A look at social data in the financial markets with Tom Watson, Vice President of Global Market Data at NYSE Euronext; Brian Hyndman, Senior Vice President of Global Information Services at NASDAQ OMX Group, Inc., Rich Brown, Global Head of Elektron Analytics at Thomson Reuters and Heidi Johnson, Global Product Lead for Hub and Collaboration Services at Markit.
The use of social media data in the finance industry presents some inherent and unique challenges. This panel explored how social data could and should be used on Wall Street.
There is a great opportunity for leadership in this space, as no one entity is currently driving the charge on how to structure the use, the infrastructure or the verification of social data in financial markets. Firms need to focus on how to get this volume of data from the social channels and to the customers in order to trade and make investment decisions based on that data. Firms are receptive to using social data, but regulatory and compliance oversight make this tricky. Who is to say when information becomes public? Firms are looking for real-time data solutions but must examine this information within the context of historical models. Historical trends put real time data in perspective; both are critical.
How do firms vett social media accounts as the professional, official individuals and groups? A verification process or Klout-esque score or index is needed in order to confirm that the data from social channels or social sources are reliable, trusted, consistent: Is that the company or individual you think it is? Currently we passively consume this social data, but how can financial firms weed out false positives and anomalies? The nature of social data is that it moves so fast, people and companies react before verification can be made of a trusted source. Fake accounts can and have tanked stocks. Identity verification of expert, trusted sources is crucial. It’s not the first tweet, it’s the conglomerate of the tweets, blog posts, etc., but people often react to the first data they see instead of looking at trends and patterns, as well as the original source. Think snowball, not snowflake. Social amplifies data. Everyone has the ability to reach millions of people now on social media. Data needs to be corroborated with other sources of information.
Earlier this year, the Associated Press Twitter account was hacked and sent out a falsified tweet about a bombing at the White House. The fake news event caused a real market event, dubbed the Hash Crash- a dip (and reversion) in the stock markets. But even from the start, a large proportion of overall Twitter conversation doubted the veracity of that tweet, and traditional news sources showed a different story. This disproved Tweet led to the V-shaped dip and recovery of the markets.
Social Data Examples in Financial Markets
- Spikes in weather reports and crop prices
- Violence in Iran and oil prices
- Sentiment and Psychological index – fear, greed, optimism
- Geospatial – Florida orange grove region and supply chain data
There is a great need for historical models, real time data analytics, data mining, and verification processes in the financial realm, and firms are receptive to finding these solutions.