Saturday, August 31

Portfolio Optimization II : Black-Litterman model

In the previous post, we have been discussing conventional approach to the portfolio optimization, where assets' expected returns, variances and covariances were estimated from historical data. Since these parameters affect optimal portfolio allocation, it is important to get their estimates right. This article illustrates how to achieve this goal using Black-Litterman model and the technique of reverse optimization. All examples in this post are build around the case study implemented in Python.

Instability of asset returns

Empirical studies show that expected asset returns explain vast majority of optimal portfolio weightings. However, extrapolation of past returns into the future doesn't work well due to a stochastic nature of financial markets. Nevertheless, our goal is to achieve optimal and stable asset allocation, rather than trying to predict future market returns.

The world of efficient markets

To maximize investor's utility function, Modern Portfolio Theory suggests holding the combination of a risk-free asset and optimal risky portfolio (ORP) lying on a tangency of the efficient frontier and capital market line. Modern Portfolio Theory also suggests that optimal risky portfolio is a market portfolio (e.g. capitalization-weighted index).

If we assume markets are fully efficient and all assets are fairly priced, we don't have any reason to deviate from the market portfolio in our asset allocation. In such case, we don't even need to know equilibrium asset returns nor perform any kind of portfolio optimization, as outlined in the previous article. An optimization based on equilibrium asset returns would lead back to the same market portfolio anyway.

An active investor's view

The different situation is when investor believes the market as a whole is efficient, but has concerns about the performance of specific assets or asset classes due to the possession of material non-public information, resulting for example from superior fundamental analysis.

If investor with distinct views on a performance of few specific securities doesn't want to completely abolish the idea of mean-variance optimization, he may still use quantitative approach to incorporate such view into the MVO framework, further referred to as the Black-Litterman model.

Black-Litterman model

In the previous article, we have been discussing classic Mean-Variance optimization process. The process solves for asset weights which maximize risk-return trade-off of the ORP portfolio, given historical asset returns and covariances:



On the other hand, the general workflow of the Black-Litterman optimization is illustrated on the diagram below.


The forward-optimization part in this model is the same as in the classic MVO process (boxes bgijkl).
However, the thing which differs is a way how we are observing expected asset returns, which is one of the inputs into the forward optimizer (g). Previously we have used historical asset returns as a proxy.

Now, as we can see in the diagram, equilibrium asset returns are used instead (d or g).
Equilibrium asset returns (d) are returns implied from the market capitalization weights of individual assets or asset classes (a) and historical asset covariances (b) in a process known as reverse optimization (c).

Forward (h) and reverse (c) optimizations are mutually inverse functions. Therefore, forward-optimizing equilibrium asset returns (d) would yield to the optimal risky portfolio (j) with exactly the same weights as the weights observed from assets' capitalization (a).

However, the beauty of the Black-Litterman model comes with the ability to adjust equilibrium market returns (f) by incorporating views into it and therefore to get optimal risky portfolio (j) reflecting those views. This ORP portfolio may be therefore different to the initial market cap weights (a).

Case Study

We will build upon the case study introduced in the previously published post Mean-Variance portfolio optimization. In that post, we have used forward optimizer to calculate weights of an optimal risky portfolio \(\mathbf(W_{n \times 1})\) given historical returns \(\mathbf(R_{n \times 1})\) and covariances \(\mathbf(C_{n \times n})\)  Now, we will use the same case study to illustrate the principle of the Black-Litterman model.

Before doing so, let us define some basic symbols:

\(\mathbf{R}\)Vector of historical asset returns
\(\pi\)Vector of implied equilibrium returns
\({\pi}'\)Vector of view-adjusted implied equilibrium returns
\(\mathbf{C}\)Matrix of historical return covariances
\(\mathbf{W}\)Vector of market capitalization weights
\(\mathbf{W}'\)Vector of optimal risky portfolio weights
\(\lambda\)Risk-return trade-off
\(\mathbf{Q}\)Vector of views about asset returns
\(\mathbf{P}\)Matrix linking views to the portfolio assets

Reverse optimization of equilibrium returns

Equilibrium market returns are those implied by the capitalization weights of market constituents. They represent more reasonable estimates than those derived from historical performance. The another advantage is that equilibrium returns (whether or not adjusted) will yield into the portfolio weights similar than weights of the capitalization weighted index, thus representing more intuitive and investable portfolios. 

Equilibrium excess returns \(\pi\) are derived in Black-Litterman using this formula:

$$ \lambda = \frac{r_p - r_f}{\sigma_p^2} $$
$$ \pi=\lambda \cdot \mathbf{C} \cdot \mathbf{W} $$

In our example, corresponding code is represented by the following snippet. Please note that excess returns don't include risk free rate.

# Calculate portfolio historical return and variance mean, var = port_mean_var(W, R, C) # Black-litterman reverse optimization lmb = (mean - rf) / var # Calculate return/risk trade-off Pi = dot(dot(lmb, C), W) # Calculate equilibrium excess returns

As we can see, equilibrium returns implied from capitalization weights represent more conservative estimates than those calculated from historical performance. For example, implied return on Google \(\pi_6\) is "only" 16.5% given its capitalization weight of 11.6%, as opposed to unjustified historical performance \(\mathbf{R}_6\) of 33.2%.


Name\(\mathbf{W}\) \(\pi\) \(\mathbf{R}\)
XOM 16.0% 15.6% 7.3%
AAPL15.6% 17.9% 13.0%
MSFT11.2% 15.2% 17.2%
JNJ 9.7% 9.9% 14.4%
GE 9.4% 17.9% 14.0%
GOOG11.6% 16.5% 33.2%
CVX 9.2% 17.3% 9.2%
PG 8.5% 9.4% 11.2%
WFC 8.7% 21.8% 26.9%


Efficient frontier based on historical returns is charted in red, while implied returns are in green:

Incorporating custom views to equilibrium returns

As we have already mentioned, the whole idea behind the reverse and forward optimization in Black-Litterman model is a process of incorporating custom views to the returns. The good news is that the whole process can be expressed as mathematical formula, while the bad news is that the steps to derive this formula are beyond the scope of this article.

Given the views and links matrices \(\mathbf{Q}\) and \(\mathbf{P}\), we calculate view-adjusted excess returns \({\pi}'\) as follows:

$$ \tau = 0.025 $$ $$ \omega = \tau \cdot \mathbf{P} \cdot \mathbf{C} \cdot \mathbf{P}^T $$ $$ {\pi}' = [(\tau \mathbf{C})^{-1} + \mathbf{P}^T \omega^{-1} \mathbf{P}]^{-1}  \cdot [(\tau \mathbf{C})^{-1} \pi + \mathbf{P}^T \omega^{-1} \mathbf{Q})] $$
, where \(\tau\) is scaling factor, \(\omega\) is uncertainty matrix and \({\pi}'\) is vector of view-adjusted equilibrium excess returns.

Please note that rather than specifying uncertainty matrix of views \(\mathbf{P}\) explicitly by the investor, we derive it from the formula above. It's because we believe that volatility of assets represented by covariance matrix \(\mathbf{C}\) is affecting certainty of both returns and views in a similar way.

Going back to our example, we have the vector of nine excess returns \(\pi\), to which we want to apply our views. Let's our views to be as below:
  • Microsoft (MSFT) will outperform General Electric (GE) by 2%
  • Apple (AAPL) will under-perform Johnson & Johnson (JNJ) by 2%
Of course these views are created randomly for the sake of our example, but in a real-world scenario they may be the result of some kind of observation based on statistical arbitrage. These two views will be mapped to a portfolio of nine assets. Therefore, the corresponding views and link matrices will have the following dimensions and content:

$$ \mathbf{Q}_{1 \times 2} = \begin{pmatrix}0.02 & 0.02\end{pmatrix} $$ $$ \mathbf{P}_{2 \times 9} = \begin{pmatrix}0 & 0 & 1 & 0 &-1 & 0 & 0 & 0 & 0\\ 0 &-1 & 0 & 1 & 0 & 0 & 0 & 0 & 0\end{pmatrix} $$

, where the first row in a link matrix \(\mathbf{P}\) has a positive sign +1 for MSFT and negative -1 for GE.

By applying these views to an original vector of excess equilibrium returns \(\pi\), we will get the view-adjusted vector \({\pi}'\), later used to forward-optimize optimal risky portfolio weights \(\mathbf{W}'\):

Name\(\mathbf{W}\) \({\pi}\) \({\pi}'\) \(\mathbf{W}'\)
XOM 16.0% 15.6% 14.9% 15.3%
AAPL15.6% 17.9% 13.1% 3.3%
MSFT11.2% 15.2% 15.7% 22.6%
JNJ 9.7% 9.9% 10.1% 21.9%
GE 9.4% 17.9% 16.1% -0.0%
GOOG11.6% 16.5% 15.2% 11.6%
CVX 9.2% 17.3% 16.4% 8.8%
PG 8.5% 9.4% 9.2% 8.4%
WFC 8.7% 21.8% 20.5% 8.2%

These views are incorporated into equilibrium returns \(\pi\) also with respect to the uncertainty matrix \(\omega\). Therefore, for example, the stated view that MSFT will outperform GE by 2% is not fully incorporated into a difference in their implied returns, which is just 16.1% - 15.7% = 0.4%.

Forward-optimization of view-adjusted returns

Once the view-adjusted equilibrium returns are ready, the final step is forward mean-variance optimization of the market weights \(\mathbf{W}'\) with respect to these returns \({\pi}'\) and historical covariances \(\mathbf{C}\), pretty much as described in the previous post: Mean-Variance Portfolio Optimization

Graphically, the efficient frontier constructed using equilibrium returns before and after incorporating views looks as follows.
Conclusion

Black-Litterman model is based on an assumption that asset returns have greatest impact to portfolio weightings in mean-variance optimization. It is therefore attempting to reverse-engineer these returns from index constituents rather than relying on historical data. This results into several advantages including but not limited to:
  • Intuitive portfolios with weightings not too much different from benchmark indices.
  • Ability to incorporate custom views
  • Lower tracking error and reliance on historical data
  • Greater stability of efficient frontier and better diversification

Saturday, July 20

Mean-Variance Portfolio Optimization

This article introduces readers to the mean-variance optimization of asset portfolios. The underlying formulas are implemented in Python together with API for downloading market data from the Google Finance. Both case study and generic library versions are available.

Calculation of assets' weights, returns and covariances

Our example starts with the calculation of vector of asset weights \(\mathbf{W}_{n \times 1}\), expected annual returns \(\mathbf{R}_{n \times 1}\) and covariances \(\mathbf{C}_{n \times n}\). Top nine S&P companies sorted by market capitalization are used as asset classes for the purpose of this article: XOM, AAPL, MSFT,  JNJ, GE, GOOG, CVX, PG and WFC, as of 1 Jul 2013.

The time frame of historical data should depend on planned investment horizon. For the purpose of our analysis, we will consider daily prices for recent two years. The function call from previously mentioned example implementing the loader is as follows:

# Load names, prices, capitalizations from yahoo finance
names, prices, caps = load_data()
# Estimate assets's expected return and covariances
names, W, R, C = assets_meanvar(names, prices, caps) rf = .015 # Define Risk-free rate

The data downloaded in this example will yield to the historical returns, deviations and capitalization-based weights as shown below. In the next section, we will use these figures to calculate portfolio risk/return characteristics and to optimize its asset weights.

Name       Weight Return    Dev
XOM         16.0%   7.3%  19.8%
AAPL        15.6%  13.0%  30.3%
MSFT        11.3%  17.2%  22.6%
JNJ          9.7%  14.4%  13.9%
GE           9.4%  14.0%  24.3%
GOOG        11.6%  33.2%  25.9%
CVX          9.2%   9.2%  22.6%
PG           8.5%  11.2%  15.9%
WFC          8.7%  26.9%  30.0%

Mean-variance trade-off and diversification benefit

In the investment management process, almost every risky asset class exhibits some degree of trade-off between the returns and uncertainty of these returns. Mean returns are quantitatively measured by geometrically averaging series of daily or weekly returns over a certain period of time, while the uncertainty is expressed by the variance or standard deviation of such series.

Naturally more volatile the asset is, higher is also the expected return. This relationship is determined by the supply and demand forces on a capital market and may be mathematically expressed by one of the capital pricing models, such as APT, CAPM or others.

Most of these pricing models are based on an assumption that only systematic risk is reflected in capital prices and therefore investors shouldn't expect additional risk premium by holding poorly diversified or standalone investments.

Assuming normal distribution of asset returns, we can quantitatively measure this diversification benefit by calculating correlation between two price series, which is equal or less than one.

Let's consider a simple example of portfolio containing two assets, 50% of asset A and 50% of asset B, where asset returns are \(r_{A}=4\%\), \(r_{B} = 5\%\) and variances are \(\sigma_{A}^{2}=7\%^{2}\) and \(\sigma_{B}^{2}=8\%^{2}\).

In case there is no diversification benefit, portfolio expected return and variance will be just linear combination of the asset weights, in this case \(r_{p}=4.5\%\), \(\sigma_{p}^{2}=7.5\%^{2}\).

However, in case the correlation between these two assets is less than one and therefore diversification potential is available, portfolio variance will be less than linear combination of weights: \(\sigma_{p}^{2}<7.5\%^{2}\). The mean-variance trade-offs for different levels of diversification are shown on the Figure 1.

Diversification benefit based on the correlation between assets.

Portfolio mean-variance calculation

The generalized formula for calculating portfolio return \(r_{p}\) and variance \(\sigma_{p}\) consisting of \(n\) different assets is then defined as:
 $$ r_{p} = \mathbf{W} \cdot \mathbf{R} $$
, where \(\mathbf{W}_{n\times 1}\) is the vector of asset weights and \(\mathbf{R}_{n\times 1}\) vector of corresponding asset returns.

Portfolio variance is then defined as:
$$ \sigma_{p}^{2} = \mathbf{W} \cdot \mathbf{C} \cdot \mathbf{W} $$
, where \(\mathbf{C}_{n\times n}\) is the covariance matrix of asset returns.

The corresponding code in our python example:

# Calculate portfolio historical return and variance mean, var = port_mean_var(W, R, C)

Portfolio Optimization 

Considering the starting vector of weights \(\mathbf(W_{n \times 1})\), the optimization process is tailored towards maximizing some kind of mean-variance utility function, such as Sharpe ratio:
$$ s=\frac{r_{p} - r_{f} }{\sigma_{p}} $$
, because we know that optimal risky portfolio with highest sharpe ratio \(s\) lies on a tangency of efficient frontier with the capital market line (CML).

Given the historical asset returns \(\mathbf{R}_{n \times 1}\) and covariances \(\mathbf{C}_{n \times n}\), the optimization code using SciPy's SLSQP method may look for example as follows:

# Given risk-free rate, assets returns and covariances, this function calculates # weights of tangency portfolio with respect to sharpe ratio maximization def solve_weights(R, C, rf): def fitness(W, R, C, rf):
# calculate mean/variance of the portfolio mean, var = port_mean_var(W, R, C) util = (mean - rf) / sqrt(var) # utility = Sharpe ratio return 1/util # maximize the utility n = len(R) W = ones([n])/n # start with equal weights b_ = [(0.,1.) for i in range(n)] # weights between 0%..100%. 
# No leverage, no shorting c_ = ({'type':'eq', 'fun': lambda W: sum(W)-1. }) # Sum of weights = 100% optimized = scipy.optimize.minimize(fitness, W, (R, C, rf), 
method='SLSQP', constraints=c_, bounds=b_)
if not optimized.success: raise BaseException(optimized.message) return optimized.x # Return optimized weights

The function returns vector of optimized weights, which together with original vector of returns and deviations looks as follows:

# optimize W = solve_weights(R, C, rf) mean, var = port_mean_var(W, R, C) # calculate tangency portfolio front_mean, front_var = solve_frontier(R, C, rf) # calculate min-var frontier

We can see that this optimization results in a concentrated portfolio consisting just of two constituents: Johnson&Johnson and Google:

Name       Weight Return    Dev
XOM          0.0%   7.3%  19.8%
AAPL        -0.0%  13.0%  30.3%
MSFT        -0.0%  17.2%  22.6%
JNJ         45.1%  14.4%  13.9%
GE           0.0%  14.0%  24.3%
GOOG        52.8%  33.2%  25.9%
CVX          0.0%   9.2%  22.6%
PG          -0.0%  11.2%  15.9%
WFC          2.1%  26.9%  30.0%

It is not surprising that these assets have one of the lowest correlations among all pairs (0.431). Companies Apple and Exxon were not selected despite the fact that they have even lower mutual correlation. This is because their stand-alone risk/return trade-off is not that great indeed.

Correlation between assets
Calculation of minimum variance frontier

In the previous section, we have used optimization technique to find the best combination of weights in order to maximize the risk/return profile (Sharpe ratio) of the portfolio. This resulted into a single optimal risky portfolio represented by a single point in the mean-variance graph. Although the utility function is clear, to maximize the Sharpe ratio, it has two degrees of freedom - the mean and the variance.

In order to calculate the minimum variance frontier, we need to iterate through all levels of investor's risk aversity, represented by required return (y-axis) and use the optimization algorithm in order to minimize the portfolio variance. In each iteration, the required return is hold constant for the purpose of optimization, reducing the problem to one degree of freedom. The output of such optimization will be a vector of minimum variances, one value for each level of risk aversity, also called the minimum variance frontier.

The corresponding Python function may look for example as follows:

# Given risk-free rate, assets returns and covariances, this function calculates # min-var frontier and returns its [x,y] points in two arrays def solve_frontier(R, C, rf): def fitness(W, R, C, r): # For given level of return r, find weights which minimizes # portfolio variance. mean, var = port_mean_var(W, R, C)
# Big penalty for not meeting stated portfolio return 
# effectively serves as optimization constraint penalty = 100*abs(mean-r) return var + penalty frontier_mean, frontier_var = [], [] n = len(R) # Number of assets in the portfolio
# Iterate through the range of returns on Y axis for r in linspace(min(R), max(R), num=20): W = ones([n])/n # start optimization with equal weights b_ = [(0,1) for i in range(n)] c_ = ({'type':'eq', 'fun': lambda W: sum(W)-1. }) optimized = scipy.optimize.minimize(fitness, W, (R, C, r), 
method='SLSQP', constraints=c_, bounds=b_) if not optimized.success: raise BaseException(optimized.message) # add point to the min-var frontier [x,y] = [optimized.x, r] frontier_mean.append(r) frontier_var.append(port_var(optimized.x, C)) return array(frontier_mean), array(frontier_var)

Again, we have chosen Sequential Least Square Programming method (SLSQP), which allows us to perform the constrained optimization based on the following constraints:

  • Sum of weights must be 0
  • Weights must be in range (0,1) - no short-selling or leveraged holdings.
  • Portfolio required return is given
The first constraint is provided using the lambda function lambda W: sum(W)-1., which is equality constraint saying that the whole expression must be as close to zero as possible. The second constraint are effectively search bounds passed to the optimization function and third constraint is implemented in a fitness function itself. It imposes a big penalty for portfolio mean return not meeting the target return "r".

After solving minimum variance portfolios for all twenty levels of risk aversity, we will get the following minimum variance frontier with optimal risky portfolio represented by dot (o) and individual constituents represented by cross (x):
Minimum Variance Frontier
Conclusion

The major drawback of mean-variance optimization based on historical returns is that such optimization leads to undiversified portfolios, as seen in our example. The reason behind this observation is that market prices are often far from their equilibriums on a risk-adjusted basis, as modeled by the Capital Market Pricing Model.
Moreover, extrapolated historical returns are notoriously bad proxies to be used instead of expected mean returns, which are direct inputs into the mean-variance optimization algorithm.

In the next post, we will illustrate how to use Black-Litterman model to overcome this problem by deriving returns from the market capitalization weights in the process known as reverse optimization.