| July 09, 2009
There are over 100,000 unique English language stories on average about finance published daily from reputable sources like Dow Jones, Bloomberg, Reuters, and other newswire services.
Stories are said to be unique in that they discuss a specific company, topic or theme, and reveal new information. In just a few seconds, these news stories are fed into thousands of news outlets, content aggregators, websites, and end-user applications like trading terminals. News is reproduced, redistributed, and duplicated into millions of news items in minutes. As more time goes by (usually a few days), the original stories become an
uncontrollable stream of news items
found just about anywhere from corporate or proprietary intranets to the Web. So, when does news become noise?
For starters, most original financial news comes from newswires and professional paid news services and not blogs or other social media. News vendors sell their information usually as part of a subscription model and investors pay a regular fee to receive their content.
Accessing content in real-time involves a fee whereas consuming the information online typically has a lower cost (or no cost) because it’s delayed.
Investors will pay premium
to receive timely content like economic indicators, corporate news and sentiment data in a machine-readable format, as a low-latency feed, sometimes even co-locating servers with the content provider to gain just a few milliseconds on delivery. The faster one can access the content and the more efficient the format is (pre-analyzed for sentiment, relevance, and potential market impact), the higher the premium they are willing to pay.
One of the reasons why investors pay for content is to be closer to the original source and further away from the noise.
Having access to direct feeds of original content helps remove noise
, but doesn’t solve the problem. Even reputable and original sources may cover the same stories but produce separate unique news items. A challenge is then to sift through the content of original publishers and find the “news” on the first instance when it happens. Here is where the burden is placed more on the format of the stories (rich tags and analytics) than on speed.
The following factors play an important role in eliminating noise:
Fig. 1: Distribution of Relevance on news stories with sentiment for the Dow 30. Relevance is measured with a score of 0-100 where higher values indicate a story is more relevant to a company in the Dow 30.
There are perhaps other factors to consider when addressing the question of
when news becomes noise
. David Leinweber does a really good job explaining some of these issues and provides a structured view for the sources of
investment news and securities trading rumours
in his book
“Nerds on Wall Street”
. I also read some postings by Jason Goepfert in Sentiment’s Edge where he discusses the importance of
accuracy in sentiment analysis
sources of content used to measure sentiment
Overall, I think understanding the various aspects of news production and consumption is key to generate alpha from public information.
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