Portfolio risk reduction: Optimising selection of resources projects by application of financial industry techniques

2001 ◽  
Vol 2001 (1) ◽  
pp. 1-4
Author(s):  
Noll Moriarty
Keyword(s):  
Risk Reduction ◽  
Portfolio Risk ◽  
Financial Industry ◽  
Selection Of

Risk-Based Selection of Portfolio

2015 ◽  
pp. 744-758
Author(s):  
Soma Panja ◽  
Dilip Roy
Keyword(s):  
Investment Decision ◽  
The Other ◽  
Portfolio Risk ◽  
Heuristic Solution ◽  
Value System ◽  
City Block ◽  
Optimum Decision ◽  
Optimum Solution ◽  
City Block Distance ◽  
Selection Of

This chapter examines the closeness between the optimum portfolio and portfolio selected by an investor who follows a heuristic approach. There may be basically two ways of arriving at an optimum portfolio – one by minimizing the risk and the other by maximizing the return. In this chapter, the authors propose to strike a balance between these two. The optimum portfolio has been obtained through a mathematical programming framework so as to minimize the portfolio risk subject to return constraint expressed in terms of coefficient of optimism (a), where a varies between 0 to 1. Simultaneously, the authors propose to develop four heuristic portfolios for the optimistic and pessimistic investors, risk planners, and random selectors. Given the optimum portfolio and a heuristic portfolio, City Block Distance has been calculated to measure the departure of the heuristic solution from the optimum solution. Based on daily security wise data of ten companies listed in Nifty for the years 2004 to 2008, the authors have obtained that when the value of a lies between 0 to 0.5, the pessimistic investor's decision is mostly closest to the optimum solution, and when the value of a is greater than 0.5, the optimistic investor's decision is mostly near to the optimum decision. Near the point a = 0.5, the random selectors and risk planners' solutions come closer to the optimum decision. This study may help the investors to take heuristic investment decision and, based on his/her value system, reach near to the optimum solution.

Real Estate Portfolio Risk Reduction through Intracity Diversification

1998 ◽  
Vol 4 (1) ◽  
pp. 35-41 ◽  
Author(s):  
Marvin Wolverton ◽  
Ping Cheng ◽  
William Hardin
Keyword(s):  
Real Estate ◽  
Risk Reduction ◽  
Portfolio Risk

scholarly journals Brief Communication: CATALYST – a multi-regional stakeholder think tank for fostering capacity development in disaster risk reduction and climate change adaptation

2014 ◽  
Vol 14 (8) ◽  
pp. 2157-2163 ◽  
Author(s):  
M. P. Hare ◽  
C. van Bers ◽  
P. van der Keur ◽  
H. J. Henriksen ◽  
J. Luther ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Reduction ◽  
Climate Change Adaptation ◽  
Disaster Risk Reduction ◽  
Capacity Development ◽  
Disaster Risk ◽  
Think Tank ◽  
The Caribbean ◽  
Set Up ◽  
Selection Of

Abstract. This brief communication presents the work and objectives of the CATALYST project on "Capacity Development for Hazard Risk Reduction and Adaptation" funded by the European Commission (October 2011–September 2013). CATALYST set up a multi-regional think tank covering four regions (Central America and the Caribbean, East and West Africa, the European Mediterranean, and South and Southeast Asia), intending to strengthen capacity development for stakeholders involved in disaster risk reduction (DRR) and climate change adaptation, in the context of natural hazards. This communication concludes with a selection of recommendations for capacity development in DRR and climate change adaptation from the perspective of governance issues.

Risk-Based Selection of Portfolio

2014 ◽  
pp. 222-237
Author(s):  
Soma Panja ◽  
Dilip Roy
Keyword(s):  
Investment Decision ◽  
The Other ◽  
Portfolio Risk ◽  
Heuristic Solution ◽  
Value System ◽  
City Block ◽  
Optimum Decision ◽  
Optimum Solution ◽  
City Block Distance ◽  
Selection Of

This chapter examines the closeness between the optimum portfolio and portfolio selected by an investor who follows a heuristic approach. There may be basically two ways of arriving at an optimum portfolio – one by minimizing the risk and the other by maximizing the return. In this chapter, the authors propose to strike a balance between these two. The optimum portfolio has been obtained through a mathematical programming framework so as to minimize the portfolio risk subject to return constraint expressed in terms of coefficient of optimism (a), where a varies between 0 to 1. Simultaneously, the authors propose to develop four heuristic portfolios for the optimistic and pessimistic investors, risk planners, and random selectors. Given the optimum portfolio and a heuristic portfolio, City Block Distance has been calculated to measure the departure of the heuristic solution from the optimum solution. Based on daily security wise data of ten companies listed in Nifty for the years 2004 to 2008, the authors have obtained that when the value of a lies between 0 to 0.5, the pessimistic investor's decision is mostly closest to the optimum solution, and when the value of a is greater than 0.5, the optimistic investor's decision is mostly near to the optimum decision. Near the point a = 0.5, the random selectors and risk planners' solutions come closer to the optimum decision. This study may help the investors to take heuristic investment decision and, based on his/her value system, reach near to the optimum solution.

Preliminary Analysis on Science for Global Safety with Reference to the Sendai Framework for Disaster Risk Reduction

2016 ◽  
Vol 11 (3) ◽  
pp. 421-424 ◽  
Author(s):  
Yasuhito Jibiki ◽  
Keyword(s):  
Risk Reduction ◽  
Preliminary Analysis ◽  
Disaster Risk Reduction ◽  
Present Report ◽  
Disaster Risk ◽  
Legal Systems ◽  
Land Usage ◽  
Sendai Framework ◽  
International Policies ◽  
Selection Of

The contexts in which “safe” and “safety” are used in the Sendai Framework for Disaster Risk Reduction are clarified to inform the selection of necessary elements in establishing science for global safety in relation to disaster risk reduction. The present report shows that “safe” and “safety” are used in the contexts of health, legal systems, housing, more assured provision of means of livelihood, and important infrastructure. From the perspective of the continuity of the Sendai Framework for Disaster Risk Reduction and preceding international policies, it is determined that the contexts of legal systems, health, and land usage are significant to establishing science for global safety.

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