With a historical two-day market shutdown in the wake of Superstorm Sandy, emotions on Wall Street are running high. Perhaps it's only fitting, then, that we talk about the use of sentiment analysis in the investment and trading industry.
Last Friday, in the calm before the storm, I talked with Rich Brown, head of Elektron Analytics at Thomson Reuters, about how sentiment is coming into play in making buy/sell and other investment decisions. Brown, who is in New Jersey, had been scheduled to speak at Tuesday's Sentiment Analysis Symposium in San Francisco. Aleksander Sobczyck, head of quantitative research-machine readable news, presented in his place.
As I wrote in a March post, Thomson Reuters News Analytics (TRNA) scores digital content for sentiment, relevance, and novelty, helping its newsfeed clients better capture market opinion and determine their next moves based on "what's going on out there." In March, the news was that Thomson Reuters had extended its machine-readable news offering with a sentiment scoring system for social media.
Use of the social media scoring remains "really niche" for now, Brown told me. After all, as Brown himself pointed out, "Lengthier content lets you get better sentiment signal than does 140 characters. Tweets don't give you enough context to understand the implications." Blogs are better, but news analysis, closed captioning feeds, conference call transcripts, and other, more traditional content types, are even more so, he said.
Sentiment analysis in the investment and trading sector is still leading-edge stuff, Brown said. He placed the number of clients using sentiment in the scores rather than the thousands, and said fewer than 100 clients are exploiting the social media sentiment scoring at this point.
But, as he noted, the numbers have been, and will continue, growing:
As more and more firms are writing research to prove how you can make money off of sentiment analysis and use sentiment in your trading models, it's becoming more and more common. Eventually, sentiment will be a signal everybody has to have in their models.
TRNA delivers sentiment scores on a 100-point scale, showing content's positivity or negativity. An article talking about a company's "challenging management environment" might get a score of negative two, while a piece discussing how results are "exceeding expectations" might get a positive three. The engine uses entity-level scoring, so if a news article disses Microsoft while singing Apple's praises, then Microsoft would get a negative sentiment score and Apple a positive one, Brown added.
To date, Thomson Reuters has seen clients use the sentiment scores in a variety of ways. The most common are:
As a circuit breaker. "If news comes out on IBM, the client will stop trading on it till a human can evaluate whether it should continue buying or selling -- whether it changes your investment hypothesis," Brown explained.
To change trading patterns. The rate at which news flows can be a predictor of future volume and volatility spikes. "So, you might trade faster when news comes out to get a better price on the market before it runs away."
To "generate alpha." This is the staple "buy the good news, sell the bad news -- and many different versions of that," Brown said.
Of course, according to Brown, this is all "amazingly sophisticated." I'd add, "complex," too. TRNA uses 82 variables, which get combined in any number of ways. Then take into consideration the number of different time horizons, the number of different content sources, and so on -- and, well, you can see how "complex" easily substitutes for "sophisticated."
Some help comes through a visualization toolkit Thomson Reuter also offers. "It can put thousands and thousands of stories in greater context, and see how that can confirm or go against an investment hypotheses," said Brown. A client, for example, might color code a country or sector for an at-a-glance look at sentiment. Tech sector companies might appear in green, financial services in an orangey color, and healthcare, with the nebulousness surrounding the impending election, yellow.
"If a picture is worth 1,000 words, an intelligent visualization on news analytics can be worth a million articles of 1,000 words each," Brown quipped.
How will the horrors of the hurricane translate into content tone picked up and scored by TRNA, and used in making investment decisions? I guess that all depends on who's doing the decision-making, whether they're doing so with the short term or long term in mind, and, oh, about a jillion other factors. Let's leave that to the analytics engines.
It's a longstanding rule of investing: Don't trade the news. Don't react to blips on the radar. Yet most investors do, so sentiment could help determine direction.
@noreen, I'd agree, a sentiment score could help them make more solid decisions on when and how to trade on the news since, as you say, they're going to do it anyways. With that said, though, I suppose some traders would misuse the sentiment scoring. Brown stressed this during our interview, in fact. He wanted to make sure readers understand that the scoring engine measures the tone of content not where the market is going to go based on that tone. Each trader will measure the market over their own time horizons -- are they wanting to act to optimize trading for a short-term market reaction or for a longer-term market reaction, for example? The market, as we know, can play out differently over different time horizons. So, for those who aren't careful, the metrics might be misleading. "But generally, it's a pretty accurate system," as Brown said.
When it mentions articles are they pulling those from a specific publication? I can just see a stock taking a big dip because some blogger with an axe to grind does nothing but post anti Microsoft articles all day. I would assume that they considered that and are only using industry based publications but even then you'd want to take those with a grain of salt when working out any kind of scoring.
Hi SaneIT, Brown told me that for the basic service, TRNA would be scoring sentiment on articles served up by a premium news service like Reuters -- so, presumably, you're not going to find a bunch of crap in the mix. Depending on the size of the article, it can score about six to 10 articles/second. Clients vary in how they use the service -- some might want a daily score, and would get that from perhaps 10 articles/day. Others might want more of a weekly or monthly average view, and would be needing to take in much more content to come up with that metric, say 100 items a day. Clients that want to really dig into the content, self-selecting additional content sources, including blogs, would buy the software platform and bring it in house rather than relying on the basic service.
@Noreen -- it is pretty interesting stuff, with lots more to come I suspect. Thomson Reuters already has expanded client options with market psych indices, available since the summer. This lets clients look at "behavioral-fueled finance" -- how much fear, joy, optimism, uncertainty, etc., are expressed in the articles, Brown said. They'd apply this sort of metric, available in a separate feed, to different categories like ag commodities, currencies or equity indices, or to countries, looking at trust, fear, etc., around, say, a regime change.
Many of the investors are forced to make irrational decisions because of a particular sentiment in their social investing circle which might not be representative of the overall market sentiment in general or about a particular scrip. Corroborating the sentiment around you with the sentiment scoring derived by a specialized firm which considers into account sentiment of all types of media and reliable sources can result in helping an investor enhance his confidence before making an investment or divestment decision.
"Brown told me that for the basic service, TRNA would be scoring sentiment on articles served up by a premium news service like Reuters -- so, presumably, you're not going to find a bunch of crap in the mix."
Thats a good strategy. To enhance the confidence in the scoring, it would be ideal if all the sources taken into account are disclosed, if not already being planned to be done. This is really important as many news agencies and analysts have a particularly biased view about certain companies; as rightly said by SaneIT.
It's good to hear that their basic service seems to be aware that you have to be careful with the press when using them for weighting, but the additional services where they are looking for 100+ articles a day seems like they are going to give up a bit of the harder facts for opinion which could get a questionable. I guess it would show how at least one segment feels about the company, I'd just want to dig deep into where the article were coming from before using them to make a decision.
Hi Sane IT. I think with volume cames balance, in a sense -- and don't forget, the Reuters feed would be of business and financial services news from reputable sources. Beyond that, the clients would be selecting content sources. Not sure why anybody would want the entire Twitter "firehose" or obnoxiously opinionated blowhard bloggers, but if they do, they've got the perogative!
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