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Portfolio Selection - Science topic

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Can the combination of technical and fundamental analysis deliver superior returns over long periods?
This question is precisely the theme that I develop in my new book and that I explore in the context of the management of portfolios based on value, dividend paying and dividend growing companies. The objective is for people who gained financial independence and retired early (FIRE) to maintain permanent cash-flow streams that will not only enable them to support their lifestyle but to far outlast their savings and optimally to keep growing their portfolios to support their heirs.
I would appreciate open discussions on that or related matters (e.g. investment, finance, stock-markets, financial-analysis, quantitative-finance) reviews and comments of my book so that it would help me improve it for some future edition. I hope you'll find my work interesting and I appreciate constructive criticisms. I am open to scientific cooperation (recommending, citing, reviewing..) etc.
Let me know your reactions.
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Great subject, however is a life-long experiment sufficient to get a conclusion?
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find a real example of how a company or organization uses a structured process to aid in project,
program, and/or project portfolio selection. As an alternative, document the process you followed to make a major decision
where you had multiple options, such as what college or university to attend, what job to take, where to live, what car to
buy, and so on. Prepare a short paper summarizing your findings.
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This is a very strange question that looks more like a task.
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I am working on portfolio optimization using lower partial moment of order 1, can someone help me how to implement LPM-1 in excel sheet using "tau" as my threshold value as 0.00% and order (n) as 1.
Thank you all in advance for your contributions to my question.
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For orientation I am looking for theories as a basis of an goal-based investment approach. So far I found some appraoches using
  • Portfolio Optimization with Mental Accounts (POMA)
  • Asset Liability Management (ALM)
  • Stochastic Dominance (SD)
Do you have any other suggestions?
Which approach is most common and accepted?
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Based on the substitution of personal goals for market goals - these are more theories from the field of behavioral finance. As we say: a tit in the hand is better than a crane in the sky!
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I did my PhD in a topic “RISK MODEL AND PORTFOLIO SELECTION: A BEHAVIORAL APPROACH FOR OPTIMIZATION OF RETURNS”. I took five behavioral biases in order identify the behavioral aspects of investor and later developed a descriptive model on portfolio selection. The basic research problem covered in the study is : what are the relevant aspects of investment behavior of individual investor? What is their relative importance in shaping investment behavior of individual investor? What are the decision making tools and techniques used by the investors? What is the impact of determinants of investment behavior on individual investor’s investment decision making process?
Now I am looking for post doctoral programme. What can be the best now I study for my post-doctoral programme.
Moreover, which are the available options for me right
Waiting for the suggestions.
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E. Atta Mills still in the process. At least by this year i hope i will get my admission
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I want to optimize a portfolio in different horizon times.
can you help me what can I do and which software be suitable for this kind of problem? especially about portfolio re-balancing?
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I think LINGO
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Dear colleagues, is there any information or papers about linking good corporate governance to portfolio selection? Thanks in advance for any help.
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Dear Elvis,
As you can see from the statement of Jyoti, the answer to your question may be not as clear cut as one could suppose. Jyoti points to La Porta et al. (2002) who argue that in the environment of better legal protection investors may be ready to pay a "security premium" on the stock prices. But, on the other hand, she says that costs of equity decrease due to reduced monitoring and auditing costs in a strong legal environment, a view that I also support. However, this contradicts the findings of La Porta et al. (2002). I think there are still lots of issues open to further research.
Paul
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I am still struggling to understand the exact differences between Multi-attribute Decision Making (MADM) and Multi-objective Decision Making (MODM). From what I know a major difference is the solution space (discrete vs continuous).
Could someone provide me with a practical example to illustrate that difference? An (project) portfolio selection example would be even more helpful.
Thanks a lot!
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For simplicity, I will take only three attributes for portfolio selection. Let there be five IT companies c1,...,c5 and three attributes, namely profit, debt equity ratio and eps. We collect past data of three attributes for these five companies. You can also see this data as 5 points in three dimensional space. Out of these five points, we have to select the best one using some criterion. Thus, it became a problem of selection from a finite set. This is MADM.
Now consider the following problem. I have taken this problem just randomly to explain. It may not have any solution.
Max f1=x1+x2-x3;
Min f2=x1-2*x2+3*x3;
subject to
g1(x1,x2)=3*x1+4*x2-x3<=5;
-1<=x1<=1, 2<=x2<=3; -2<=x3<=4;
Between the specified range of x1,x2 and x3 and those points which satisfy g1 also, there are infinitely many points from which I want a point which maximizes f1 and minimizes f2 simultaneously. These points are also from three dimensional space but the number of points are infinite. We are not given limited alternatives. In this case we have infinite alternatives. This is MODM.
I hope now you might have understood the difference.
For your kind information, our paper on portfolio selection is in press. I will upload it when it is published.
Unfortunately, I was not so active in Q&A when you asked the question. So, too late answer.
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I just have a Electre tri software.
I'm going to compare some sorting methods (around 5) in a stock portfolio problem. then I should find some proper sorting methods and softwares.
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Hi Seyed,
There are several software available. Find here some pointers. Best is to contact the authors if you are interested in their method.
- FlowSort  sorting method (based on Promethee methodology): www.smart-picker.com  --> there is a trial version and also research licences
- FINCLASS (Multicriteria Decision Support for Financial Classification Problems) (in Newsletter, Series 3, No. 3, Spring 2001): http://www.cs.put.poznan.pl/ewgmcda/pdf/SWFinclass.pdf
Other references:
Best wishes,
Philippe
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I am considering which algorithm to use for Multi-class classification of credit risk scores.
I have a training set of ~100 default rates... and another transaction history of 1000 companies.
The idea is to classify in 5 groups A, B, C, D, E
My plan was to use Support Vector Machine for Mult-class. but I am open for new ideas.
Thanks
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There are two things that I would advise is:
a)As Sylantyev mentioned the time period is important due to crisis post-crisis period. you should test the data to see if the features correlate to the time periods. Also, see if shorter time periods have an effect on the data (spot cyclical or specific time periods which might bias the data)
b) At the stage that you are mentioning right now it is better to test several algorithms and not just one. Unless you have previous literature that already has done the work for you, I would suggest that you try several methods.
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In a real world portfolio selection project where credit is given to business customers, I found two possible approaches.
1) Machine Learning - Building some sort of algorithm and find a maximization for risk/return given a certain amount of capital. The algo would have to search how to allocate the capital to get the maximum amount of return given a maximum affordable and pre-defined risk score. (The risk score is by the way, the current risk in the portfolio, as it already exist and we are trying to improve it).
2) The second approach, if possible, could be straightforward, trying to maximize Sharpe Ratio.
The training set contains a 50.000 transaction set of more than 1.000 businesses.
The questions are
a) Which approach would work better and why?
b) If machine learning is the recommended method, which algorithm would be suitable for the job.
c) If Sharpe Ratio is recommended, so how would I maximize without a utility function?
d) Is there a totally different way at looking at the challenge?
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