Use of heuristic rules in evolutionary methods for the selection of optimal investment portfolios

2007 ◽  
Author(s):  
Ruben Ruiz-Torrubiano ◽  
Alberto Suarez
Keyword(s):  
Optimal Investment ◽  
Heuristic Rules ◽  
Evolutionary Methods ◽  
Investment Portfolios ◽  
Selection Of

Machine Learning and Financial Investing

2010 ◽  
pp. 375-386
Author(s):  
Jie Du ◽  
Roy Rada
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Evolutionary Computation ◽  
Evolutionary Methods ◽  
Knowledge Based ◽  
Key Features ◽  
Selection Of

This chapter presents the case for knowledge-based machine learning in financial investing. Machine learning here, while it will exploit knowledge, will also rely heavily on the evolutionary computation paradigm of learning, namely reproduction with change and selection of the fit. The chapter will begin with a model for financial investing and then review what has been reported in the literature as regards knowledge-based and machine-learning-based methods for financial investing. Finally, a design of a financial investing system is described which incorporates the key features identified through the literature review. The emerging trend of incorporating knowledge-based methods into evolutionary methods for financial investing suggests opportunities for future researchers.

scholarly journals Criteria of Optimal Portfolio Selection: Evidence From Private Investors in Greece, Turkey, Italy

2018 ◽  
Vol 8 (2) ◽  
pp. 258
Author(s):  
Mihail N Diakomihalis ◽  
Katerina A Parra ◽  
Assunta Di Vaio ◽  
Derya Atlay Isik
Keyword(s):  
Analytic Hierarchy ◽  
Developed Country ◽  
Financial Investment ◽  
Long Time ◽  
Optimal Portfolio Selection ◽  
Private Investors ◽  
Investment Portfolios ◽  
Hierarchy Process ◽  
Selection Of ◽  
Country Specific

What are the criteria for private investors when they decide to invest their savings in to different investment products? Do these criteria differ between investors from different countries? We are investigating the investment portfolios determinants between private investors from Greece, Turkey and Italy. The study is grounded in the current and potential criteria and sub-criteria influencing investors in selecting financial investment products. The methodology applied in order to satisfy the research aims is the Analytic Hierarchy Process (AHP). The results show that there are considerable differences in the ranking and significance of factors that determine the selection of financial investment products in these three countries. We conclude that differences in the ranking and significance are related to country-specific rather than investor factors and they are justified by the differences of the three countries, one of which is a member of the Eurozone facing a long time of economic crisis, another a candidate EU member, with unforeseeable political system which influences the economic environment as well, and one is highly developed country which belongs to G20.

scholarly journals Identification and Prioritization of Risk and Its Effect of the Renewable Energy Life Cycle Based on Performance and Risk Indicators

2019 ◽  
Vol 2 (2) ◽  
pp. p61
Author(s):  
Hassan Farsijani ◽  
Maryam Moradi
Keyword(s):  
Risk Factors ◽  
Renewable Energy ◽  
Life Cycle ◽  
Electricity Market ◽  
Risk Control ◽  
Optimal Investment ◽  
Energy System ◽  
Risk Indicators ◽  
High Risk Factors ◽  
Investment Portfolios

Risk management consists of two aspects of risk control and risk assessment in the electricity market. So, risk control should cover the risk and work out of the way of optimal investment portfolios. Thus, the aim of this research is producing solar electricity life cycle profitability. First to identify existing risks in the production of electricity using Delphi technique between 300 experts in 15 Powerhouse. Then, the grey ANP model was the adoption of the New Energy Organization of Iran. The number of risk factors were collected by subject literature in renewable energy in Iran that have analyzed and selected the high-risk factors by ANP GREY method. Finally, to examine the life cycle of solar power, the authors analyzed financial indicators and the life cycle’s factors which relates to performance and risk variables, then, the Regression model used in three stages of life cycle. Finally, the result provides incentives for the energy system to support production renewable electricity and aid to increase the profitability of the renewable energy cycle.

scholarly journals Linear and non-linear optimization models for the selection of investment portfolio

2021 ◽  
Vol 47 ◽  
Author(s):  
Sigutė Vakrinienė ◽  
Gintautas Misevičius
Keyword(s):  
Linear Programming ◽  
Risk Evaluation ◽  
Statistical Data ◽  
Linear Optimization ◽  
Investment Portfolio ◽  
Non Linear Programming ◽  
Non Linear ◽  
The Baltic ◽  
Investment Portfolios ◽  
Selection Of

This research suggests a maxmin model for the selection of investment portfolios. The risk evaluation coefficients are introduced. The components of portfolio are found by solving linear programming task in onemodel and non-linear programming task in the other.  In the experimental part of the research ineffective portfolios exerted from these models are tested referring to the statistical data of the Baltic stock market. Realizations of the suggested portfolios with different risk coefficient values are compared to realizations of effective (Pareto optimal) portfolios.

Machine Learning and Financial Investing

2012 ◽  
pp. 1687-1697
Author(s):  
Jie Du ◽  
Roy Rada
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Evolutionary Computation ◽  
Evolutionary Methods ◽  
Knowledge Based ◽  
Key Features ◽  
Selection Of

This chapter presents the case for knowledge-based machine learning in financial investing. Machine learning here, while it will exploit knowledge, will also rely heavily on the evolutionary computation paradigm of learning, namely reproduction with change and selection of the fit. The chapter will begin with a model for financial investing and then review what has been reported in the literature as regards knowledge-based and machine-learning-based methods for financial investing. Finally, a design of a financial investing system is described which incorporates the key features identified through the literature review. The emerging trend of incorporating knowledge-based methods into evolutionary methods for financial investing suggests opportunities for future researchers.

Matching and portfolio selection: Part 2

1987 ◽  
Vol 114 (3) ◽  
pp. 551-568 ◽  
Author(s):  
A. J. Wise
Keyword(s):  
Pension Fund ◽  
General Criterion ◽  
Actuarial Approach ◽  
Subsequent Discussion ◽  
The One ◽  
Investment Portfolios ◽  
Main Ideas ◽  
Cross Reference ◽  
Selection Of

8.1 This paper is concerned with the selection of investment portfolios to meet specified criteria which involve the liabilities of a long term investing institution such as a pension fund or a life office. In Part 1 (5) I showed a certain relationship between, on the one hand, portfolios selected according to a criterion of pure matching to the liabilities and, on the other hand, portfolios selected according to a more general criterion of ‘efficiency’. This connection points to a particular actuarial approach to the selection of portfolios, which is now further examined in Part 2.8.2 Writing Part 2 separately presents an opportunity to re-state the main ideas. The next few paragraphs recapitulate the basic points with a view to reducing, within the subsequent discussion, the amount of cross-reference to Part 1, and to the three preceding papers on which the study is based.

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