Thresholded ConvNet ensembles: neural networks for technical forecasting

By Sid Ghoshal, Stephen J. Roberts. 2018

14 citations recorded by the source corpus.

Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of ‘domain knowledge’. We investigate the predictive value of patterns in financial time series, applying machine learning and signal processing techniques to 22 years of US equity data. By reframing technical analysis as a poorly specified, arbitrarily preset feature-extractive layer in a deep neural network, we show that better convolutional filters can be learned directly from the data, and provide visual representations of the features being identified. We find that an ensemble of shallow, thresholded convolutional neural networks optimised over different resolutions achieves state-of-the-art performance on this domain, outperforming technical methods while retaining some of their interpretability.

Subfields: Computer Vision and Pattern Recognition, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Stefan Zohren
2. Bryan Lim
3. Stephen J. Roberts

In Management Science and Operations Research

1. Stefan Zohren
2. Bryan Lim
3. Stephen J. Roberts
4. Weiwei Jiang

Citations in the corpus

• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, methodology): “Trading rules are then manually defined in relation to class probabilities – either by using thresholds on classification probabilities to determine when to initiate positions [26], or incorporating these thresholds into the classification problem itself by dividing price movements into buy, hold an”