Economic Analysis: Theory and Practice

Abstracting and Indexing

Referativny Zhurnal VINITI RAS
Google Scholar

Online available



Cyberleninka (12 month OA embargo)

Portfolio analysis in a random environment of stock market alternative opportunities

Vol. 18, Iss. 12, DECEMBER 2019

Received: 1 October 2019

Received in revised form: 11 October 2019

Accepted: 23 October 2019

Available online: 25 December 2019


JEL Classification: C25, C32, C61

Pages: 2356–2370

Baumanis V.I. Riga Stradins University, Riga, Republic of Latvia

Subject Within the theory of portfolio investment, specialists often discuss the issue related to the lack of spatial dimension of investment opportunities. Only two measures are assumed to be sufficient to describe portfolio investing, i.e. profitability and risk. On the one hand, the prevailing practice of using these two characteristics is in line with the goal of reducing the computational complexity of portfolio analysis procedures and making it easier to formalize a number of related tasks. On the other hand, the forced refusal to reproduce the real processes of the stock market multidimensionality clearly constrains the application of methods that would greatly expand the possibilities of modern portfolio analysis.
Objectives The study aims to develop an econometric analogue model of random wandering and an application in the task of choosing an efficient Markowitz portfolio.
Methods I employ data analysis and machine learning techniques.
Results The paper shows the results of portfolio solutions in a random environment of alternative opportunities of the stock market. The linear nature of relationship between the return on equity and the yield of the market is replaced by non-linear, which is reproduced by a separate regression model with a discrete dependent variable. On its basis, I present an econometric model of return on equity, which uses the probability of positive return. Under this model, preferences may simultaneously lead to higher returns and reduced risk.
Conclusions Stochastic volatility regression in portfolio analysis can significantly improve the efficiency of portfolio solutions.

Keywords: risk, random variable, Bernoulli distribution


  1. Markowitz H.M. Portfolio Selection. The Journal of Finance, 1952, vol. 7, no. 1, pp. 77–91. URL: Link
  2. Black F., Scholes M. The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 1973, vol. 81, iss. 3, pp. 637–654. URL: Link
  3. Merton R.C. Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 1973, vol. 4, iss. 1, pp. 141–183. URL: Link
  4. Balynin I.V. [Optimization of investment portfolio as part of practical implementation of a risk-based approach: A variety of methods and principles]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2016, no. 10, pp. 79–92. URL: Link (In Russ.)
  5. Borochkin A.A. [Managing the risk of stock market volatility and State economic policy uncertainty in international portfolio investment]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2017, vol. 10, iss. 7, pp. 790–804. (In Russ.) URL: Link
  6. Maleeva E.A., Bel'sner O.A., Kritskii O.L. [Securities portfolio selection using the risk margin]. Finansy i kredit = Finance and Credit, 2018, vol. 24, iss. 12, pp. 2708–2720. (In Russ.) URL: Link
  7. Tobin J. The Theory of Portfolio Selection. In: F.H. Hahn and F.P.R. Brechling (eds) The Theory of Interest Rates. London, MacMillan, 1965.
  8. Kolyasnikova E.R. [Building a portfolio based on different risk measures and investor's risk perception]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2017, vol. 16, iss. 8, pp. 1583–1596. (In Russ.) URL: Link
  9. Kroll Y., Levy H., Markowitz H.M. Mean‐Variance versus Direct Utility Maximization. The Journal of Finance, 1984, vol. 39, no. 1, pp. 47–61. URL: Link
  10. Sharpe W.F. A Simplified Model for Portfolio Analysis. Management Science, 1963, vol. 9, iss. 2, pp. 277–293. URL: Link
  11. Barros A.J.D., Hirakata V.N. Alternatives for Logistic Regression in Cross-Sectional Studies: An Empirical Comparison of Models That Directly Estimate the Prevalence Ratio. BMC Medical Research Methodology, 2000, vol. 3, pp. 1–13. URL: Link
  12. Fedorova E.A., Guzovskii Ya.E., Lukashenko I.V. [Evaluating the applicability of modified beta coefficient in the Russian stock market]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2017, vol. 16, iss. 11, pp. 2163–2176. (In Russ.) URL: Link
  13. Elton E.J., Gruber M.J., Urich T.J. Are Betas Best? The Journal of Finance, 1978, vol. 33, iss. 5, pp. 1375–1384. URL: Link
  14. Lee L. Identification and Estimation in Binary Choice Models with Limited (Censored) Dependent Variables. Econometrica, 1979, vol. 47, no. 4, pp. 977–996. URL: Link
  15. Cheng P.L., Deets M.K. Portfolio Returns and the Random Walk Theory. The Journal of Finance, 1971, vol. 26, iss. 1, pp. 11–30. URL: Link
  16. Evans J.L. The Random Walk Hypothesis, Portfolio Analysis and the Buy-and-Hold Criterion. The Journal of Financial and Quantitative Analysis, 1968, vol. 3, iss. 3, pp. 327–342. URL: Link
  17. Cohen K.J., Pogue J.A. An Empirical Evaluation of Alternative Portfolio-Selection Models. The Journal of Business, 1967, vol. 40, iss. 2, pp. 166–193. URL: Link
  18. Asaturov K.G. [Determinants of systematic risk: Evidence from the Russian stock market]. Finansy i kredit = Finance and Credit, 2017, vol. 23, iss. 23, pp. 1343–1363. (In Russ.) URL: Link
  19. Negomedzyanov Yu.A., Negomedzyanov G.Yu. [Extending the functionality of the VaR concept]. Finansy i kredit = Finance and Credit, 2016, no. 2, pp. 2–8. URL: Link (In Russ.)
  20. Head K., Mayer T. Market Potential and the Location of Japanese Investment in the European Union. Review of Economics and Statistics, 2004, vol. 86, iss. 4, pp. 959–972. URL: Link

View all articles of issue


ISSN 2311-8725 (Online)
ISSN 2073-039X (Print)

Journal current issue

Vol. , Iss.