  This work presents a Convolutional Neural Network (CNN) for the prediction of
next-day stock fluctuations using company-specific news headlines. Experiments
to evaluate model performance using various configurations of word-embeddings
and convolutional filter widths are reported. The total number of convolutional
filters used is far fewer than is common, reducing the dimensionality of the
task without loss of accuracy. Furthermore, multiple hidden layers with
decreasing dimensionality are employed. A classification accuracy of 61.7\% is
achieved using pre-learned embeddings, that are fine-tuned during training to
represent the specific context of this task. Multiple filter widths are also
implemented to detect different length phrases that are key for classification.
Trading simulations are conducted using the presented classification results.
Initial investments are more than tripled over a 838 day testing period using
the optimal classification configuration and a simple trading strategy. Two
novel methods are presented to reduce the risk of the trading simulations.
Adjustment of the sigmoid class threshold and re-labelling headlines using
multiple classes form the basis of these methods. A combination of these
approaches is found to more than double the Average Trade Profit (ATP) achieved
during baseline simulations.
