One way to calculate news sentiment in finance is to use something RavenPack calls Expert Consensus. The idea is you take a group of say 10 financial analysts, give them the same set of news stories to read, and ask them to rate each story as positive, negative, or neutral relative to a stock.
The sets involve both news flashes and articles and includes about 10,000 stories picked from various publishers and segments of time. Because of the large volume, it can take about 200 man hours per analyst to complete their classification. In addition to sentiment ratings, analysts are asked to rate the impact of the story on a scale of (1-5) and provide individual classifications should there be multiple companies covered in the article. Analysts are asked to think in terms of stock price impact over different trading windows. Is the news likely to impact the price of the stock positively or negatively in an hour? By the end of the trading day? Over the Long term?
Once the training sets are complete, RavenPack looks for patterns across the classifications provided by the analysts to determine which types of news are consistently classified as positive or negative, thereby looking for consensus. So if 9 out of 10 analysts agree that bankruptcy news is negative with a high impact score, the algorithm would be trained using this data. The sentiment algorithm learns from the classifications provided by the group consensus and uses this information as its training set to analyze news stories automatically moving forward.
What’s neat about this technique is that it tries to emulate expert group behavior and can perform this task consistently, continuously and in a fraction of a second after news gets published. Normally, it would take minutes for a financial analyst or trader to digest each piece of news, let alone act on it under some form of consensus.
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