Monitoring Stock Market Returns: A Stochastic Approach

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.