Detección de caminata aleatoria en precios bursátiles mediante cadenas de Markov: aplicación al Índice de Precios y Cotizaciones de México

Abstract

The Random Walk (weak form efficient market) hypothesis is of vital importance in economics and finance to explain the behaviour of asset prices. Several authors have examined the validity and conditions under which the hypothesis holds. Most of the techniques and models used, rely on runs and serial correlation tests, however test using Markov chains are rare. Most Markov chains applications perform an stratification of returns defining the structure of the state space. The aim of this research is to detect the presence of random walk in stock market returns using Markov chains. The chain states are defined as the run lengths the process can develop. The concept of cycles is also introduced modelling the process in a more concretely. Conclusions are drawn analysing stationarity of the steady state probability distributions under diverse scenarios. The Mexican stock market daily closing prices index is analysed, covering a 16-year period, finding that the random walk is not present. This result is corroborated applying conventional random walk hypothesis tests.