A performance study of anevolutionary algorithm for twopoint stock forecasting

Abstract: This study was conducted to conclude whether or not it was possible to accurately predict stock behavior by analyzing general patterns in historical stock data. This was done by creating an evolutionary algorithm that learned and weighted possible outcomes by studying the behaviour of the Nasdaq stock market between 2000 and 2016 and using the result from the training to make predictions. The result of testing with varied parameters concluded that clear patterns could not reliably be established with the suggested method as small adjustments to the measuring dates yielded wildly different results. The results also suggests that the amount of data is more relevant than how closely the stocks are related for the performance and that less precise predictions performs better than predicting multiple degrees of change. The performance of the seemingly better setting was shown to perform worse than random predictions but research with other settings might yield more accurate predictions.