• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

The Effectiveness of the Dogs of the Dow Investment Strategy in Emerging Capital Markets

Student: Ozerov Nikita

Supervisor: Tamara Teplova

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2021

This paper will describe, analyze and empirically test the "Dogs of the Dow" investment strategy based on the theory of signal dividend payments. When using this strategy, the portfolio includes shares with the maximum dividend yield, which belong to the category of so-called value shares, since they are significantly undervalued by the market and are traded at a discount in relation to their fair intrinsic value. Moreover, the "Dogs of the Dow" strategy will be tested on data from two of Asia's leading emerging markets - Taiwan and China, which will be in the focus of investors' attention in the current year. The novelty of this work is the implementation of the portfolio optimization procedure based on the provisions of the modern portfolio theory. This procedure will be carried out using the Python programming language using Monte Carlo simulations.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses