Social Data: What’s Next in Finance?

After a couple exciting years in social finance and some major events, we’re back with an update to our previous paper “Social Media in Markets: The New Frontier”. We’re excited to be able to provide this broad update on a rapidly evolving and increasingly important segment of financial service.

Social media analytics for finance has lagged brand analytics by 3 to 4 years despite being an enormous potential for profit through investing based on social insights. Our whitepaper explains why that gap has existed and what has changed in the social media ecosystem that is causing that gap to close. Twitter conversation around tagged equities has grown by more than 500% since 2011. The whitepaper explores what that means for investors.

Finance-Diagram-Final

We examine the finance specific tools that have emerged as well as outline a framework for unlocking the value in social data for tools that are yet to be created. Then we provide an overview of changes in academic research, social content, and social analytics for finance providers that will help financial firms figure out how to capitalize on opportunities to generate alpha.

Download our new whitepaper.

State of Social Media in the Financial Sector

In the two years that Gnip has been working with the financial industry, the state of social data and social media in the financial sector has changed dramatically. Here’s a look at where the industry stands and where we think it’ll go.

Social Media Moves Markets

Perhaps the highest profile event to involve social media in the financial sector this year was the Hash Crash.

On April 23, the AP Twitter account was hacked and tweeted that two explosions in the White House had injured Obama. The result? The Dow dropped more than 140 points within two minutes. It was an eye opener for many, on the power of social media to move markets. But what got lost in the coverage of this incident was that Twitter was part of the reason that the market rebounded so quickly. What Gnip has seen time and again is that rumors on Twitter are defeated just as quick as a rumor is started. Immediately after the initial Tweet, many others started to debunk the rumor giving live reports from the White House and setting the record straight.

The Hash Crash provided another, audible and clear reason for using social data if you are a participant in the financial markets. Our hedge fund and asset management customers have known this for sometime. If you weren’t following and analyzing social, you were most likely slower than others to understand what was happening in the market – in the dark.

SEC and Reporting on Social Media

Another big change to shape the industry was an official clarification in SEC policy on social media from the Securities and Exchange Commission allowing companies to announce key information on social media as long as investors knew that such channels would be used. As of today, more than 150 companies are using social media to report financial results or performance. Real-Estate Tech company Zillow, Nasdaq:Z ($Z) took this concept even further, opening their earnings Q&A up to questions from Twitter. Earnings calls have always been intended to provide color and transparency for all investors and potential investors of a publicly traded company, but the reality has been that they have been events attended and monitored almost exclusively by investment professionals. Opening up the announcement and especially the Q&A portion to Twitter isn’t as much a radical new move as it is a use of new technology to help re-align these events with their initial intent to give everyone access to information on the company to make investment decisions.

Social Data in the Markets

When Gnip first started looking at the ways the financial markets could use social data, we never would have guessed how fast the market would grow and how hungry people would be for data. In two years, we’ve seen large-scale growth of large hedge funds using Twitter social data as part of their trading strategies. Twitter provides a broad based stream that can answer questions about sentiment about companies, brands, ideas and rumors.  Investors are finding value both through intelligent aggregation and data mining. When a merger rumor is breaking, you can find speculative deal values on Twitter before official numbers have been released. In addition to Twitter, financial institutions have found value in similar content from Stocktwits as well. Stocktwits has a curated community of financial investors who buy into sharing their thoughts online. Stocktwits has been especially valuable for traders and hedge funds who don’t want to sift through the noise on Twitter.  If you search for Justin Bieber on StockTwits, you won’t find anything.

 

And earlier this year, Gnip signed a partnership with Estimize, a crowdsourced earnings estimates platform that provides open sourced financial estimates with incredible transparency, making it a valuable and unique set of social data. Estimize has a platform to capture and provide structure around the long explored concept of a whisper number. They’ve recently added Vinish Jha, a former Starmine Quant, to help add a layer of intelligent analytics on top of the open community, and to really work towards an open estimate that includes only the most accurate prognosticators.

The Adoption of Social Data in Trading Terminals

One of the oft passed around anecdotes at Gnip is how financial institutions talk about traders and analysts using their iPhones under the desk so they can keep an eye on Twitter. Due to regulation, most banks or brokerages don’t allow traders to post or use social media. To enable traders and analysts to access social media (but not to post) a number of banks and terminal providers have been adding social data to terminals  – thus enabling users to at least look up conversations and research online.  In the case of Bloomberg, for now they provide a curated feed, so it isn’t always the complete and full conversations.

New Uses – Risk Management

Over the next two years the acceptance of correlations between stock prices and social data will allow for deeper insights. The area I see making the most progress is in risk management. A good portion of making money in investing is figuring out how not to lose money.   With the S&P on a 5 year growth run, it’s no secret that there is a risk of a pullback, the big question is when?

Social data allows for risk modeling that removes one of the inherent biases of price/volume based modeling. Price and volumes of a security or asset only move when investors are ready to take action. Social media volumes and sentiment move around thought and discussion. Given the hope that thought and discussion still generally precedes action in the strategy of most investors, there exists a huge opportunity to pick up on early, previously undetectable correlations between companies and concepts.  A quick teaser example below shows normalized rolling 24 hour Twitter volumes for 2 related securities (LNKD and FB) and two unrelated securities (LNKD and IBM).  In the next year I expect more companies to start looking at these types of correlations for risk management, both between securities and concepts like “government shutdown”.

 

So Where Are We Headed?

Many of the initial uses cases have been reading social media for actionable trade ideas. The growing number of firms trying to offer social media based signals shows the success in this area. The next 1-2 years will be about expansion in two directions:  improvements in implementation/standardization and expansion of insights. Now that social data has made it through the sandbox phase for certain applications, the focus turns to integrating with existing processes and data sets. The most successful aggregators and indicators will partner with exchanges and traditional financial data vendors to help their data flow through to existing trading and research systems making the information more broadly accessible and cheaper to implement. On the raw data side, more tools will emerge to standardize linking data back to existing security/company identifiers and accepted industry and index classifications.

Using social data in the financial sector is fast becoming a must have, not a nice to have.

 

When an Earnings Release Isn’t an Earnings Release

Going into an earnings release, it’s important to know what recent news may be already baked into the stock price and what recent events are being discussed. Blackberry provided a great example on how you can easily look at social media to get context around an earnings release.

On Friday, Sept. 26th, Blackberry released their official results which included a loss of almost $1bln and a decline in revenue of 45%. Despite those dismal results, shares of Blackberry were trading slightly up during the day. How? Those following the stock knew this was because of a partial result pre-release a week earlier and an offer for privatization mid week. This post uses an internal interface we built using Gnip’s new Search API for Twitter to show how social data tells a full story.

A quick general search for “Blackberry” can help easily show whether or not there has been any major news in the week leading up to the call. On the chart you can see multiple spikes in the week prior to the official release.

Blackberry Mentions on Twitter

By zooming in on the spikes and looking through some of the details you can find three major events:

  • An earnings/revenue pre-release
  • Early talk of a privatization buyout around $9/share
  • The full official release of results

Here’s an example of digging into the one of the spikes. I added a second term “earnings” to get more relevant results on the earnings pre-release.

Blackberry Earnings on Twitter

Looking at the price chart, you can see all three events reflected on the price of the stock during the course of the week. A huge hit on the initial earnings release, a recovery rally on the news of the buyout and little movement on the final official earnings release.

Aside from being an interesting example of monitoring news through social media velocity, this example provides a great case study of where understanding investor reactions and volume could be useful but need to be paired with additional information. In this case, stock price movements accompanied the first two high Twitter volume events, but the third event had very little price movement despite high volume and bad news. A strong example of where more analysis is needed to determine if trending news is already reflected in the stock price!