Impact of News on Asset Behaviour: Return, Volatility and Liquidity in an Intra-Day Setting

33 Pages Posted: 23 Jul 2013 Last revised: 19 Nov 2013

See all articles by Xiang Yu

Xiang Yu

OptiRisk Systems

Gautam Mitra

Brunel University London - CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications

Keming Yu

Brunel University London - CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications

Date Written: July 22, 2013

Abstract

We report an empirical study of a predictive analysis model for equities; the model uses high frequency (minute-bar) market data and quantified news sentiment data. The purpose of the study is to identify a predictive model which can be used in designing automated trading strategies. Given that trading strategies take into consideration three important characteristics of an asset, namely, return, volatility and liquidity, our model is designed to predict these three parameters for a collection of assets. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.

Keywords: News sentiment, high frequency data, return, volatility, liquidity, predictive analysis

Suggested Citation

Yu, Xiang and Mitra, Gautam and Yu, Keming, Impact of News on Asset Behaviour: Return, Volatility and Liquidity in an Intra-Day Setting (July 22, 2013). Available at SSRN: https://ssrn.com/abstract=2296855 or http://dx.doi.org/10.2139/ssrn.2296855

Xiang Yu (Contact Author)

OptiRisk Systems ( email )

UNICOM R&D House
One Oxford Road
Uxbridge, UB9 4DA
United Kingdom

Gautam Mitra

Brunel University London - CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications ( email )

John Crank Building
Brunel University
Uxbridge, UB8 3PH
United Kingdom

Keming Yu

Brunel University London - CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications

John Crank Building
Brunel University
Uxbridge, UB8 3PH
United Kingdom

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