Load Data

Load a simulated data set, which includes asset returns total p = 100 assets and 2000 daily observations.

clear;
load('asset_return_100_simulated.mat');
[nObservation, p] = size(stockReturns)

nObservation = 2000

p = 100

splitPoint = ceil(nObservation*0.6);
training = 1:splitPoint;
test = splitPoint+1:nObservation;
trainingNum = numel(training);

Visualize the equity curve for each stock. For this example, plot the first five stocks.

plot(ret2tick(stockReturns{training,1:5}, 'method', 'simple')*100); hold off;
xlabel('Timestep');
ylabel('Value');
title('Equity Curve');
legend(stockReturns.Properties.VariableNames(1:5), 'Location',"bestoutside",'Interpreter', 'none');

Optimize the Asset Allocation Directly Against Factors with a Problem-Based Definition Framework

For the factors, you can use statistical factors extracted from the asset return series. In this example, you use principal component analysis (PCA) to extract statistical factors [1]. You can then use this factor model to solve the portfolio optimization problem.

With a factor model, p asset returns can be expressed as a linear combination of k factor returns, ${\mathit{r}}_{\mathit{a}}={\mu }_{\mathit{a}}+\mathit{F}{\mathit{r}}_{\mathit{f}}+{\epsilon }_{\mathit{a}}$, where k<<p. In the mean-variance framework, portfolio risk is

$\mathrm{Var}\left({\mathit{R}}_{\mathit{p}}\right)=\mathrm{Var}\left({{\mathit{r}}_{\mathit{a}}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}\right)=\mathrm{Var}\left({\left({\mu }_{\mathit{a}}+\mathit{F}{\mathit{r}}_{\mathit{f}}+{\epsilon }_{\mathit{a}}\right)}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}\right)={\mathit{w}}_{\mathit{a}}^{\mathit{T}}\left(\mathit{F}{\Sigma }_{\mathit{f}}{\mathit{F}}^{\mathit{T}}+\mathit{D}\right){\mathit{w}}_{\mathit{a}}={\mathit{w}}_{\mathit{f}}^{\mathit{T}}{\Sigma }_{\mathit{f}}{\mathit{w}}_{\mathit{f}}+{\mathit{w}}_{\mathit{a}}^{\mathit{T}}\mathit{D}{\mathit{w}}_{\mathit{a}}$, with ${\mathit{w}}_{\mathit{f}}={\mathit{F}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}$,

where:

${\mathit{R}}_{\mathit{p}}$ is the portfolio return (a scalar).

${\mathit{r}}_{\mathit{a}}$ is the asset return.

${\mu }_{\mathit{a}}$ is the mean of asset return.

$\mathit{F}$ is the factor loading, with the dimensions p-by-k.

${\mathit{r}}_{\mathit{f}}$ is the factor return.

${\epsilon }_{\mathit{a}}$ is the idiosyncratic return related to each asset.

${\mathit{w}}_{\mathit{a}}$ is the asset weight.

${\mathit{w}}_{\mathit{f}}$ is the factor weight.

${\Sigma }_{\mathit{f}}$ is the covariance of factor returns.

$\mathit{D}$ is the variance of idiosyncratic returns.

The parameters ${\mathit{r}}_{\mathit{a}}$, ${\mathit{w}}_{\mathit{a}}$, ${\mu }_{\mathit{a}}$, ${\epsilon }_{\mathit{a}}$are p-by-1 column vectors, ${\mathit{r}}_{\mathit{f}}$and ${\mathit{w}}_{\mathit{f}}$ are k-by-1 column vectors, ${\Sigma }_{\mathit{a}}$ is a p-by-p matrix, ${\Sigma }_{\mathit{k}}$ is a k-by-k matrix, and $\mathit{D}$ is a p-by-p diagonal matrix.

Therefore, the mean-variance optimization problem is formulated as

$\max {\mu }_{\mathit{a}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}},\mathit{s}.\mathit{t}.$ ${\mathit{F}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}={\mathit{w}}_{\mathit{f}}$, ${\mathit{w}}_{\mathit{f}}^{\mathit{T}}{\Sigma }_{\mathit{f}}{\mathit{w}}_{\mathit{f}}+{\mathit{w}}_{\mathit{a}}^{\mathit{T}}\mathit{D}{\mathit{w}}_{\mathit{a}}\le {\mathit{t}}_{\mathrm{risk}}$,$0\le \mathit{w}\le 1,{\mathit{e}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}=1$.

In the p dimensional space formed by p asset returns, PCA finds the most important k directions that capture the most important variations in the given returns of p assets. Usually, k is less than p. Therefore, by using PCA you can decompose the p asset returns into the k factors, which greatly reduces the dimension of the problem. The k principle directions are interpreted as factor loadings and the scores from the decomposition are interpreted as factor returns. For more information, see pca (Statistics and Machine Learning Toolbox™). In this example, use k = 10 as the number of principal components. Alternatively, you can also find the k value by defining a threshold for the total variance represented as the top k principal components. Usually 95% is an acceptable threshold.

k = 10;
[factorLoading,factorRetn,latent,tsq,explained,mu] = pca(stockReturns{training,:}, 'NumComponents', k);
disp(size(factorLoading))

   100    10

disp(size(factorRetn))

        1200          10

In the output p-by-k factorLoading, each column is a principal component. The asset return vector at each timestep is decomposed to these k dimensional spaces, where k << p. The output factorRetn is a trainingNum-by-k dimension.

Estimate the factor covariance matrix with factor returns.

covarFactor = cov(factorRetn);

You can reconstruct the p asset returns for each observation using each k factor returns by following ${\mathit{r}}_{\mathit{a}}={\mu }_{\mathit{a}}+\mathit{F}{\mathit{r}}_{\mathit{f}}+{\epsilon }_{\mathit{a}}$.

Reconstruct the total 1200 observations for the training set.

reconReturn = factorRetn*factorLoading' + mu;
unexplainedRetn = stockReturns{training,:} - reconReturn;

There are unexplained asset returns ${\epsilon }_{\mathit{a}}$ because the remaining (p - k) principal components are dropped. You can attribute the unexplained asset returns to the asset-specific risks represented as D.

unexplainedCovar = diag(cov(unexplainedRetn));
D = diag(unexplainedCovar);

You can use a problem-based definition framework from Optimization Toolbox™ to construct the variables, objective, and constraints for the problem: $\max {\mu }_{\mathit{a}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}},\mathit{s}.\mathit{t}.$ ${\mathit{F}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}={\mathit{w}}_{\mathit{f}}$, ${\mathit{w}}_{\mathit{f}}^{\mathit{T}}{\Sigma }_{\mathit{f}}{\mathit{w}}_{\mathit{f}}+{\mathit{w}}_{\mathit{a}}^{\mathit{T}}\mathit{D}{\mathit{w}}_{\mathit{a}}\le {\mathit{t}}_{\mathrm{risk}}$,${\mathit{e}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}=1,0\le \mathit{w}\le 1$. The problem-based definition framework enables you to define variables and express objective and constraints symbolically. You can add other constraints or use a different objective based on your specific problem. For more information, see First Choose Problem-Based or Solver-Based Approach.

targetRisk = 0.007;  % Standard deviation of portfolio return
tRisk = targetRisk*targetRisk;  % Variance of portfolio return
meanStockRetn = mean(stockReturns{training,:});

optimProb = optimproblem('Description','Portfolio with factor covariance matrix','ObjectiveSense','max');
wgtAsset = optimvar('asset_weight', p, 1, 'Type', 'continuous', 'LowerBound', 0, 'UpperBound', 1);
wgtFactor = optimvar('factor_weight', k, 1, 'Type', 'continuous');

optimProb.Objective = sum(meanStockRetn'.*wgtAsset);

optimProb.Constraints.asset_factor_weight = factorLoading'*wgtAsset - wgtFactor == 0;
optimProb.Constraints.risk = wgtFactor'*covarFactor*wgtFactor + wgtAsset'*D*wgtAsset <= tRisk;
optimProb.Constraints.budget = sum(wgtAsset) == 1;

x0.asset_weight = ones(p, 1)/p;
x0.factor_weight = zeros(k, 1);
opt = optimoptions("fmincon", "Algorithm","sqp", "Display", "off", ...
    'ConstraintTolerance', 1.0e-8, 'OptimalityTolerance', 1.0e-8, 'StepTolerance', 1.0e-8);
x = solve(optimProb,x0, "Options",opt);
assetWgt1 = x.asset_weight;

In this example, you are maximizing the portfolio return for a target risk. This is a nonlinear programming problem with a quadratic constraint and you use fmincon to solve this problem.

Check for asset allocations that are over 5% to determine which assets have large investment weights.

percentage = 0.05;
AssetName = stockReturns.Properties.VariableNames(assetWgt1>=percentage)';
Weight = assetWgt1(assetWgt1>=percentage);
T1 = table(AssetName, Weight)

T1=7×2 table
     AssetName      Weight 
    ___________    ________

    {'Asset9' }    0.080054
    {'Asset32'}     0.22355
    {'Asset47'}     0.11369
    {'Asset57'}    0.088321
    {'Asset61'}    0.068845
    {'Asset75'}    0.063648
    {'Asset94'}     0.22163

Optimize Asset Allocation Using Portfolio Class with Factor Information

If you already have the factor loading and factor covariance matrix from some other analysis or third-party provider, you can use this information to compute the asset covariance matrix and then directly run a mean-variance optimization using the Portfolio class. Recall that portfolio risk is $\mathrm{Var}\left({\mathit{R}}_{\mathit{p}}\right)=\mathrm{Var}\left({\left({\mu }_{\mathit{a}}+\mathit{F}{\mathit{r}}_{\mathit{f}}+{\epsilon }_{\mathit{a}}\right)}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}\right)={\mathit{w}}_{\mathit{a}}^{\mathit{T}}\left(\mathit{F}{\Sigma }_{\mathit{f}}{\mathit{F}}^{\mathit{T}}+\mathit{D}\right){\mathit{w}}_{\mathit{a}}$ , so you can obtain the covariance of the asset returns by ${\Sigma }_{\mathit{a}}=\mathit{F}{\Sigma }_{\mathit{f}}{\mathit{F}}^{\mathit{T}}+\mathit{D}$.

Use estimateFrontierByRisk in the Portfolio class to solve the optimization problem: $\max {\mu }_{\mathit{a}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}},\mathit{s}.\mathit{t}.$ ${{\mathit{w}}_{\mathit{a}}^{\mathit{T}}\Sigma }_{\mathit{a}}{\mathit{w}}_{\mathit{a}}\le {\mathit{t}}_{\mathrm{risk}}$,$0\le {\mathit{w}}_{\mathit{a}}\le 1,{\mathit{e}}^{\mathit{T}}{\mathit{w}}_{\mathit{a}}=1$. The Portfolio class supports has a variety of built-in constraints that you can use to describe your portfolio problem and estimate the risk and returns on the efficient frontier. For more information, see Portfolio Set for Optimization Using Portfolio Object.

covarAsset = factorLoading*covarFactor*factorLoading'+D;
port = Portfolio("AssetMean", meanStockRetn, 'AssetCovar', covarAsset, 'LowerBound', 0, 'UpperBound', 1, ...
    'Budget', 1);
assetWgt2 = estimateFrontierByRisk(port, targetRisk);

AssetName = stockReturns.Properties.VariableNames(assetWgt2>=percentage)';
Weight = assetWgt2(assetWgt2>=percentage);
T2 = table(AssetName, Weight)

T2=7×2 table
     AssetName      Weight 
    ___________    ________

    {'Asset9' }    0.080061
    {'Asset32'}     0.22355
    {'Asset47'}     0.11369
    {'Asset57'}    0.088314
    {'Asset61'}    0.068847
    {'Asset75'}    0.063644
    {'Asset94'}     0.22163

Portfolio Optimization Results

Tables T1 and T2 show an identical allocation for the asset allocations that are over 5%. Therefore, in this example, both approaches to portfolio optimization with a factor model obtain asset weights that are identical.

Visualize the performance of the optimized allocation over the testing period.

retn = stockReturns{test, :}*assetWgt1;
plot(ret2tick(retn, 'method', 'simple')*100); hold off;
xlabel('Timestep');
ylabel('Value');
title('Portfolio Equity Curve');

This example demonstrates how to derive statistical factors from asset returns using PCA and then use these factors to perform a factor-based portfolio optimization. This example also shows how to use these statistical factors with the Portfolio class. In practice, you can adapt this example to incorporate some measurable market factors, such as industrial factors or ETF returns from various sectors, to describe the randomness in the market [1]. You can define custom constraints on the weights of factors or assets with high flexibility using the problem-based definition framework from Optimization Toolbox™. Alternatively, you can work directly with the Portfolio class to run a portfolio optimization with various built-in constraints.

Reference