Resumen:
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In the present project, a comparison of di erent types of arti cial neu-
ral networks has been used to analyze their behavior with noisy time series
prediction, with the goal of maximizing the bene t obtainable by ...[+]
In the present project, a comparison of di erent types of arti cial neu-
ral networks has been used to analyze their behavior with noisy time series
prediction, with the goal of maximizing the bene t obtainable by investing
in them. To do so, a wide range of datasets has been used, containing stock
market prices until September 2014 and starting from January 2000 on-
wards. The starting experiment has been a regular multilayer perceptron
using a sliding window of the latest values as the input of the network and
three outputs representing three possible actions as buy, sell or keep. Fur-
ther experiments have been tested, such as the replacement of the three
outputs classi er by a single one, converting the system in a forecasting
model with only one output, or the use of di erent averages of recent val-
ues instead of a simple sliding window as the network's input. Also, it has
been tested the use of a single dataset from where each sample is used
rst to test and validate, and to train the network later on in a new step
instead of the traditional way of training-validation-test splitting of data.
Finally, two new models that seize all the data have been tested, one with
a speci c period of data validation, and the other one with an implicit
period, as it has been skipped by doing some networks pre-training. After
a comprehensive applying of these methods to the time series, certain pre-
dictability was found. Some networks were able to predict the direction
of change for the next day with an error rate of around the 40%, which
in some optimistic cases decreases to about 30% when rejecting examples
where the system has low con dence in its prediction. A practical simu-
lation has been explained, showing an average gain close to the 0.33% by
acting the half of the times.
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