+7 925 966 4690, 9am6pm (GMT+3), Monday – Friday
ИД «Финансы и кредит»

JOURNALS

  

FOR AUTHORS

  

SUBSCRIBE

    
Financial Analytics: Science and Experience
 

Ranking framework for portfolio selection

Vol. 15, Iss. 3, SEPTEMBER 2022

Received: 3 September 2019

Received in revised form: 16 September 2019

Accepted: 30 September 2019

Available online: 30 August 2022

Subject Heading: MATHEMATICAL ANALYSIS AND MODELING IN ECONOMICS

JEL Classification: C58, G23

Pages: 354–372

https://doi.org/10.24891/ea.18.11.2172

Dmitrii G. LEVSHUK Polotsk State University (PSU), Novopolotsk, Vitebsk Oblast, Republic of Belarus
levshuk81@rambler

https://orcid.org/0000-0001-5725-8633

Subject. The stock market is characterized by a significant variety of economic processes. Classical methods for modeling the time series to analyze and forecast processes in the stock market often produce unsatisfactory results. The article investigates and encourages new approaches to projections in the stock market in the face of uncertainty.
Objectives. The aim is to develop a technique to examine rank decisions in portfolio analysis.
Methods. In the research process, I used data analysis and machine learning methods.
Results. I examined the procedure for ranking portfolio solutions. This procedure rests on replacing optimization with a preference analysis commonly used in expert data processing. The replacement is possible due to the use of a special equation that includes the probability of positive yield as a factor.
Conclusions. I suggest using probabilistic preference technique based on the auto predictive model, instead of optimization approach to portfolio selection. The main result of this technique is rank portfolio formation that expands opportunities of portfolio analysis.

Keywords: probability, portfolio, binary choice

References:

  1. Markowitz H.M. Portfolio Selection. The Journal of Finance, 1952, vol. 7, no. 1, pp. 77–91. URL: Link
  2. Tobin J. The Theory of Interest Rates. London, MacMillan, 1965.
  3. 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
  4. Sharpe W.F. A Simplified Model for Portfolio Analysis. Management Science, 1963, vol. 9, iss. 2, pp. 277–293. URL: Link
  5. 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
  6. Bera A.K., Ivliev S., Lillo F. Financial Econometrics and Empirical Market Microstructure. Switzerland, Springer International Publishing, 2015, 284 p.
  7. Greene W.H. Econometric Analysis. New York, Macmillian Publishing Company, 2000, 1004 p.
  8. 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
  9. Cox D.R., Snell E.J. Analysis of Binary Data. London, Chapman and Hall, 1989, 441 p.
  10. Cox J.C., Ross S.A., Rubinstein M. Option Pricing: A Simplified Approach. Journal of Financial Economics, 1979, vol. 7, iss. 3, pp. 229–263. URL: Link90015-1
  11. Cox J.C., Ingersoll J.E., Ross S.A. A Theory of the Term Structure of Interest Rates. Econometrica, 1985, vol. 53, no. 2, pp. 385–407. URL: Link
  12. Campbell J.Y. Asset Pricing at the Millennium. The Journal of Finance, 2000, vol. 55, no. 4, pp. 1515–1567. URL: Link
  13. 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
  14. 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.)
  15. 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
  16. 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
  17. 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.)
  18. Suleimanova D.Yu. Issledovanie vremennykh ryadov s pomoshch'yu ekonometricheskogo analiza i tekhnicheskogo analiza na rynke Foreks: monografiya [Investigating the time series using the econometric analysis and technical analysis in the Forex market: a monograph]. Moscow, RUSAINS Publ., 2018, 150 p.
  19. Gerfin M. Parametric and Semi-Parametric Estimation of the Binary Response Model of Labour Market Participation. Journal of Applied Econometrics, 1996, vol. 11, iss. 3, pp. 321–340. URL: Link
  20. Peters E. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. New York, John Wiley & Sons, 1994, 332 p.

View all articles of issue

 

ISSN 2311-8768 (Online)
ISSN 2073-4484 (Print)

Journal current issue

Vol. 15, Iss. 3
September 2022

Archive