Possible Applications Of Genetic Algorithms In The Time Series Analysis, Using Stock Market Data

In the decision making process the forecasting and time series analysis are important, but unfortunately the reliability of the prediction is often questionable. In today's rapidly changing business environment, it is crucial that decisions are based on correct information which means a better estimate of the expected economic developments. In this paper I examine the possible applications of using genetic algorithms in time series analysis to improve the reliability of the forecast. I try to submit the most relevant findings in the field of genetic algorithms and forecasting. My goal is to give a thorough description about the possible applications of genetic algorithms (GA) and I like to prove that this method can be useful in the time series analysis. The literature review is focused only to the prediction of stock market data. First I summarize shortly the most important methods of time series analysis, then I introduce the genetic algorithm and its main steps. The essential of the paper is the literature review, where I try to describe the most important applications of GA in finance. There are lots of interesting results in the forecasting of stock market data, which makes the GA more important. Of course the GA model is not perfect, it has some shortcomings and limitations of application. After drawing the conclusionsI hope this study will help the reader to understand better the genetic algorithm and its significance in the forecast.