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Research into the Modeling of Complex Systems
Predicting the Stock Market
John Sherwood
Period 2
Abstract
Methodology
Abstract
Abstract
Results and Findings
Early tests using short term predictions and primitive artificial intelligence 'stock bots' proved to be successfully, especially given their low level of sophistication; however this was probably more likely to fortunate fluctuations in the stock market than anything else. Despite that, it did lead to some useful findings for later prediction algorithms as described above.
In terms of the results of the generated prediction equations, I found that the equations created by my algorithms tended to become less and less accurate the further away they were used from the timeframe they were generated for, implying that the effect of news on stock prices is constantly changing over time.
In order to gain the information necessary to analyze stocks, an XML parser is used to read financial news based websites (Such as Google Finance and Reuters). The information garnered by these data miners is stored into a MySQL database so that it is accessible to other aspects of the program. The news data is then analyzed with a program that takes the qualitative news renderred in text and assigns a signed floating point number to it so that the regressor has a qualitative value to base its analysis on . This, along with the stock price data which is taken from CSV (comma-seperated-value) files provided by Yahoo! Finance, is fed into an equation refiner, which generates equations describing the effect of news over time on stock prices using a genetic algorithm to refine the equations. The equations are then used to project the future price of stocks.