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Heuristic Optimization Strategies inFinance - An Overview
Marianna Lyra
COMISEF Fellows’ Workshop: Numerical Methods and Optimization in Finance
Financial support from the EU Commission through COMISEF is gratefullyacknowledged
June 18, 2009
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Marianna LyraEarly Stage Researcher - COMISEFResearch Network
1 BA Business Administration -minor Finance, UCY
2 MSc Finance, UCY3 PhD student JLU Giessen, DE4 Research Interests: Apply
Optimization Heuristics inFinance (Credit RiskManagement)
5 Contact Info:http://comisef.wikidot.com/mariannalyra
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
1 IntroductionFinancial WorldComplexity
2 Heuristic Optimization TechniquesClassical conceptDifferential Evolution
3 Financial ApplicationsOptimization heuristic strategies in finance
4 ConclusionHave in mind
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
1 IntroductionFinancial WorldComplexity
2 Heuristic Optimization TechniquesClassical conceptDifferential Evolution
3 Financial ApplicationsOptimization heuristic strategies in finance
4 ConclusionHave in mind
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Financial World
Financial world
Optimization
Financial problems
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Complexity
Least median of squares residuals as a function of α and β
Multiple local minima (optima)Apply optimization heuristic techniques
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
1 IntroductionFinancial WorldComplexity
2 Heuristic Optimization TechniquesClassical conceptDifferential Evolution
3 Financial ApplicationsOptimization heuristic strategies in finance
4 ConclusionHave in mind
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Classical concept
Local search procedure
1: Generate initial solution xc
2: while stopping criteria not met do3: Select xn ∈ N (xc) (neighbor to current solution)4: if f (xn) < f (xc) then5: xc = xn
6: end if7: end while
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Classical concept
Heuristic Techniques
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_ _ _ _ _ _Construction methods Local search methods
Trajectory methodsmmmmmm
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Thresholdaccepting
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Differentialevolution
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Tabusearch
Geneticalgorithms
Hybrid Meta Heuristics
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Classical concept
Heuristic Techniques
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��_ _ _ _ _ _Construction methods Local search methods
Trajectory methodsnnnnnnn
wwnnnnnn Population search
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((PPP
Thresholdaccepting
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Differentialevolution
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Tabusearch
Geneticalgorithms
Hybrid Meta Heuristics
99dd
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionPopulation based heuristic with remarkable performance incontinuous numerical problems.
Example
Capital Asset Pricing Model (CAPM): Risk-returnequilibrium.
ri,t − r st = α + β(rm,t − r s
t ) + εi,t (1)
Least Median of Squares Estimators (LMS) (Rousseeuwand Leroy (1987)):
minα,β
(med(ε2i,t)) (2)
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM usingLMS estimators
1 Generate random set values α and β (initialsolutions)
2 Evaluate initial solutions minimizing LMS3 Generate new candidate solutions from the
initial one4 Evaluate new candidate solutions minimizing
LMS5 Repeat until a very good solution is found
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM usingLMS estimators
1 Generate random set values α and β (initialsolutions)
2 Evaluate initial solutions minimizing LMS3 Generate new candidate solutions from the
initial one4 Evaluate new candidate solutions minimizing
LMS5 Repeat until a very good solution is found
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM usingLMS estimators
1 Generate random set values α and β (initialsolutions)
2 Evaluate initial solutions minimizing LMS3 Generate new candidate solutions from the
initial one4 Evaluate new candidate solutions minimizing
LMS5 Repeat until a very good solution is found
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM usingLMS estimators
1 Generate random set values α and β (initialsolutions)
2 Evaluate initial solutions minimizing LMS3 Generate new candidate solutions from the
initial one4 Evaluate new candidate solutions minimizing
LMS5 Repeat until a very good solution is found
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM usingLMS estimators
1 Generate random set values α and β (initialsolutions)
2 Evaluate initial solutions minimizing LMS3 Generate new candidate solutions from the
initial one4 Evaluate new candidate solutions minimizing
LMS5 Repeat until a very good solution is found
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 1 & 2: Generate and evaluate random set valuesα[−1, 2] and β[0, 1] (initial solutions)
P(0)=
d
12
np
minα,β(med(ε2
i,t))
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 1 & 2: Generate and evaluate random set valuesα[−1, 2] and β[0, 1] (initial solutions)
P(0)=
d
12
np
minα,β(med(ε2
i,t))
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
Generate new candidate solutions from the initial oneStep 3a: Differential mutation
P(0)1,i =
1 2 ... r3 ... r1 ... r2 ... np
P(υ)
1,i = P(0)1,r1
+ F × (P(0)1,r2
- P(0)1,r3
)
F scale factor ∈ [0, 1+] determines speed of shrinkage
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
......u1 ud
CR CR
uniform crossover
...u1
ud
P(u)1,i
. . . P(u)d ,i
Random crossoverStep 3b: Crossover elements fromP(0)
1,i and P(υ)1,i
1 Generate for each parameter auniform random number,ud ∈ [0, 1]
2 Determine the crossoverprobability, CR ∈ [0, 1]
3 Only if u < CR, P(u)1,i = P(υ)
1,i
4 else P(u)1,i = P(0)
1,i
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
Generate new candidate solutions from the initial oneStep 3a: Differential mutation
P(0)1,i =
1 2 ... r3 ... r1 ... r2 ... np
P(υ)
2,i = P(0)2,r1
+ F × (P(0)2,r2
- P(0)2,r3
)
F scale factor ∈ [0, 1+] determines speed of shrinkage
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
......u1 ud
CR CR
uniform crossover
...u1
ud
P(u)1,i
. . . P(u)d ,i
Random crossoverStep 3b: Crossover elements fromP(0)
d ,i and P(υ)d ,i
1 Generate for each parameter auniform random number,ud ∈ [0, 1]
2 Determine the crossoverprobability, CR ∈ [0, 1]
3 Only if u < CR, P(u)d ,i = P(υ)
d ,i
4 else P(u)d ,i = P(0)
d ,i
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 4: Evaluate new candidate solutions minimizing LMS
if f (P(u).,i ) < f (P(0)
.,i ) then f (P(0).,i ) = f (P(u)
.,i )
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 4: Evaluate new candidate solutions minimizing LMS
if f (P(u).,i ) < f (P(0)
.,i ) then f (P(0).,i ) = f (P(u)
.,i )
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 5: Repeat steps 3 & 4 until a good solution is found orfor a predefined number
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Differential Evolution
Differential EvolutionOptimal parameter estimation of CAPM using LMS estimators
InitializationStep 5: Repeat steps 3 & 4 until a good solution is found orfor a predefined number
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
1 IntroductionFinancial WorldComplexity
2 Heuristic Optimization TechniquesClassical conceptDifferential Evolution
3 Financial ApplicationsOptimization heuristic strategies in finance
4 ConclusionHave in mind
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
VAR
Transaction Costs
Cardinality constraints
Index tracking
Model estimation
Model selectionFinancial forecasting
Portfolio improvement
Mutual funds style
Portfolio OptimizationRobust methods
Clustering
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
VAR
Transaction Costs
Cardinality constraints
Index tracking
Heuristics
Model estimation
Model selectionFinancial forecasting
Portfolio improvement
Mutual funds style
Portfolio OptimizationRobust methods
Clustering
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Portfolio SelectionDueck and Winker (1992) appliedThreshold Accepting
1 Portfolio optimization usingvarious risk measures
2 Index tracking to mutual fundreplication
3 Currency portfolio optimization
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Portfolio SelectionDueck and Winker (1992) appliedThreshold Accepting
1 Portfolio optimization usingvarious risk measures
2 Index tracking to mutual fundreplication
3 Currency portfolio optimization
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Portfolio SelectionDueck and Winker (1992) appliedThreshold Accepting
1 Portfolio optimization usingvarious risk measures
2 Index tracking to mutual fundreplication
3 Currency portfolio optimization
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Model estimation1 Risk estimation and GARCH models2 Indirect estimation and Agent Based Models3 Yield curve estimation
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Model estimation1 Risk estimation and GARCH models2 Indirect estimation and Agent Based Models3 Yield curve estimation
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Model estimation1 Risk estimation and GARCH models2 Indirect estimation and Agent Based Models3 Yield curve estimation
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Yield curve estimation
Fig. 6. Error in the estimation of the interest rates. Comparison of the traditional non-linear least squares and the GA for the Nelson and Siegel (1987) on
the left and for the Svensson (1994) function on the right for 1 year (top graph) and 10 years (bottom graph) government bonds.
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Model selection1 Risk factor selection (Asset Pricing Theory model (APT))
ri,t = α +k∑
f=1
βf rf ,t + εi,t (3)
2 Selection of bankruptcy predictors
BMW assets = 0.8German factor + 0.2US factor+0.9automotive factor + 0.1finance factor
+BMW non − systematic risk (4)
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Model selection1 Risk factor selection (Asset Pricing Theory model (APT))
ri,t = α +k∑
f=1
βf rf ,t + εi,t (3)
2 Selection of bankruptcy predictors
BMW assets = 0.8German factor + 0.2US factor+0.9automotive factor + 0.1finance factor
+BMW non − systematic risk (4)
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Clustering1 Bankruptcy prediction2 Credit risk rating3 Portfolio performance improvement4 Identify optimal number of clusters
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Clustering1 Bankruptcy prediction2 Credit risk rating3 Portfolio performance improvement4 Identify optimal number of clusters
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Clustering1 Bankruptcy prediction2 Credit risk rating3 Portfolio performance improvement4 Identify optimal number of clusters
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Optimization heuristic strategies in finance
Clustering1 Bankruptcy prediction2 Credit risk rating3 Portfolio performance improvement4 Identify optimal number of clusters
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
1 IntroductionFinancial WorldComplexity
2 Heuristic Optimization TechniquesClassical conceptDifferential Evolution
3 Financial ApplicationsOptimization heuristic strategies in finance
4 ConclusionHave in mind
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Have in mind
Heuristic optimization techniquesFlexible to tackle many complex optimization problems
Flexibility costMight be computationally more demanding than traditionalmethods (not a limitation nowadays)Results carefully interpreted
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Introduction Heuristic Optimization Techniques Financial Applications Conclusion
Have in mind
SummaryApply Optimization Heuristics
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