Second order Stochastic Dominance (SSD) has a well recognised importance in portfolio selection, since it provides a natural interpretation of the theory of risk-averse investor behaviour. Recently, SSD-based models of portfolio choice have been proposed; these assume that a reference distribution...

We develop a news-enhanced risk model, which utilizes news sentiments to optimize the valuation of market risk. In particular, our model takes into account news sentiment scores, which value news of various sources according to their relevance for particular companies. A GJR-GARCHX model is...

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...

The explosive development of electronic media has brought to the market participants thousands of pieces of financial news which are released on different platforms every day. Many news wires published online are editorially controlled and can be relied as factual summary as opposed to fake news...

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....

The rapid rise of social media communication has touched upon all aspects of our social and commercial life. In particular, the rise of social media as the most preferred way of connecting people on-line has led to new models of information communication amongst the peers. Of these media Twitter...

In nonparametric multivariate regression analysis, one usually seeks methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. We study nonparametric estimation of multivariate conditional distribution and quantile regression...

Robust optimization is a tractable alternative to stochastic programming particularly suited for problems in which parameter values are unknown, variable, and their distributions are uncertain. We evaluate the cost of robustness of the robust counterpart to the maximum return portfolio...