn17717

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									Diversification, Beta and the
           CAPM
                    Diversification
• We saw in the previous week that by combining stocks
  into portfolios, we can create an asset with a better risk-
  return tradeoff (a higher Sharpe ratio).
• The reduction of risk in a portfolio occurs because of
  diversification. By combining different assets into a
  portfolio, we can diversify risk, reduce the overall
  volatility of the portfolio, as well as increase the Sharpe
  ratio.
• But what are the limits of diversification?
             Limits of Diversification:
             How Many Stocks?(1/4)
• There are two main factors that affect the extent to which
  volatility can be reduced: the number of assets in the
  portfolio, and the correlation between the assets.
   – Increasing the number of assets reduces the volatility of the
     portfolio.
   – Adding an asset with a low correlation with the existing assets of a
     portfolio also helps to reduce the volatility of the portfolio.
• Consider the following related questions: What is the
  lowest volatility that can be achieved as we increase the
  number of assets? How many assets does one typically
  need to diversify?
                          (2/4)
• Let us examine this by using our usual formula for
  portfolio volatility. For simplicity, that each of the
  N assets have the same volatility ( ), the same
  correlation ( ) with each other, and the same
  weight. Because all stocks have the same weight,
  the portfolio is equally weighted with w=1/N.
• The portfolio volatility can then be calculated by
  the usual formula. Substituting and simplifying,
  we get (why?):
   – Portfolio Variance = (1/N) 2 + (1-1/N)  2
Sample Spreadsheet
              Some Conclusions (1/2)
• By changing n=number of stocks in portfolio, and
  the correlation, we can examine how the portfolio
  volatility decreases.
• We can make the following observations:
• 1. For all positive correlations, there is a threshold beyond
  which we cannot reduce the portfolio volatility. This
  threshold depends on the magnitude of the correlation.
   – If the correlation is zero or less than zero, then it theoretically
     possible to bring down the portfolio volatility to zero.
   – If the correlation is positive, then we cannot decrease the portfolio
     volatility below: sqrt(  2) (…why?). This threshold represents
     the undiversifiable or the systematic risk of the portfolio.
              Some Conclusions (2/2)
• 2. As the correlation decreases, the more we can reduce the
  portfolio volatility. However, it takes more assets to bring
  down the portfolio volatility to its theoretical minimum.
   – Suppose the average correlation is 0.9, and the average volatility of
     each stock in the portfolio is 40%, then the lowest portfolio
     volatility that is possible is about 37.95%. We can reach within
     0.5% of this minimum volatility by creating a portfolio of only 4
     assets.
   – Suppose instead that the average correlation is 0.5. Then the
     lowest possible portfolio volatility is 28.28%; However, to reach
     within 0.5% of this value, we need as many as 30 stocks.
Beta and the CAPM
                 Road Map
• 1. What is the CAPM and the beta of an
  asset?
• 2. What can the beta be used for in portfolio
  management?
    Capital Asset Pricing Model: CAPM
                    (1/2)
• Recall from our earlier analysis, recall that, given the assets
  in the economy there is only one way to form an optimal
  portfolio.
• Under certain assumptions, the Capital Asset Pricing Model
  shows that this optimal risky portfolio (with the highest
  Sharpe ratio) must be the market portfolio. Think of the
  market portfolio as a portfolio of all assets in the economy.
• Strictly speaking, this market portfolio is unobservable.
  However, we shall proxy it by a broad index of stocks.
• Recall that we said that all optimal portfolio allocations are
  on the line connecting the riskfree rate to the optimal
  portfolio. Given our proxy for the market portfolio (say,
  S&P 500 or the Wilshire 5000), we may assume that all
  optimal portfolios are some combination of the riskfree
  asset and this index. We will call the graph of these
  portfolios as the capital market line.
    Capital Asset Pricing Model: CAPM
                    (2/2)
• From now onwards, we will assume that a large diversified
  market portfolio will proxy for our optimal portfolio, or the
  portfolio with the highest Sharpe ratio. We will use one of
  the standard indexes as our market portfolio.
• The usefulness of this analysis in the context of portfolio
  management is that it gives us a way of quantifying the
  risk of any individual asset. Thus, this allows us to
  determine both the risk premium that is required of
  individual assets (and, in principle, a mechanism for
  identifying mis-priced stocks), as well as for evaluating the
  performance of individual portfolio manager.
• We begin with a simple question: how do we measure the
  risk of a single stock? This allows us to answer the related
  question: what return do we require from that stock?
A Stock’s Risk in Relation to a Portfolio
• If we add a new stock to our portfolio, how much more
  risk do we add?
• For example: over the period Jan 2000 to Dec 2002, the
  volatility of KO and PEP were 30.237% and 28.072%,
  respectively. Thus, KO had a higher volatility than PEP.
  Does this mean that KO is riskier than PEP? Answer: not
  necessarily.
• The additional risk a stock adds to a portfolio depends on
  its covariance with the market portfolio. It does not depend
  on its volatility.
• By knowing the risk a stock adds to a portfolio, we can
  quantify it, using our usual formulae for portfolio returns
  and volatility.
The Market Portfolio and the Individual Stock
• Assume we know the Sharpe ratio of the market portfolio
  (say, a proxy like a stock index).
• We further assume that any investor can choose to invest in
  this portfolio, if they want to.
• Now suppose a new stock is issued in the market. How
  should it be priced so that investors are willing to buy the
  stock? To answer this question, we ask how much risk the
  stock adds to the market portfolio.
• Suppose that after adding the stock to the market portfolio,
  the Sharpe ratio decreases. If so, then nobody would be
  willing to invest in it.
• Investors should be willing to invest in the stock only if it
  does not decrease the Sharpe ratio. In other words, the stock
  will be priced such that, at the minimum, the Sharpe ratio of
  the market portfolio after adding the stock is the same as it
  was before the stock was issued.
   An Example of KO and the S&P 500
• Suppose we proxy our market portfolio by the S&P 500
  (SPX). Over the period, 1997-02, using monthly data, the
  annualized SPX volatility was 0.1905.
• In contrast, KO volatility is 0.3024 and PEP volatility is
  0.2807.
• Qt: is the volatility of KO and PEP the relevant measure of
  risk of KO and PEP relative to S&P 500?
• Answer: no. Because it doesn’t tell us how the Sharpe ratio
  of the market is affected when you add this stock to the
  portfolio.
• A less risky stock would be the stock that adds less risk
  when it is added to the portfolio; A more risky stock would
  be one that adds more risk.
  KO and Its Effect on the Sharpe Ratio
• How do we figure out the effect of KO on the market’s
  risk-variability ratio (Sharpe ratio)?
• We can ask ourselves how much the Sharpe ratio changes
  when we add a very small quantity (say, 1%) of KO to the
  market portfolio. This will enable us to generate a relation
  between the return we expect from KO and the return we
  expect from the market.
• We will compare a portfolio of 99.9% in S&P 500 and
  0.1% in KO to a portfolio of 100% in S&P 500.
           Adding KO to the Market
• The Sharpe ratio of a 100% investment in the S&P 500 is :
  (Rm-Rf)/(Vol of Mkt) where Rm is the required (or
  expected) return on the market and Rf is the riskfree rate.
  As we already mentioned, the volatility of the market is
  19.051%/year.
• How much risk does KO add to this portfolio? Let us add
  a small quantity of KO, and see what its effect is.
  Construct a portfolio of 0.1% in KO and 99.9% in S&P
  500, and measure its volatility (see spreadsheet). The
  volatility of this portfolio is 19.04%.
• Therefore, the Sharpe ratio of a 99.9% investment in the
  S&P 500 and a 0.1% investment in KO is: (0.99rm + 0.01
  R(KO) - Rf)/19.04, where R(KO) is the required return on
  KO.
                   “Beta” of KO.
• In equilibrium, all investors should want the highest
  possible Sharpe ratio, so they will demand the same Sharpe
  ratio from the 0.1% KO+ 99.9% SP500 portfolio as they
  achieve in the 100% SP500 portfolio. Thus,
• (Rm-Rf)/(0.19051) = (0.999 Rm + 0.001 R(KO) -
  Rf)/(0.1904).
• This algebraic equation now represents a relation between
  the return of the market (Rm) and the return of KO
  [R(KO)].
• With some algebraic manipulation, we get:
• R(KO) = Rf + 0.4123 (Rm - Rf).
• Thus, the excess return (R-Rf) that investors require from
  KO is 0.4123 times that required from the market
  portfolio.
• The number 0.4123 is called the beta of the KO, and it is
  the relevant measure of risk for KO.
             Estimating Beta: Two ways
• 1. We can estimate the beta by asking how much the
  volatility of the portfolio changes when we add a small
  quantity of the stock to the portfolio, and then working out
  the equations to ensure that the Sharpe ratio before and
  after remains the same. To get an accurate answer, we have
  to add an infinitesimal amount of the new stock (so the
  weight of the stock has to very small). If we assume the
  weight of KO is 0.0001, then we get a more precise
  estimate of the beta as 0.4111.
• 2. We can estimate the beta by a linear regression. This is
  the preferred approach, as it also gives us all the statistics
  that are of interest.
• Ideally, we should regress (R-Rf) of the stock on (Rm-Rf) of the
  market. Thus, (R-Rf) is the LHS variable or the Y-variable. And (Rm-
  Rf) is the RHS or the X-variable. If the riskfree rate is constant, then
  we can also estimate the beta by a regression of R on Rm.
   The Beta and Portfolio Management
• There are at least three uses of the beta in portfolio
  management:
• 1. As we have already seen, the beta is a measure of
  the risk the stock adds to the market portfolio.
  Thus, it can be used to value a stock.
• 2. The beta can be used to approximate the
  correlation between two stocks, and thus it is useful
  for creating the frontier.
     If beta of the regression of KO return on S&P 500 return is 0.64,
     then the correlation between KO and S&P500 is (beta)(vol of
     S&P500)/(vol of KO)=0.64*0.317/0.179=0.36.
• 3. The beta is useful for performance evaluation.
        Using the Beta to Estimate the
              Correlation (1/2)
• If we assume that: R = rf + beta(Rm - Rf) + e, where the
  market return is the only common factor amongst stock
  returns (so that the error “e” represents only idiosyncratic
  risk), then:
• Correlation = (beta1 beta2)(variance of rm)/[(vol of r 1)(vol
  of r2)].
• For example, suppose the beta of KO is 0.4111, the beta of
  PEP is 0.7213 and the volatilities of the market, KO and
  PEP are 19.05%, 30.237%, 28.07%, respectively. Then the
  estimate of the correlation is
  (0.4111)(0.7213)(0.1905*0.1905)/[0.30237*0.2807]=0.126
  8.
    Limitation of the Use of the Beta to
       Calculate Correlation (2/2)
• The use of the beta for calculating the correlation
  implicitly assumes that the KO and PEP are correlated only
  because they are each, individually, correlated with the
  market. The market is the only common factor that affects
  both KO and PEP returns.
• However, KO and PEP most likely have additional
  common factors. Thus, if you use the beta to estimate the
  correlation, then you will underestimate the real correlation
  between the two stocks. For example, the real correlation
  between KO and PEP is 0.55, much higher than 0.17.
• The underestimate of the correlation suggests that there are
  other common factors between KO and PEP, besides the
  market. This is not surprising as both KO and PEP belong
  to the same industry, and they are likely to have many
  common factors.
The Alpha and the Beta for Performance
           Evaluation(1/2)
• The regression model for calculating the beta is useful as it
  also provides a means of performance evaluation. If we
  regress (R-Rf) on (Rm-Rf), then the intercept of the
  regression, or the “alpha”, provides an estimate of the
  amount by which the stock or a portfolio has beaten the
  market after adjusting for the beta risk.
• In other words: R-Rf = alpha + beta (Rm-Rf).
• A positive alpha means that the portfolio has outperformed
  the market. A negative alpha means that it has lagged
  behind the market.
• If we regress R on Rm, then the intercept will not directly estimate the
  alpha. However, we can easily calculate it by noting that: R = alpha +
  Rf - beta Rf + beta Rm so that the intercept of the regression will be
  equal to [alpha + Rf(1 - beta)]. So alpha = intercept - Rf(1-beta).
The Alpha and the Beta for Performance
           Evaluation(2/2)
• Regressing of R-Rf on Rm-Rf for PEP, the intercept
  0.005631 on a monthly basis, or about 7% on an
  annualized basis. The riskfree rate was about 5% on an
  annualized basis over this period. The estimated beta is
  0.7213.
• The positive alpha for KO indicates that KO
  underperformed the market by that magnitude on an annual
  basis over the last 5 years.
   – Statistically, is this significant? The t-statistic for the intercept is -
     0.60 so that, statistically, we cannot say with any confidence that
     the alpha is different from 0. In fact, we shall see that the main
     problem with using the alpha as a means of performance
     evaluation is that it is very difficult to verify whether the alpha’s
     are really different from 0.
                     Summary
• 1. The risk that the a stock adds to a portfolio is
  related to its beta (or covariance with the
  portfolo), and not its total volatility.
• 2. The beta can be calculated by a linear
  regression of the stock’s return on the market’s
  return.
• 3. The beta is useful for estimating the required
  return on a stock, and as a means of performance
  analysis.

								
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