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Multi-objective Approach to Portfolio Optimization
童培俊张帆
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CONTENTS Introduction Motivation Methodology Application Risk Aversion Index
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Key Concept Reward and risk are measured by
expected return and variance of a portfolio
Decision variable of this problem is asset weight vector
1 nx x x
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Introduction to Portfolio Optimization The Mean Variance Optimization
Proposed by Nobel Prize Winner Markowitz in 1990
Model 1: Minimize risk for a given level of expected return
Minimize: Subject to:
2 T
p x V x
*p
*1 1T Tx and p x p
////////////////////////// //
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Not be the best model for those who are extremely risk seeking
Does not allow to simultaneously minimize risk and maximize expected return
Multi-objective Optimization
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Introduction to Multi-objective Optimization Developed by French-Italian economist
Pareto Combine multiple objectives into one
objective function by assigning a weighting coefficient to each objective
1
minn
i ii
F x a f x
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Multi-objective Formulation Minimize w.r.t. Subject to:
Assign two weighting coefficients
Minimize: Subject to:
,T Tp x x V x////////////////////////////////////////////////////////
1 1Tx
1 1Tx
T Tp x x V x ////////////////////////////////////////////////////////
x
1 21 0and
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Risk Aversion Index We can consider as a risk aversion
index that measures the risk tolerance of an investor
Smaller , more risk seeking Larger , more risk averse
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Model 2: Maximize expected return (disregard risk) Maximize: Subject to:
Model 3: Minimize risk (disregard expected return) Minimize: Subject to:
T
px p x////////////////////////////
2 T
p x V x
1 1Tx
1 1Tx
0When
When
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Comparison with Mean Variance Optimization Since the Lagrangian multipliers of both
methods are same, their efficient frontiers are also same
Different in their approach to producing their efficient frontiers
Varying Varying
*p
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Two comparative advantages For investors who are extremely risk
seeking When investors do not want to place
any constraints on their investment
Provide the entire picture of optimal risk-return trade off
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Solving Multi-objective Optimization Using Lagrangian multiplier
The optimized solution for the portfolio weight vector is
1 1T T T
L x p x x V x x ////////////////////////////////////////////////////////////////////////////// //////
1* 1 2
1 1
1 11 2
1 2( )1
2 2
1 1 1T T
aVx V p
a a
where a V and a V p
////////////////////////// //
///////////// /
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Convex Vector Optimization The second derivative of the objective
function is positive definite The equality constraint can be
expressed in linear form
is the optimal solution *x
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ApplicationsStock Exp. Return Variance
IBM 0.400% 0.006461
MSFT 0.513% 0.0039
AAPL 4.085% 0.012678
DGX 1.006% 0.005598361
BAC 1.236% 0.001622897
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IBM MSFT AAPL DGX BAC
IBM 0.006461
0.002983
0.00235487
0.00235487
0.00096889
MSFT 0.002983
0.0039 0.00095937
-0.0001987
0.00063459
AAPL 0.002355
0.000959
0.01267778
0.00135712
0.00134481
DGX 0.002355
-0.0002
0.00135712
0.00559836
0.00041942
BAC 0.000969
0.000635
0.00134481
0.00041942
0.0016229
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Example When equals to 50, the optimal
portfolio strategy shows that the investor should invest
-15.94% in IBM 30.37% in MSFT 3.19% in AAPL 22.60% in DGX 59.78% in BAC
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If cases involving of short selling are excluded in this example, the investor should invest
19.77% in MSFT 2.05% in AAPL 16.96% in DGX 61.22% in BAC
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1 2 3 4 5 6 7 8 9 10-1
-0.5
0
0.5
1
1.5
IBM
MSFT
AAPLDGX
BAC
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The risk aversion parameter
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Proof:
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The End
Thanks!