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...

A class of semiparametric fractional autoregressive GARCH models (SEMIFAR-GARCH), which includes deterministic trends, difference stationarity and stationarity with short-and long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper...

Multifactor models are often used as a tool to describe equity portfolio risk. Naturally, risk is dependent on the market environment and investor sentiment. Traditional factor models fail to update quickly as market conditions change. It is desirable that the risk model updates to incorporate...

Volatility prediction plays an important role in the financial domain. The GARCH family of prediction models is very popular and efficient in using past returns to forecast volatility. It has also been observed that news, scheduled and unscheduled, have an impact on return volatility of assets....

Due to its significance, forecasting asset volatility has been an active area of research in recent decades. In this whitepaper we aim to take into account the stylised facts of volatility to improve predictive power of a simple GARCH model. We investigate the power of three GARCH models (GARCH,...

In this study we investigate how the prediction of future volatility is improved by using news (meta)data. We use three input time series, namely: (i) market data, (ii) news sentiment impact scores, as explained by Yu (2014), and (iii) the news volume. We compare the results of predicting...