ISSN:
1433-3058
Keywords:
Event-knowledge
;
Forecasting
;
Neural networks
;
Selective presentation learning
;
Stockprice prediction
;
Stopping criterion
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract This paper proposes a selective presentation learning technique for improving the learnability and predictability of large changes by back-propagation neural networks. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events into account. Training data corresponding to large changes of prediction-target time series are presented more often, and network learning is stopped at the point that has the maximal profit. When this technique is applied to daily stock-price prediction, the prediction error on large-change data was reduced by 11%, and the network's ability to make profits through experimental stock-trading was improved by 67% to 81%, in comparison with results obtained using conventional learning techniques.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01414874
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