Thresholded ConvNet ensembles: neural networks for technical forecasting
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ORIGINAL ARTICLE
Thresholded ConvNet ensembles: neural networks for technical forecasting Sid Ghoshal1
•
Stephen Roberts1
Received: 22 February 2019 / Accepted: 20 March 2020 Ó The Author(s) 2020
Abstract 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. Keywords Technical analysis Machine learning Deep neural networks
1 Introduction In financial media, extensive attention is given to the study of charts and visual patterns. Known as technical analysis or chartism, this form of financial analysis relies solely on historical price and volume data to produce forecasts, on the assumption that specific graphical patterns hold predictive information for future asset price fluctuations [1]. Early research into genetic algorithms devised solely from technical data (as opposed to e.g. fundamentals or sentiment analysis) showed promising results, sustaining the view that there could be substance to the practice [2, 3]. The rising popularity of neural networks in the past decade, fuelled by advances in computational processing power and data availability, renewed interest in their applicability to the domain of finance. Krauss et al. [4] applied multilayer perceptrons (MLPs) to find patterns in & Sid Ghoshal [email protected] Stephen Roberts [email protected] 1
Department of Engineering Science, Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK
the daily returns of the S&P500 stock market index. Dixon et al. [5] further demonstrated the effectiveness of neural nets on intraday data, deploying MLPs to classify returns on commodity and FX futures over discrete 5-min intervals. Architectures comprised of 4 dense hidden layers were sufficient to generate annualised Sharpe ratios in excess of 2.0 on their peak performers. In each instance, patterns were sought in the time series of returns rather than in the price process itself. Seminal findings by Lo et al. [6] employed instead the visuals emerging from line charts of stock closing prices, relying on kernel regression to smooth out the price process and en
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