A Neural Stock Price Predictor using Quantitative Data
نویسندگان
چکیده
Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. Financial forecasting employing neural networks is a highly significant area for exploratory study. It has long been known that exact prediction is more of an art than a science but attempts can be made to recognize trends and patterns which should help in predicting correctly within an order of magnitude. Our approach uses a typical back propagation neural net and employs various formulas on quantitative data. We picked our training stocks from different categories having varying prices and volumes; this enabled a better analysis while generalizing our findings. While it has not been possible to provide exact predictions, a definite trend is evident in most cases. Statistically-oriented projections of the significance of these findings using standard regression analysis techniques show our approach to be simple yet effective. The historical data was obtained over a time period of 40 days for major stocks on New York Stock Exchange (NYSE). This data was used to train the network while trying out various parameter values and techniques which are discussed below
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