  A binary classifier that tries to predict if the price of an asset will
increase or decrease naturally gives rise to a trading strategy that follows
the prediction and thus always has a position in the market. Selective
classification extends a binary or many-class classifier to allow it to abstain
from making a prediction for certain inputs, thereby allowing a trade-off
between the accuracy of the resulting selective classifier against coverage of
the input feature space. Selective classifiers give rise to trading strategies
that do not take a trading position when the classifier abstains. We
investigate the application of binary and ternary selective classification to
trading strategy design. For ternary classification, in addition to classes for
the price going up or down, we include a third class that corresponds to
relatively small price moves in either direction, and gives the classifier
another way to avoid making a directional prediction. We use a walk-forward
train-validate-test approach to evaluate and compare binary and ternary,
selective and non-selective classifiers across several different feature sets
based on four classification approaches: logistic regression, random forests,
feed-forward, and recurrent neural networks. We then turn these classifiers
into trading strategies for which we perform backtests on commodity futures
markets. Our empirical results demonstrate the potential of selective
classification for trading.
