Neural Networks and the Stock Market
Abstract: In the 19th century, gold diggers emigrated from Europe to North America with the hopes of a brighter future. Gold diggers today might look to the stock market with hopes of finding the key to incredible wealth. The introduction of neural networks has revolutionized data analysis and the possibility of using them to do significantly better than chance on the stock market is investigated in this report. The aim of the report is to investigate a short period after a stock drastically decreases in price with neural networks. Firstly, neural networks are constructed and trained to estimate price changes. Thereafter an investment strategy is constructed and evaluated. The results of the investigation is that it is possible, under some assumptions, to do significantly better than chance in the stock market. It appears to be possible to do this for all days from the third to the ninth after a drastic crash. The predictions are significantly better than chance, however probably not large enough to compensate for transaction costs. It appears that the gold diggers of the 21st century, operating on the stock market, have to wait a while longer. It is possible to guess significantly better than chance if the price will increase or decrease, however the difference might not be large enough to profit from the trades. The networks that are developed in this thesis are not good enough to give rise to sufficient profits on the stock market. However, maybe it is possible to do so with slight changes in the structures of the networks, and thus earn incredible profits by investing wisely on the stock market?
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