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Extracting predictive information from heterogeneous data streams using Gaussian Processes

Extracting predictive information from heterogeneous data streams using Gaussian Processes Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain’s heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Algorithmic Finance IOS Press

Extracting predictive information from heterogeneous data streams using Gaussian Processes

Algorithmic Finance , Volume 5 (1-2): 10 – Jan 1, 2016

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References (19)

Publisher
IOS Press
Copyright
Copyright © 2016 IOS Press and the authors. All rights reserved
ISSN
2158-5571
eISSN
2157-6203
DOI
10.3233/AF-160055
Publisher site
See Article on Publisher Site

Abstract

Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain’s heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction.

Journal

Algorithmic FinanceIOS Press

Published: Jan 1, 2016

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