ISSN: 2225-8329
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Many researchers have used a small number of portfolios extracted from a large volume of assets and, therefore, the problem was that these portfolios were first grouped, according to certain characteristics, variables and then these portfolios thus rearranged can ensure that a constant characteristic is maintained for a portfolio that refers to the stops it includes. A series of issues are then presented to identify the fact that a grouping of portfolios is an activity with enormous significance and utility, in the sense that it eliminates the discrepancies, ensures homogeneity of the groups in which the respective portfolios are constituted and thus can be reached. the fact that in the realization of portfolios the idiosyncratic volatility is reduced and it is possible to obtain more accurate estimates of the action of the factors considered in the equations used. Another aspect that is taken into consideration is that it is sometimes observed that portfolio weights are not always deterministic, as they are calculated from a previous data sample or from previous data samples. In this context, the problem arises to recalculate or calculate the market capitalization at the basic moment of the observed price, the number of outstanding shares and then sort those entities and portfolios based on a market ceiling, which is now deterministic. The relationships used are presented and explained in their essence. Time is a determining factor that is largely explicit, using some data and graphical representations, which we have considered. Some critical appraisals are also expressed in connection with CAPM testing, pointing out that not always positive portfolios in parameter space is like looking for linear numbers with arithmetic progression, which is not always the case. The active or passive management of the portfolio is another problem that has been solved within this exposure, in the mentioned article.
1. Adam, Al., Houkari, M., Laurent, J. P. (2008). Spectral risk measures and portfolio selection. Journal of Banking & Finance, 32, 1870–1882
2. Aldrich, E.M., Kung, H. (2009). Computational Methods for Production-Based Asset Pricing Models with Recursive Utility. Economic Research Initiatives at Duke, Working Paper Number 87
3. Amenc, N., Le Sourd, V. (2003). Theorie du Portefeuille et Analyse de sa Performance, Editura Economica, Paris
4. Anghel, M. G., Iacob, ?. V., Dumitru, D., Popovici, M. (2019). Dynamic models used in analysis capital and population. Theoretical and Applied Economics, XXVI (4) (621), Winter, 149-162
5. Anghel, M. G., Bodo, G., Bartek, O. (2016). Model of Static Portfolio Choices. Romanian Statistical Review Supplement, 1, 53-57
6. Anghelache, C., Anghel, M. G., Sacal?, C. (2016). The financial sector influence on portfolio dynamics. Romanian Statistical Review Supplement, 7, 9-13
7. Anghelache, C., Sacal?, C., Stanciu, E. (2015). Regression models using the instrumental variables. Romanian Statistical Review Supplement, 9, 138 – 146
8. Anghelache, C., Anghel, M. G. (2014). Using the regression model in the analysis of financial instruments portfolios, Procedia Economics and Finance, 10, 324-329, The 7th International Conference on Applied Statistics
9. Anghelache, C., Anghelache, G. V. (2009). Utilization of the econometric models in the analysis of the assets rates. Metalurgia International, XIV (9), Special Issue, Editura ?tiin?ific? F.M.R.
10. Badiu, A., Iacob, ?. V., Grigorescu, D. L. (2019). Studiu privind reducerea absen?ei riscului ?i a pruden?ei/ Study on reducing the absence of risk and prudence. Romanian Statistical Review Supplement, 10, 176-184
11. Hagstromer, B., Binner, J. M. (2009). Stock portfolio selection with full-scale optimization and differential evolution. Applied Financial Economics, 1559–1571
12. Iacob, ?. V. (2019). Utilizarea metodelor statistico-econometrice ?i econofizice în analize economice, Editura Economic?, Bucure?ti
13. Justiniano, A., Primiceri, G. E., Tambalotti, A. (2010). Investment Shocks and Business Cycles. Journal of Monetary Economics, 57, 132 – 145
14. Kini, O., Mian, S., Rebello, M., Venkateswaran, A. (2009). On the Structure of Analyst Research Portfolios and Forecast Accuracy. Journal of Accounting Research, 47 (4), 867-909
15. Liu, J. (2007). Portofolio selection in stochastic environments. The Review of Financial Studies, 20 (1), 1-39
16. Lucas, A., Siegmann, A. (2008). The Effect of Shortfall as a Risk Measure for Portfolios with Hedge Funds. Journal of Business Finance & Accounting, 35 (1) & (2), 200–226
17. Okhrimenko, O., Manaenko, I. (2014). Determinants of Investment Support: Principles, Mechanisms, Efficiency. Economics of Development, 69 (1), 34-40.
To cite this article: Anghel, M.-G., Ha?egan, D.-A., Popovici, M. (2020). Model for Analyzing the Significance and Usefulness of Grouping Portfolios, International Journal of Academic Research in Accounting, Finance and Management Sciences 10 (1): 229-238.
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