YAP3: improved detection of similarities in computer program and other texts
SIGCSE '96 Proceedings of the twenty-seventh SIGCSE technical symposium on Computer science education
Sim: a utility for detecting similarity in computer programs
SIGCSE '99 The proceedings of the thirtieth SIGCSE technical symposium on Computer science education
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Temporal pattern matching for the prediction of stock prices
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Symbolic analysis of indicator time series by quantitative sequence alignment
Computational Statistics & Data Analysis
A Phylogenetic Analysis for Stock Market Indices Using Time-Series String Alignments
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
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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.