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Abstract: Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the... (Update)
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...only concerned at how good the forecasts fit their target. In order to increase the forecastability in terms of profit earning, Yao [23] proposes a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error,...
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BibTeX entry: (Update)
J. T. Yao, C. L. Tan, "Time dependent Directional Profit Model for Financial Time Series Forecasting", Proceedings of The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, 2427 July 2000, Volume V, pp291-296. http://citeseer.ist.psu.edu/yao00time.html More
@misc{ yao00time,
author = "J. Yao and C. Tan",
title = "Time dependent Directional Profit Model for Financial Time Series Forecasting",
text = "J. T. Yao, C. L. Tan, Time dependent Directional Profit Model for Financial
Time Series Forecasting, Proceedings of The IEEE-INNS-ENNS International
Joint Conference on Neural Networks, Como, Italy, 2427 July 2000, Volume
V, pp291-296.",
year = "2000",
url = "citeseer.ist.psu.edu/yao00time.html" }
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