Abstract
In this chapter, neural networks are used to predict the future stock prices and develop a suitable trading system. Wavelet analysis is used to de-noise the time series and the results are compared with the raw time series prediction without wavelet de-noising. Standard and Poor 500 (S&P 500) is used in experiments. We use a gradual data sub-sampling technique, i.e., training the network mostly with recent data, but without neglecting past data. In addition, effects of NASDAQ 100 are studied on prediction of S&P 500. A daily trading strategy is employed to buy/sell according to the predicted prices and to calculate the directional efficiency and the rate of returns for different periods. There are numerous exchange traded funds (ETF’s), which attempt to replicate the performance of S&P 500 by holding the same stocks in the same proportions as the index, and therefore, giving the same percentage returns as S&P 500. Therefore, this study can be used to help invest in any of the various ETFs, which replicates the performance of S&P 500. The experimental results show that neural networks, with appropriate training and input data, can be used to achieve high profits by investing in ETFs based on S&P 500.
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Wang, L., Gupta, S. (2013). Neural Networks and Wavelet De-Noising for Stock Trading and Prediction. In: Pedrycz, W., Chen, SM. (eds) Time Series Analysis, Modeling and Applications. Intelligent Systems Reference Library, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33439-9_11
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DOI: https://doi.org/10.1007/978-3-642-33439-9_11
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