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Optimization models for portfolio optimization
Reference request: how to model nonlinear regression?What is the connection of Operations Research and Reinforcement Learning?Recommended books/materials for practical applications of Operations Research in industryHow to avoid having your optimization models rusting?Benchmark problems for scenario-based stochastic optimizationCombinatorial Optimization: Metaheuristics, CP, IP — “versus” or “and”?Usages of logarithmic mean in optimizationGuidelines for Linear Optimization approaches?maximum eigenvalue across subsamplesOptimization terminology: “Exact” v. “Approximate”
$begingroup$
What are the mainstream models for portfolio optimization? We have Markowitz mean-variance model and CVaR-based models (e.g., max return subject to a CVaR constraint). What else is out there in terms of risk measures or formulations?
optimization combinatorial-optimization finance
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
$endgroup$
add a comment |
$begingroup$
What are the mainstream models for portfolio optimization? We have Markowitz mean-variance model and CVaR-based models (e.g., max return subject to a CVaR constraint). What else is out there in terms of risk measures or formulations?
optimization combinatorial-optimization finance
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
$endgroup$
add a comment |
$begingroup$
What are the mainstream models for portfolio optimization? We have Markowitz mean-variance model and CVaR-based models (e.g., max return subject to a CVaR constraint). What else is out there in terms of risk measures or formulations?
optimization combinatorial-optimization finance
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
$endgroup$
What are the mainstream models for portfolio optimization? We have Markowitz mean-variance model and CVaR-based models (e.g., max return subject to a CVaR constraint). What else is out there in terms of risk measures or formulations?
optimization combinatorial-optimization finance
optimization combinatorial-optimization finance
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
edited Jul 9 at 16:13
Daniel Duque
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
asked Jul 7 at 15:23
Daniel DuqueDaniel Duque
5568 bronze badges
5568 bronze badges
add a comment |
add a comment |
4 Answers
4
active
oldest
votes
$begingroup$
Here's what is not really mainstream now, but should be. The mean and especially the covariance matrix of returns is not known. Treating estimates of then as though they are known with certainty can lead to very suboptimal results.
Just to start vectoring yourself in the right direction, you can start by looking at
MEAN–VARIANCE PORTFOLIO OPTIMIZATION WHEN MEANS AND COVARIANCES ARE UNKNOWN, TZE LEUNG LAI, HAIPENG XING, and ZEHAO CHEN, Annals of Statistics, 2011, Vol. 5, No. 2A, 798–823.
Improving Portfolios Global Performance with Robust Covariance Matrix Estimation:Application to the Maximum Variety Portfolio, Emmanuelle Jay, Eugenie Terreaux, Jean-Philippe Ovarlez, and Frederic Pascal.
You may also find of interest methods to identify financial risk factors using large data sets.
Identifying Financial Risk Factors with a Low-Rank Sparse Decomposition, Lisa Goldberg and Alex Shkolnik. This decomposes covariance as a sum of a rank-one factor component and a diagonal security specific return component
Here is a semi-classic paper advising you NOT to use the sample covariance matrix for portfolio optimization. "Shrinking" it toward a better conditioned matrix. even though producing a biased estimator of the covariance matrix, can improve the results of portfolio optimization (note that the condition number of the sample covariance matrix is a very biased estimator of the condition number of the true covariance matrix, and is infinite when the number of vector data points is less than the number of variables).
Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit and MichaelWolf, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119 (link is to free version of the paper)
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
$endgroup$
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
add a comment |
$begingroup$
Another aspect of portfolio optimization which is often important in practise is sparsity, i.e., requiring that the portfolio invests in at most $k$ non-zero stocks to cut down on transaction fees and avoid functioning as an index fund. This can be achieved by imposing a sparsity constraint $Vert x Vert_0 leq k.$
The classical reference on this topic is this 1996 paper by Bienstock.
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
$endgroup$
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
add a comment |
$begingroup$
I'm no expert on the topic, but I found the textbook Optimization Methonds in Finance really accessible. The authors teach optimization modeling and solving motivated by applications in finance, including portfolio optimization.
In particular they describe a linear model using mean absolute deviation as a linear proxy for variance.
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
$endgroup$
add a comment |
$begingroup$
For what it's worth, here's a video explanation and the source code of my Portfolio Optimization implementation. Run InvestmentApp to try it yourself.
For the sake of having a simple example, we decided to go with expected return (1 year), standard deviation risk and asset correlation. The example xlsx data came from Yahoo Finance IIRC.
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
$endgroup$
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
add a comment |
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Here's what is not really mainstream now, but should be. The mean and especially the covariance matrix of returns is not known. Treating estimates of then as though they are known with certainty can lead to very suboptimal results.
Just to start vectoring yourself in the right direction, you can start by looking at
MEAN–VARIANCE PORTFOLIO OPTIMIZATION WHEN MEANS AND COVARIANCES ARE UNKNOWN, TZE LEUNG LAI, HAIPENG XING, and ZEHAO CHEN, Annals of Statistics, 2011, Vol. 5, No. 2A, 798–823.
Improving Portfolios Global Performance with Robust Covariance Matrix Estimation:Application to the Maximum Variety Portfolio, Emmanuelle Jay, Eugenie Terreaux, Jean-Philippe Ovarlez, and Frederic Pascal.
You may also find of interest methods to identify financial risk factors using large data sets.
Identifying Financial Risk Factors with a Low-Rank Sparse Decomposition, Lisa Goldberg and Alex Shkolnik. This decomposes covariance as a sum of a rank-one factor component and a diagonal security specific return component
Here is a semi-classic paper advising you NOT to use the sample covariance matrix for portfolio optimization. "Shrinking" it toward a better conditioned matrix. even though producing a biased estimator of the covariance matrix, can improve the results of portfolio optimization (note that the condition number of the sample covariance matrix is a very biased estimator of the condition number of the true covariance matrix, and is infinite when the number of vector data points is less than the number of variables).
Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit and MichaelWolf, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119 (link is to free version of the paper)
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
$endgroup$
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
add a comment |
$begingroup$
Here's what is not really mainstream now, but should be. The mean and especially the covariance matrix of returns is not known. Treating estimates of then as though they are known with certainty can lead to very suboptimal results.
Just to start vectoring yourself in the right direction, you can start by looking at
MEAN–VARIANCE PORTFOLIO OPTIMIZATION WHEN MEANS AND COVARIANCES ARE UNKNOWN, TZE LEUNG LAI, HAIPENG XING, and ZEHAO CHEN, Annals of Statistics, 2011, Vol. 5, No. 2A, 798–823.
Improving Portfolios Global Performance with Robust Covariance Matrix Estimation:Application to the Maximum Variety Portfolio, Emmanuelle Jay, Eugenie Terreaux, Jean-Philippe Ovarlez, and Frederic Pascal.
You may also find of interest methods to identify financial risk factors using large data sets.
Identifying Financial Risk Factors with a Low-Rank Sparse Decomposition, Lisa Goldberg and Alex Shkolnik. This decomposes covariance as a sum of a rank-one factor component and a diagonal security specific return component
Here is a semi-classic paper advising you NOT to use the sample covariance matrix for portfolio optimization. "Shrinking" it toward a better conditioned matrix. even though producing a biased estimator of the covariance matrix, can improve the results of portfolio optimization (note that the condition number of the sample covariance matrix is a very biased estimator of the condition number of the true covariance matrix, and is infinite when the number of vector data points is less than the number of variables).
Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit and MichaelWolf, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119 (link is to free version of the paper)
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
$endgroup$
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
add a comment |
$begingroup$
Here's what is not really mainstream now, but should be. The mean and especially the covariance matrix of returns is not known. Treating estimates of then as though they are known with certainty can lead to very suboptimal results.
Just to start vectoring yourself in the right direction, you can start by looking at
MEAN–VARIANCE PORTFOLIO OPTIMIZATION WHEN MEANS AND COVARIANCES ARE UNKNOWN, TZE LEUNG LAI, HAIPENG XING, and ZEHAO CHEN, Annals of Statistics, 2011, Vol. 5, No. 2A, 798–823.
Improving Portfolios Global Performance with Robust Covariance Matrix Estimation:Application to the Maximum Variety Portfolio, Emmanuelle Jay, Eugenie Terreaux, Jean-Philippe Ovarlez, and Frederic Pascal.
You may also find of interest methods to identify financial risk factors using large data sets.
Identifying Financial Risk Factors with a Low-Rank Sparse Decomposition, Lisa Goldberg and Alex Shkolnik. This decomposes covariance as a sum of a rank-one factor component and a diagonal security specific return component
Here is a semi-classic paper advising you NOT to use the sample covariance matrix for portfolio optimization. "Shrinking" it toward a better conditioned matrix. even though producing a biased estimator of the covariance matrix, can improve the results of portfolio optimization (note that the condition number of the sample covariance matrix is a very biased estimator of the condition number of the true covariance matrix, and is infinite when the number of vector data points is less than the number of variables).
Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit and MichaelWolf, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119 (link is to free version of the paper)
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
$endgroup$
Here's what is not really mainstream now, but should be. The mean and especially the covariance matrix of returns is not known. Treating estimates of then as though they are known with certainty can lead to very suboptimal results.
Just to start vectoring yourself in the right direction, you can start by looking at
MEAN–VARIANCE PORTFOLIO OPTIMIZATION WHEN MEANS AND COVARIANCES ARE UNKNOWN, TZE LEUNG LAI, HAIPENG XING, and ZEHAO CHEN, Annals of Statistics, 2011, Vol. 5, No. 2A, 798–823.
Improving Portfolios Global Performance with Robust Covariance Matrix Estimation:Application to the Maximum Variety Portfolio, Emmanuelle Jay, Eugenie Terreaux, Jean-Philippe Ovarlez, and Frederic Pascal.
You may also find of interest methods to identify financial risk factors using large data sets.
Identifying Financial Risk Factors with a Low-Rank Sparse Decomposition, Lisa Goldberg and Alex Shkolnik. This decomposes covariance as a sum of a rank-one factor component and a diagonal security specific return component
Here is a semi-classic paper advising you NOT to use the sample covariance matrix for portfolio optimization. "Shrinking" it toward a better conditioned matrix. even though producing a biased estimator of the covariance matrix, can improve the results of portfolio optimization (note that the condition number of the sample covariance matrix is a very biased estimator of the condition number of the true covariance matrix, and is infinite when the number of vector data points is less than the number of variables).
Honey, I Shrunk the Sample Covariance Matrix, Olivier Ledoit and MichaelWolf, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119 (link is to free version of the paper)
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
edited Jul 7 at 16:54
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
answered Jul 7 at 16:39
Mark L. StoneMark L. Stone
2,4195 silver badges24 bronze badges
2,4195 silver badges24 bronze badges
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
add a comment |
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
$begingroup$
Thanks for the references. I agree that parameter estimation is often disregarded in this particular problem.
$endgroup$
– Daniel Duque
Jul 9 at 15:07
add a comment |
$begingroup$
Another aspect of portfolio optimization which is often important in practise is sparsity, i.e., requiring that the portfolio invests in at most $k$ non-zero stocks to cut down on transaction fees and avoid functioning as an index fund. This can be achieved by imposing a sparsity constraint $Vert x Vert_0 leq k.$
The classical reference on this topic is this 1996 paper by Bienstock.
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
$endgroup$
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
add a comment |
$begingroup$
Another aspect of portfolio optimization which is often important in practise is sparsity, i.e., requiring that the portfolio invests in at most $k$ non-zero stocks to cut down on transaction fees and avoid functioning as an index fund. This can be achieved by imposing a sparsity constraint $Vert x Vert_0 leq k.$
The classical reference on this topic is this 1996 paper by Bienstock.
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
$endgroup$
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
add a comment |
$begingroup$
Another aspect of portfolio optimization which is often important in practise is sparsity, i.e., requiring that the portfolio invests in at most $k$ non-zero stocks to cut down on transaction fees and avoid functioning as an index fund. This can be achieved by imposing a sparsity constraint $Vert x Vert_0 leq k.$
The classical reference on this topic is this 1996 paper by Bienstock.
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
$endgroup$
Another aspect of portfolio optimization which is often important in practise is sparsity, i.e., requiring that the portfolio invests in at most $k$ non-zero stocks to cut down on transaction fees and avoid functioning as an index fund. This can be achieved by imposing a sparsity constraint $Vert x Vert_0 leq k.$
The classical reference on this topic is this 1996 paper by Bienstock.
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
answered Jul 10 at 14:29
Ryan Cory-WrightRyan Cory-Wright
8654 silver badges17 bronze badges
8654 silver badges17 bronze badges
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
add a comment |
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
Interesting. Why not simply penalize the transactions fees itself in the fitness function, and leave it to the solver to figure out how many to incur?
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
$begingroup$
You could certainly do that instead. If you did that you would have a fixed cost component for each stock which you invested in, plus a marginal transaction cost which you can model by modifying the expected return vector accordingly, so this approach would lead to adding a $lambda Vert x Vert_0$ term to the objective. You may however want to impose $Vert x Vert_0 leq k$ instead, since MINLPs are solved via decomposition schemes and cardinality constraints restrict the size of the subproblems. I've given some other reasons in section 2.2 of this paper.
$endgroup$
– Ryan Cory-Wright
6 hours ago
add a comment |
$begingroup$
I'm no expert on the topic, but I found the textbook Optimization Methonds in Finance really accessible. The authors teach optimization modeling and solving motivated by applications in finance, including portfolio optimization.
In particular they describe a linear model using mean absolute deviation as a linear proxy for variance.
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
$endgroup$
add a comment |
$begingroup$
I'm no expert on the topic, but I found the textbook Optimization Methonds in Finance really accessible. The authors teach optimization modeling and solving motivated by applications in finance, including portfolio optimization.
In particular they describe a linear model using mean absolute deviation as a linear proxy for variance.
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
$endgroup$
add a comment |
$begingroup$
I'm no expert on the topic, but I found the textbook Optimization Methonds in Finance really accessible. The authors teach optimization modeling and solving motivated by applications in finance, including portfolio optimization.
In particular they describe a linear model using mean absolute deviation as a linear proxy for variance.
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
$endgroup$
I'm no expert on the topic, but I found the textbook Optimization Methonds in Finance really accessible. The authors teach optimization modeling and solving motivated by applications in finance, including portfolio optimization.
In particular they describe a linear model using mean absolute deviation as a linear proxy for variance.
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
answered Jul 10 at 8:04
Robert SchwarzRobert Schwarz
5262 silver badges8 bronze badges
5262 silver badges8 bronze badges
add a comment |
add a comment |
$begingroup$
For what it's worth, here's a video explanation and the source code of my Portfolio Optimization implementation. Run InvestmentApp to try it yourself.
For the sake of having a simple example, we decided to go with expected return (1 year), standard deviation risk and asset correlation. The example xlsx data came from Yahoo Finance IIRC.
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
$endgroup$
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
add a comment |
$begingroup$
For what it's worth, here's a video explanation and the source code of my Portfolio Optimization implementation. Run InvestmentApp to try it yourself.
For the sake of having a simple example, we decided to go with expected return (1 year), standard deviation risk and asset correlation. The example xlsx data came from Yahoo Finance IIRC.
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
$endgroup$
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
add a comment |
$begingroup$
For what it's worth, here's a video explanation and the source code of my Portfolio Optimization implementation. Run InvestmentApp to try it yourself.
For the sake of having a simple example, we decided to go with expected return (1 year), standard deviation risk and asset correlation. The example xlsx data came from Yahoo Finance IIRC.
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
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For what it's worth, here's a video explanation and the source code of my Portfolio Optimization implementation. Run InvestmentApp to try it yourself.
For the sake of having a simple example, we decided to go with expected return (1 year), standard deviation risk and asset correlation. The example xlsx data came from Yahoo Finance IIRC.
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
answered Jul 10 at 13:49
Geoffrey De SmetGeoffrey De Smet
89911 bronze badges
89911 bronze badges
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
add a comment |
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
Very nice. So the underlying model is Markowitz 52'?
$endgroup$
– Daniel Duque
2 days ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
$begingroup$
I am not sure about the spec version any more (I wrote this 4 years ago in a day or 2), but it was based of the wikipedia article about Markowitz Portfolio Theory and the formula shown in the video, see also the problem spec in docs section 3.18.
$endgroup$
– Geoffrey De Smet
20 hours ago
add a comment |
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