An Impact Measure for News: Its Use in Daily Trading Strategies

25 Pages Posted: 12 Dec 2015

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

Cristiano Arbex-Valle

OptiRisk Systems

Tilman Sayer

Advanced Logic Analytics

Date Written: December 10, 2015

Abstract

We investigate how ‘news sentiment’ in general and the ‘impact of news’ in particular can be utilised in designing equity trading strategies. News is an event that moves the market in a small way or a big way. We have introduced a derived measure of news impact score which takes into consideration news flow and decay of sentiment. Since asset behaviour is characterised by return, volatility and liquidity we first consider a predictive analytic model in which market data and impact scores are the inputs and also the independent variables of the model. We finally describe the trading strategies which take into consideration the three important characteristics of an asset, namely, return, volatility and liquidity. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.

Keywords: news sentiment, news impact, efficient market hypothesis daily trading, predictive analytics, asset returns, volatility of assets, liquidity of assets, GARCH model, bid-ask spread, news metadata, market data, volatility pumping, Kelly strategy

Suggested Citation

Yu, Xiang and Mitra, Gautam and Arbex-Valle, Cristiano and Sayer, Tilman, An Impact Measure for News: Its Use in Daily Trading Strategies (December 10, 2015). Available at SSRN: https://ssrn.com/abstract=2702032 or http://dx.doi.org/10.2139/ssrn.2702032

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

Cristiano Arbex-Valle

OptiRisk Systems ( email )

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

Tilman Sayer

Advanced Logic Analytics

London
United Kingdom

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