Proposal of creation of a portfolio with minimal risk
-
DOIhttp://dx.doi.org/10.21511/imfi.14(2).2017.10
-
Article InfoVolume 14 2017, Issue #2, pp. 107-115
- Cited by
- 1081 Views
-
231 Downloads
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
The aim of this work is to propose a method for creating portfolios with a minimal expected risk. The proposed method consists of two steps. In the first step, the authors use a method for finding a minimum spanning tree. It is a graph theory tool, which is the field of discrete mathematics. Graph is defined as a set of vertices and edges. By this method the authors distribute assets, for example a stock index, into several subgroups. From each group it is then chosen an asset, from which most of the edges come out. These selected assets will be used to create a portfolio. In the second step, the authors will use a method of minimizing the standard deviation of the portfolio to calculate the weight of its assets. By this method, first it is found the weight of each asset so that the resulting portfolio would have the lowest possible expected risk. Then the authors find the portfolio with the lowest possible expected risk at required yield and create investment strategies. These strategies are compared during the time and between each other based on the variation coefficient. The article can be a practical guide for an individual investor during the minimal risk portfolio creation and shows him, which assets (and which asset weights) of the selected index to purchase.
- Keywords
-
JEL Classification (Paper profile tab)G11
-
References12
-
Tables5
-
Figures1
-
- Figure 1. Comparison between minimum spanning trees of 5 and 30-year historical yield with color-coded sectors
-
- Table 1. Criterion for classification of companies into different price histories
- Table 2. The representatives of each sector according to time period
- Table 3. Results analysis for the 5-year historical yield
- Table 4. Analysis results for the 30-year historical yield
- Table 5. Riskiness of individual portfolios measured by the variation coefficient
-
- Bonanno, G., Caldarelli, G., Lillo, F., Micciché, S., Vandewalle, N., and Mantegna, R. N. (2004). Networks of Equities in Financial Markets. The European Physical Journal B, 38(2), 363-371.
- Csardi, G., and Nepusz, T. (2006). The igraph software package for complex network research. International Journal, Complex Systems 1695.
- Fernández, A., and Gómez, S. (2007). Portfolio selection using neural networks, Computers and Operations Research, 34(4), 1177- 1191.
- Goo, Y., Chen, D., and Chang, Y. (2007). The application of Japanese candlestick trading strategies in Taiwan. Investment Management and Financial Innovations, 4(4), 49-79.
- Hadi, A. S., El Naggar, A. A., and Abdel Bary, M. N. (2016). New model and method for portfolios selection. Applied Mathematical Sciences, 10(6), 263-288.
- Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B, 11, 193-197.
- Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
- Markowitz, H. M. (1959). Portfolio Selection: Efficient Diversification of Investments. London: Yale University Press, 344 p.
- Raisová, M., Užik, M., and Hoffmeister, C. M. (2016). Normal and reverse stock splits in the V4 countries, Investment Management and Financial Innovations, 13(4), 94-105.
- Sharpe, W. F. (1992). Asset Allocation: Management Style and Performance Measurement, The Journal of Portfolio Management, 18(2), 7-19.
- Šoltés, V., Šoltés, M. (2003). Analysis of two-asset portfolio. E&M Ekonomie a Management, 6, 63-65.
- Šoltés, M. (2012). Theoretical Aspects of Three-Asset Portfolio Management. CurentulJuridic, 15(4), 130-136.