Abstract
The price prediction problem for any given stock has been an object of study, deepening and evolution within the past few decades, to achieve the basic goal of positive financial realizations with the smaller risk possible. A known risk minimization strategy is a high frequency trading regime, taking benefit from minor price variations, and achieving small profits multiple times a day. By the hypothesis and the interpretation that prices variations behavior as a Random Walk, it is possible to characterize a Markov (stochastic) process with states and transition probabilities known, allowing to describe price variations through time. Therefore, this study aims to estimate the financial outcome using Markov chains for automated decision making in a high frequency trading strategy, and then comparing the computed results with stock’s valuation (or devaluation) within the same analyzed period.
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