Using Markov Chains in Prediction of Stock Price Movements: A Study on Automotive Industry

Stock price prediction is on the agenda of most researchers based on the uncertainty in its nature. In past two decades, the literature on the development of prediction models for stock prices has extended dramatically. These studies mostly focused on specific industries such as banking and finance, petroleum, manufacturing, and automotive. In line with prior studies, the aim of this study is also to investigate the efficiency of Markov Chains Model, which is one of the most commonly applied models, in predicting the stock price movements for the firms operating in automotive industry and to reveal the possible contribution it can make to the decision making process of investors. Automotive industry is not only a major and industrial force worldwide, but also is a locomotive power that serves to many other industries. Thus, this study considers the firms operating in automotive industry and daily closing stock prices of all 13 automotive companies are collected for the calendar year of 2015. By defining three possible states (decrease, increase, and no change), individual state transition probability matrixes are formed for each company. Then, using the probabilities provided with these matrixes, different investment strategies are evaluated for the first five working days of 2016. According to the results of analysis, it is concluded that applying Markov Chains generates a positive income or at least minimizes the loss