Automotive Science and Engineering
Iran University of Science & Technology
Sun, Aug 11, 2024
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Volume 13, Issue 4 (12-2023)
ASE 2023, 13(4): 4236-4242 |
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Ghanbari H, Shabani M, Mohammadi E. Portfolio Optimization with Conditional Drawdown at Risk for the Automotive Industry. ASE 2023; 13 (4) :4236-4242
URL: http://www.iust.ac.ir/ijae/article-1-647-en.html
URL: http://www.iust.ac.ir/ijae/article-1-647-en.html
Associate Professor, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (3726 Views)
Portfolio optimization is the process of distributing a specific amount of wealth across various available assets, with the aim of achieving the highest possible returns while minimizing investment risks. There are a large number of studies on portfolio optimization in various cases, covering numerous applications; however, none have focused exclusively on the automotive industry as one of the largest manufacturing sectors in the global economy. Since the economic activity of this industry has a coherent pattern with that of the global economy, the automotive industry is very sensitive to the booms and busts of business cycles. Due to the volatile global economic environment and significant inter-industry implications, providing an appropriate approach to investing in this sector is essential. Thus, this paper aims to provide an appropriate approach to investing in this sector. In this study, an extended Conditional Drawdown at Risk (CDaR) model with cardinality and threshold constraints for portfolio optimization problems is proposed, which is highly beneficial in practical portfolio management. The feature of this risk management technique is that it admits the formulation of a portfolio optimization model as a linear programming problem. The CDaR risk functions family also enables a risk manager to control the worst ( 1- α )×100% 
drawdowns. In order to demonstrate the effectiveness of the proposed model, a real-world empirical case study from the annual financial statements of automotive companies and their suppliers in the Tehran Stock Exchange (TSE) database is utilized.
drawdowns. In order to demonstrate the effectiveness of the proposed model, a real-world empirical case study from the annual financial statements of automotive companies and their suppliers in the Tehran Stock Exchange (TSE) database is utilized.
Keywords: Portfolio optimization, Conditional drawdown at risk, Downside risk measures, Cardinality constraint, Automotive industry
Type of Study: Research |
Subject:
Autonomous vehicles
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