Optimum portfolio visualiser for risky assets using mean-variance model / Ruqayyah Apandi, Nur Suhailah Mohd Bakhtir and Nur Fatini Ramli

• Main Authors: Apandi, Ruqayyah, Mohd Bakhtir, Nur Suhailah, Ramli, Nur Fatini
• Format: Student Project
• Language: English
• Published: 2019
• Subjects: Mathematical statistics. Probabilities; Analytical methods used in the solution of physical problems
• Online Access: http://ir.uitm.edu.my/id/eprint/37287/1/37287.PDF; http://ir.uitm.edu.my/id/eprint/37287/

This research focuses in minimising the risk using mean risk model that was first introduced by Markowitz (1952) for solving portfolio selection problem. Thus, a variance is used as a risk measure in this project. The scenario returns were obtained based on the historical monthly returns from FBMKLCI. The mean-variance model and data set are being implemented in Microsoft Excel and there are different level of target returns which the optimal portfolios arc evaluated. Hence, the purpose of this study is to optimise portfolio of risky assets under different level of target return using mean-variance model. Next, to validate in-sample portfolios obtained using the out-of-sample analysis. The in-sample result shows that diversification allows us to reduce the risk of the portfolio without sacrificing potential returns and it also shows that the lower the target return, the lower the risk and the higher the target return, the higher the risk. Based on the out-of-sample analysis, when the expected realised return is low, it will give a low realised risk, when the expected realised return is medium, the realised risk will also be medium and when the expected realised return is high, the realised return is also high. Consequently, to develop user interface as an optimal portfolio visualiser. The user interface design is used to visualise the composition of portfolios and realised returns in graphicas view to help the user quickly absorb and interpret the presented result after they have entered the specific target return. Generally, based on the results that we obtained, we can conclude that mean-variance is applicable and widely used, as the method is easy to be calculated, but only favorable at low target return. If we were to design this study again, there are several changes that we would make. Most importantly we would go for a longer time period in order to create more scenario returns, include other types of data set, not only from FBMKLCI and to include more methodological work on how to robustly capture the impact and outcomes of different kind of risk measure in optimisation portfolio such as value-at-risk and also conditional value-at-risk.