Abstract
The goal of a stock prediction algorithm is to recommend a portfolio of stocks that will maximize an investor’s return. The investor has a finite amount of money and wants to create a portfolio to maximize her or his return on investment. The neural network in this chapter will predict the behavior of a portfolio of stocks given its history. This could then be used to select a portfolio of stocks with some idea of the future performance. The stock market model used in this chapter is based on Geometric Brownian Motion. Given that, we could do statistical analysis that would allow us to pick stocks. We’ll show that a neural net, which does not have any knowledge of the model, can do as well in modeling the stocks.
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Paluszek, M., Thomas, S., Ham, E. (2022). Stock Prediction. In: Practical MATLAB Deep Learning. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7912-0_10
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DOI: https://doi.org/10.1007/978-1-4842-7912-0_10
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