Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes

Autor:	Ghoshal, Sid, Roberts, Stephen
Rok vydání:	2016
Předmět:	Quantitative Finance - Statistical Finance Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 15 pages, 5 figures, accepted for publication in Algorithmic Finance
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1603.06202 Zobrazit plný text záznamu View this record from Arxiv