fied, collection of assets. These decisions are also taken more promptly (reducing
latency). Automation or semi-automation of the human judgement process widens
the limits of the investment process. Leinweber (2009) refers to this process as
intelligence amplification (IA).
As shown in Figure 1.1 news data are an additional source of information that can be
harnessed to enhance (traditional) investment analysis. Yet it is important to recognize
that NA in finance is a multi-disciplinary field which draws on financial economics,
financial engineering, behavioural finance and artificial intelligence (in particular,
natural language processing). Expertise in these respective areas needs to be
combined effectively for the development of successful applications in this area. Sophis-
ticated machine-learning algorithms applied without an understanding of the
structure and dynamics of financial markets and the use of realistic trading assumptions
can lead to applications with little commercial use (see Mittermayer and Knolmayer,
2006).
The remainder of the chapter is organized as follows. In Section 1.2 we consider the
different sources of news and information flows which can be applied for updating
(quantitative) investor beliefs and knowledge. Section 1.2.2 covers several aspects of
pre-analysis to be considered when using news in trading systems and quantitative
models. In Section 1.3 we consider how qualitative text can be converted to quantified
metrics which can form inputs to quantitative models. In Section 1.4 we present news-
based models; in particular, we consider the computational architecture (Section 1.4.1),
applications for trading and fund management (Section 1.4.2) and applications for
Applications of news analytics in finance: A review 3Figure 1.1.A simple representation of news analytics in financial decision making.