The paper presents the scheme for preliminary processing of historical records, provides the algorithm for historical sample length optimization, estimates expected returns and risks by prognostic models of optimal complexity, drawing on the principle of integrated decisions. The author offers a new method to generate efficient portfolios taking into account cross correlation links and construction of investment portfolio of optimal complexity based on the principle of integrated decisions.
This article addresses the development and testing of 'Return-Risk' model aimed at designing the investment strategies suitable for practical needs. The model rests on fundamental principles of portfolio analysis and incorporates the following properties: expected return and risk are derived from historical data while learning sample size and time span are set by practice requirements in such a way as to maintain efficiency and regular monitoring of investment portfolio. The model tested on independent material (data of 2017 were not used for the model) shows: the suggested method of moving verification results in higher forecast accuracy for return and risk of investment portfolio and, consequently, in higher quality of investment decisions. The model annual yeild in moving verification mode is 25%, whereas S&P500 index shows only 15% of the annual gain, i.e. the 'Return-Risk' model significantly beats the market. The win-loss ratio of deals (time spans) is 11:1.
The article provides quantitive evaluation of the world stock markets with the «Return-Risk» Model, which is based on the fundamental principles of the porfolio theory. The analysis undertaken is aimed at revealing the most attractive world stock markets in regard to shaping of the future investment policy in the short term as well as determining the countries which securities (stocks, bonds, financial derivatives, etc.) should be included into the extended diversified investment portfolio. In other words, the world stock markets under study are not only considered as the status displays of the national economies of the corresponding countries but also as the potential instruments for investment portfolios. The synthesized «Return-Risk» Model is applicable in investment practice. The model shows that the attempt of 1% increase in return leads to the increase of risk by 3%. The computing experiments to testify the proposed model on independent material (verification of the model with permanent structure based on sliding verification) proved its practical applicability for revealing the leading indexes and dynamics estimate of their return within the closest investment horizon, with the win-loss ratio accounting for over three to one (3,2:1). The average monthly return is 1,1% per tool.
The article offers the evaluation of investment performance in stock markets, money markets, product markets and others. The comparison is based on the «Return-Risk» Model grounded on the fundamental principles of H. Markowitz's portfolio analysis. The quantitative comparison is conducted on return-risk ratio as well as return-risk variability intervals. In the formed group of leaders, the return-risk ratio was minimal (0,12) for the money market, and maximal (0,43) for the US stock market. The patterns in focus were grouped as follows: 1. High risk aversion: US branch analysis, currency index models, currency index models (sell) and intercontinental branch analysis.2. Medium risk aversion: US stock market, world stick indexes, precious metals market and exchange goods as a whole.3. Low risk aversion: Russia's stock market. The practical evaluation of «Return-Risk» Models received for various investment patterns proved their practical validity. The empirical estimators of investment horizons computed for various groups of investment tools are of practical use.
