Original scientific paper
https://doi.org/10.17535/crorr.2022.0005
Monitoring Stock Market Returns: A Stochastic Approach
Filip Peovski
orcid.org/0000-0002-7988-7403
; Faculty of Economics - Skopje, Ss. Cyril and Methodius University in Skopje
Violeta Cvetkoska
; Faculty of Economics - Skopje, Ss. Cyril and Methodius University in Skopje
Predrag Trpeski
; Faculty of Economics - Skopje, Ss. Cyril and Methodius University in Skopje
Igor Ivanovski
; Faculty of Economics - Skopje, Ss. Cyril and Methodius University in Skopje
Abstract
Financial analysis plays a major role in investing the disposable income of various economic agents. Stock markets are predominantly made up of small investors with limited information and low
capabilities for a suitable analysis. Researchers, as well as practitioners, are divided over the findings on the adequacy of technical analysis in investing. This paper examines the Markov chain process
in the stock market to discover the essential links and probabilities for the stocks’ transition through three states of stagnation, growth, and decline (i.e., stagnant, bull, and bear markets). The subject of
analysis is a randomly selected portfolio of 20 shares traded on the New York Stock Exchange. The data suggest that the portfolio relatively quickly, in four trading days, achieves equilibrium probabilities
that allow a certain amount of predictability of future movements. At the same time, when analyzing the expected time intervals for the first transition, we found that the portfolio returns to a state of
growth much faster than a decline. In addition, the results negate the basic habits of frequent trading, herding, and taking a short position in events of negative price fluctuations. Our research contributes
towards observing regularities and stock market efficiency with a clear goal of improving expectations and technical analysis for small individual investors.
Keywords
Markov processes; operational research; stock market; portfolio theory
Hrčak ID:
280264
URI
Publication date:
12.7.2022.
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