Portfolio selection using the multiple attribute decision making model
-
DOIhttp://dx.doi.org/10.21511/imfi.18(2).2021.13
-
Article InfoVolume 18 2021, Issue #2, pp. 155-165
- 650 Views
-
296 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This paper uses a Multiple Attribute Decision Making (MADM) model to improve the out-of-sample performance of a naïve asset allocation model. Under certain conditions, the naïve model has out-performed other portfolio optimization models, but it also has been shown to increase the tail risk. The MADM model uses a set of attributes to rank the assets and is flexible with the attributes that can be used in the ranking process. The MADM model assigns weights to each attribute and uses these weights to rank assets in terms of their desirability for inclusion in a portfolio. Using the MADM model, assets are ranked based on the attributes, and unlike the naïve model, only the top 50 percent of assets are included in the portfolio at any point in time. This model is tested using both developed and emerging market stock indices. In the case of developed markets, the MADM model had 24.04 percent higher return and 53.66 percent less kurtosis than the naïve model. In the case of emerging markets, the MADM model return is 90.16 percent higher than the naïve model, but with almost similar kurtosis.
- Keywords
-
JEL Classification (Paper profile tab)F21, G11, G15
-
References25
-
Tables2
-
Figures0
-
- Table 1. Averages of decision criteria for the out-of-sample period
- Table 2. Properties of the out-of-sample period excess returns for various portfolio strategies
-
- Ang, A., Chen, J., & Xing, Y. (2006). Downside risk. The Review of Financial Studies, 19, 1191-1239.
- Banerjee, A. N., & Hung, C. D. (2013). Active momentum trading versus passive 1/N naive diversification. Quantitative Finance, 13(5), 655-663.
- Brown, S. J., Hwang, I., & In, F. (2013). Why optimal diversification cannot outperform naïve diversification: Evidence from tail risk exposure (Working paper). New York University.
- Chen, T. Y., & Li, C. H. (2010). Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis. Information Sciences, 180(21), 4207-4222.
- DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naïve diversification: How inefficient is the 1/N portfolio strategy? The Review of Financial Studies, 22, 1915-1953.
- Disatnik, D., & Katz, S. (2012). Portfolio optimization using a block structure for the covariance matrix. Journal of Business Finance & Accounting, 39, 806-843.
- Doan, P., Lin, C. T., & Zurbruegg, R. (2010). Pricing assets with higher moments: Evidence from the Australian and US stock markets. Journal of International Financial Markets, Institutions and Money, 20, 51-67.
- Galagedera, D. U. A., & Brooks, R. D. (2007). Is co-skewness a better measure of risk in the downside than downside beta? Evidence in emerging market data. Journal of Multinational Financial Management, 17, 214-230.
- Harvey, C. R., & Siddique, A. (1999). Autoregressive conditional skewness. Journal of Financial and Quantitative Analysis, 34, 465-487.
- Harvey, C. R., & Siddique, A. (2000). Conditional skewness in asset pricing tests. Journal of Finance, 55, 1263-1295.
- Hwang, S., & Satchell, S. E. (1999). Modelling emerging market risk premia using higher moments. International Journal of Finance and Economics, 4, 271-296.
- Hwang, I., Xu, S., & In, F. (2018). Naive versus optimal diversification: Tail risk and performance. European Journal of Operational Research, 265, 372-388.
- Ibbotson, R. G. (1975). Price performance of common stock new issues. Journal of Financial Economics, 2, 235-272.
- Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48, 65-91.
- Jegadeesh, N., & Titman, S. (2001). Profitability of momentum strategies: An evaluation of alternative explanations. The Journal of Finance, 56, 699-720.
- Kritzman, M., Page, S., & Turkington, D. (2010). In defense of optimization: The fallacy of 1/N. Financial Analysts Journal, 66, 31-39.
- Mandelbrot, B., & Taylor, H. M. (1967). On the distribution of stock price differences. Operations Research, 15, 1057-1062.
- Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7, 77-91.
- Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. New York: Wiley.
- Merton, Robert C. (1980). On estimating the expected return on the market. Journal of Financial Economics, 8, 323-361.
- Murtazashvili, I., & Vozlyublennaia, N. (2013). Diversification strategies: Do limited data constrain investors? Journal of Financial Research, 36, 215-232.
- Prakash, A., Chang, C. H., & Pactwa, E. (2003). Selecting a portfolio with skewness: Recent evidence from US, European, and Latin America equity markets. Journal of Banking and Finance, 27, 1375-1390.
- Platanakis, E., Sutcliffe, C. M., & Ye, X. (2019). Horses for courses: Mean-variance for asset allocation and 1/N for stock selection.
- Platen, E., & Rendek, R. (2012). Approximating the numéraire portfolio by naive diversification. Journal of Asset Management, 13, 34-50.
- Wang, Y., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51, 1-12.