by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95
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C++ Neural Networks and Fuzzy Logic
by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526 Pub Date: 06/01/95 |
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You can set up a similar function for x(t + h), the stock price at time t + h, and have a separate network computing it using the backpropagation paradigm. You will then be generating future prices of the stock and the future buy/sell signals hand in hand, but parallel.
Michitaka Kosaka, et al. (1991) report that they used time-series data over five years to identify the network model, and time-series data over one year to evaluate the model’s forecasting performance, with a success rate of 65% for turning points.
Michael Azoff in his book on time-series forecasting with neural networks (see references) creates neural network systems for predicting the S&P 500 index as well as for predicting chaotic time series, such as sunspot occurrences. Azoff uses feedforward backpropagation networks, with a training algorithm called adaptive steepest descent, a variation of the standard algorithm. For the sunspot time series, and an architecture of 6-5-1, and a ratio of training vectors to trainable weights of 5.1, he achieves training set error of 12.9% and test set error of 21.4%. This series was composed of yearly sunspot numbers for the years 1706 to 1914. Six years of consecutive annual data were input to the network.
One network Azoff used to forecast the S&P 500 index was a 17-7-1 network. The training vectors to trainable weights ratio was 6.1. The achieved training set error was 3.29%, and on the test set error was 4.67%. Inputs to this network included price data, a volatility indicator, which is a function of the range of price movement, and a random walk indicator, a technical analysis study.
Michael de la Maza and Deniz Yuret, managers for the Redfire Capital Management Group, suggest that risk-adjusted return, and not mean-squared error should be the metric to optimize in a neural network application for trading. They also point out that with neural networks, like with statistical methods such as linear regression, data facts that seem unexplainable can’t be ignored even if you want them to be. There is no equivalent for a “don’t care,” condition for the output of a neural network. This type of condition may be an important option for trading environments that have no “discoverable regularity” as the authors put it, and therefore are really not tradable. Some solutions to the two problems posed are given as follows:
Here is a sampling of resources compiled from trade literature:
NOTE: We do not take responsibility for any errors or omissions.
Particularly worthwhile is an excellent series of articles by consultant Murray Ruggiero Jr., in Futures magazine on neural network design and trading system design in issues spanning July ‘94 through June ‘95.
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