rises resulting in more stock price volatility. If news flow can be used effectively to
predict volume or volatility spikes then algorithms based on News Volume Weighted
Average Price (NVWAP) vs. VWAP on its own may add value for trade execution
strategies.
.Post-trade analysis Assist in proving best execution and trader performance through
post-trade analysis.
News data are likely to add value for investors trading at all frequencies from volatility-
based strategies to equity trading.
.Alpha-generating signal News data can be used in alpha generation at various
trading frequencies. News sentiment data may be used within factor models. Cahan,
Jussa, and Luo (2009) consider such an application. Their results are positive and they
find that such an approach does add value. In particular, they note the value of this
source of information during the Credit Crisis, when determining fundamentals
(which traditional quant factors are based on) was problematic. News data can also
aid quant investors to identify the non-rational biased behaviour of investors. These
can then be exploited.
.Stock-screening tool News data can be used to aid stock screening. In particular,
sentiment data may be used to guess the directional movement of future returns. Very
good news stocks (e.g., top sentiment quintile) might be selected to be held long and
very bad news stocks (e.g., bottom sentiment quintile) might be selected to be held
short.
.Fundamental research News analysis tools may aid traditional non-quant managers
by allowing them to undertake market research more efficiently.
.Risk management The use of news data within risk forecasting can allow for
dynamic (adaptive) risk management strategies that are forward-looking and are
based on changing market environments. Further, this risk analysis applied using
news data can help investors understand event risk and how different kinds of events
can impact their portfolio risk profile.
.Compliance/Market abuse News data may allow regulators to identify potential
market abuse and insider trading, perhaps by allowing the regulator to identify
market reactions prior to relevant public new releases.
1.5 SUMMARY AND DISCUSSIONS
The development of news analytics and its applications to finance through sentiment
analysis is gaining progressive acceptance within the investment community. A growing
number of academic studies have been conducted; in this chapter we have reviewed these
in a summary form. Research by service providers of data and content for the finance
industry is also discussed in this chapter and we have identified the applications of news
analytics to high-frequency and low-frequency trading as well as in risk control and
compliance. The study of news analytics draws upon research from a number of
disciplines including natural language processing, artificial intelligence (AI), pattern
recognition, text mining, information engineering and financial engineering. We
believe news analytics will soon become an important area of study within financial
analytics.
24 The Handbook of News Analytics in Finance