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ABSTRACT

There are many methods for analyzing patterns in time-series data. Although stock data represents a time series, there are few studies on pattern analysis and prediction stock price dynamics in the field of computer science. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we calculate randomness of stock price changes using Kolmogorov Complexity. It is related to the accuracy of stock prediction using semi-global alignments. We use stock price data of 690 firms listed on the Korea stock Exchange (KRX) during 28 years for our experiments and to evaluate our methodology. When Kolomogorov Complexity is high we cannot predict accurately stock prices; while Kolomogorov Complexity is low, we can predict stock prices accurately. However, the prediction ratio of stock price changes of interest to investors, is 12% for short-term predictions and 54% for long-term predictions.

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INDEX TERMS

Keywords:
pattern analysis, stock prediction