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Becca Lipman
Becca Lipman
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What’s Next in News Analytics: Not Social, Not Yet

News sentiment analysis has come a long way, but it's still difficult to incorporate trusted social media data.

News sentiment tools are rising in popularity as an additional trading indicator for hedge funds and asset managers. To maintain trust in the data, the analytics do not yet incorporate social media and microblogging sites.

Analyzing unstructured financial news for sentiment data has been no simple task. Tools have battled entity recognition, meaning understanding when "Apple" is referring to the tech company or the fruit, or when "Clinton" is referring to Bill or Hillary. Analytics has also accounted for misspellings, aliases, and abbreviations like AAPL, POTUS, or FLOTUS.

Once that hurdle is cleared, news sentiment tools aren't just capturing the "positive" and "negative" nuances of language towards entities -- analytics is helping to identify emerging relationships among tradable assets. Platforms will flag when firms, people, or brands rarely associated with one another are suddenly mentioned together. It will prompt an analysis of why that is, how frequent the mentions are becoming, and in what context. Most importantly, advances in event detection capabilities are enabling quant analysts and data scientists to add context to a story, anticipate market reaction to news, or predict creation of a business contract.

For example, analysis may find announcements of layoffs in certain conditions typically cause the market price to go up, while other conditions cause the share price to go down. Over time, an arsenal of correlations is at a client's disposal to better understand how novel the news is, and to better forecast events specific to a firm or country, or even geopolitical risk. Perhaps most quintessentially, today's systems are designed for speed, taking approximately 200 milliseconds to read, analyze, and distribute information in real-time across 1 million to 2 million news stories per day.

Truly, the capabilities of sentiment analysis have come a long way, says Armando Gonzalez, CEO of RavenPack, a real-time financial news analysis service. Gonzalez says he has seen significant increase in interest over the past three years from hedge funds and asset managers who want to add buzz, abnormalities, and sentiment to their multi-factor trading models. "Computers have a level of intelligence that supersedes people in this task. Even though it's not perfect, it's better than a subjective human."

As a trend, RavenPack has seen a growth rate of 30 to 40 percent per year for news sentiment products for the past three years, and that indicates market interest. Gonzalez adds that the product has a 90 percent renewal rate. "It continues to grow because this type of factor is becoming more accepted as a trading tool."

The only way technology like this can be successful is if the buy-side believes in the data. RavenPack and others of its ilk are running their analytics against information from trusted websites like The Wall Street Journal, PR Newswire, Bloomberg, and Zero Hedge, while staying well clear of social media and microblogging sites that lack reputation and accountability.

"I have strong views on why not to use Twitter and Facebook yet," says Gonzalez. "Customers want accountability. We can take a bunch of news from Barrons, from reporters who are investing resources to report and will be held accountable for that news, people who will be there tomorrow to back it up. With Twitter and Facebook there is no accountability, no trust, you're not able to prove the information."

He adds that there is some fantasy about Twitter's role in breaking news. Anyone can put up false information and see it become distributed in the market. And through all the noise, navigating to the original source is difficult. Computers can also easily misinterpret data. As an example, if you did a Twitter search for earthquakes in New York City it would seem they happen all the time the way people express "was that an earthquake?" or "feels like an earthquake" on social media, but if you were to check these tweets against geological records, there is no correlation.

Harnessing the good in social is the next big frontier for news sentiment, says Gonzalez, but it will be difficult to cast a net wide enough and specific enough to weed out the noise.

There is, however, some hope in concentrated platforms like StockTwits, a Twitter-like network for investors and traders. "It brings a bit more validity to the table," says Gonzalez. The community members participate, share opinions, and want people to follow them, so they have different incentives than the general population of Twitter. "We haven't yet found quantitative value in it," as the data science is not yet ready to take on StockTwits with enough validity, "but it's an exciting example of people sharing and being held accountable for opinions. I value that from a microblogging prospective."

Becca Lipman is Senior Editor for Wall Street & Technology. She writes in-depth news articles with a focus on big data and compliance in the capital markets. She regularly meets with information technology leaders and innovators and writes about cloud computing, datacenters, ... View Full Bio
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Blog Voyage
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Blog Voyage,
User Rank: Apprentice
7/7/2015 | 6:02:26 AM
Re: long wait
Absolutely right. Such a difficult social media to analyze and understand. So noisy.
Jonathan_Camhi
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Jonathan_Camhi,
User Rank: Author
11/23/2014 | 8:33:10 PM
Re: long wait
Twitter is kind of difficult to handle somtimes for getting specific inisghts. There's so much noise. Like you mention in the article, anybody can post false info, so getting to the valuable nuggets of info is kind of difficult.
Becca L
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Becca L,
User Rank: Author
10/29/2014 | 5:44:57 PM
Re: long wait
It's true that some firms are adding in sentiment from Twitter, but from all I understand the data isn't just taken with a grain of salt, it's more like the whole shaker. If you want to use sentiment data as a trusted factor in investing, better to leave it out.

That being said, Twitter can be very useful. InformationWeek just reported Twitter data will be made available through IBM's cloud for enterprises to run analystics against to use in apps and support decision making. "As an example, social data can accelerate product development efforts by predicting long-term trends or it can improve demand forecasting by providing real-time insight into weather patterns, IBM said." - this is much different than short term investing strategies.
fstechexec
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fstechexec,
User Rank: Moderator
10/28/2014 | 12:30:14 PM
long wait
I would have thought that a twitter-based strategy would work by now. It's been 3 years or so since we first heard of using social data for market insights. I guess the insights aren't that accurate so far, or there would be plenty of funds running social//twitter strategies. 
Greg MacSweeney
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Greg MacSweeney,
User Rank: Author
10/28/2014 | 12:15:10 PM
Re: Twitter investment strategy?
Oh, here is the source article for the Twitter hedge fund:

Twitter-fueled hedge fund bit the dust, but it actually worked

 
Greg MacSweeney
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Greg MacSweeney,
User Rank: Author
10/28/2014 | 11:44:22 AM
Twitter investment strategy?
The industry has been talking about using twitter to find investment ideas for years. Back in 2012, a twitter-based hedge fund lasted about a month and produced returns of about 2% (which doesn't sound good, but the returns for the overall market were actually negative for that same month)....so the twitter HF did outperform the market (in a very short sampling).


But no one has been able to figure out the secret to bring in social data to run an investment strategy for the long haul. It will happen eventually, but as Gonzalez says, there has to be a way to verify the 'news' on Twitter to make sure it is legitimate.
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