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Using Company-Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations

Readshaw, Jonathan; Giani, Stefano

Using Company-Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations Thumbnail


Jonathan Readshaw


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 finetuned 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 an 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.


Readshaw, J., & Giani, S. (2021). Using Company-Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations. Neural Computing and Applications, 33(24), 17353-17367.

Journal Article Type Article
Acceptance Date Jul 8, 2021
Online Publication Date Jul 29, 2021
Publication Date 2021-12
Deposit Date Jul 13, 2021
Publicly Available Date Nov 23, 2021
Journal Neural Computing and Applications
Print ISSN 0941-0643
Electronic ISSN 1433-3058
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 33
Issue 24
Pages 17353-17367


Published Journal Article (860 Kb)

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

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