Asset price and volatility forecasting using news sentiment

Abstract

The aim of this thesis is to show that news analytics data can be utilised to improve the predictive ability of existing models that have useful roles in a variety of financial applications. The modified models are computationally efficient and perform far better than the existing ones. The new modified models offer a reasonable compromise between increased model complexity and prediction accuracy. I have investigated the impact of news sentiment on volatility of stock returns. The GARCH model is one of the most common models used for predicting asset price volatility from the return time series. In this research, I have considered quantified news sentiment as a second source of information and its impact on the movement of asset prices, which is used together with the asset time series data to predict the volatility of asset price returns. Comprehensive numerical experiments demonstrate that the new proposed volatility models provide superior prediction than the “plain vanilla” GARCH, TGARCH and EGARCH models. This research presents evidence that including news sentiment term as an exogenous variable in the GARCH framework improves the prediction power of the model. The analysis of this study suggested that the use of an exponential decay function is good when the news flow is frequent, whereas the Hill decay function is good only when there are scheduled announcements. The numerical results vindicate some recent findings regarding the utility of news sentiment as a predictor of volatility, and also vindicate the utility of the new models combining the proxies for past news sentiments and the past asset price returns. The empirical analysis suggested that news augmented GARCH models can be very useful in estimating VaR and implementing risk management strategies. Another direction of my research is introducing a new approach to construct a commodity futures pricing model. This study proposed a new method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are iii iv more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. The Kalman filtering framework for forecasting arbitrage-free (futures) prices was utilized, and it is assumed that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, nonlinear functional map. This approach is motivated by the successful use of a similar model structure in my earlier work, for predicting individual stock volatility using stock-specific news. Numerical experiments with real data illustrate that this new model performs better than the one factor model in terms of accuracy of predictive power as well as goodness of fit to the data. The proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly.