Topic factor models: Uncovering thematic structure in equity market data

Issue title: Business Analytics in Finance and Industry January 6-8, 2014, Santiago, Chile

Guest editors: Cristián Bravo, Matt Davison, Alejandro Jofré, Sebastián Maldonado and Richard Weber

Article type: Research Article

Authors: Staines, Joe^* | Barber, David

Affiliations: Department of Computer Science, University College London, London, England

Correspondence: [*] Corresponding author: Joe Staines, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, England. Tel.: +44 7722853895; E-mail:joe.staines@gmail.com

Abstract: We examine the task of finding thematic structure in a data corpus comprising text and time series. To achieve this we introduce topic factor modelling (TFM). We develop a novel, joint generative model for both data types which resembles supervised latent Dirichlet allocation. TFM allows the decomposition of time series into factors which also reflect the thematic content of the text. We describe a variational method for inference and demonstrate its effectiveness on a synthetic corpus. For a corpus of publicly available equity data, we show that a TFM can simultaneously and robustly model both stock price time series and text data describing the corresponding companies. We also discuss how topic modelling could assist with external tasks such as robust covariance estimation.

Keywords: Topic modelling, latent dirichlet allocation, variational inference, computational finance, text mining

DOI: 10.3233/IDA-150770

Journal: Intelligent Data Analysis, vol. 19, no. s1, pp. S69-S85, 2015