CBOE Unveils Plans to Create Sentiment Benchmarks from Social Media Data

CBOE deal with Social Market Analytics eyes social-media based benchmarks.

The Chicago Board of Options Exchange (CBOE) has just revealed details of an exclusive licensing agreement that it entered into with Social Market Analytics (SMA) to create a suite of sentiment-based strategy benchmark indexes tied to SMA’s social media metrics.

As a leader in providing actionable intelligence from social media sources, Chicago-headquartered SMA analyzes social media streams to estimate market sentiment, leveraging its patented process to filter relevant tweets – including validating their sources while the meanings are evaluated.

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Extracting information from social media to make investment decisions represents a new and promising frontier in strategy benchmarking


Summer launch planned

The metrics that SMA gathers from its analysis are translated into actionable indicators that the company calls S-Factors, aimed to capture the ‘signature’ of financial market sentiment that is provided to clients as a technical analysis indicator.

CBOE plans to introduce its first index this summer that will use SMA’s proprietary data, thanks to the exclusive licensing agreement that will enable the series of strategy benchmark indexes to be developed.

Joseph A. Gits Source: LinkedIn
Joseph A. Gits
Source: LinkedIn

“Extracting information from social media to make investment decisions represents a new and promising frontier in strategy benchmarking. We believe that benchmark indexes based on Social Market Analytics’ state-of-the-art metrics will provide signals for investors to identify potential investment ideas,” said William Speth, Vice President, Research and Product Development, CBOE, commenting in a corporate statement regarding the deal.

High correlation of SMA data

Mr. Speth added: “Our research suggests that there is a high correlation between the price movements in stocks and SMA’s data.  Investors could potentially use these benchmarks to build strategies to enhance risk-adjusted returns in a portfolio.”

“SMA is thrilled to have been chosen by CBOE to partner on creating a family of sentiment-based benchmark indexes,” said Joseph A. Gits IV, CEO of Social Market Analytics, commenting in the joint announcement. “CBOE is an unrivaled product innovator and a leader in the index space and SMA is a leader in providing actionable social media metrics. We look forward to working with CBOE to create these first-of-their-kind indexes.”

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Filtering views

With so many people connected online and sharing their views in the collective realms of social media in real-time, firms have been increasingly trying to use such aggregated data from sources like Twitter or Facebook for some time already in order to gauge sentiment across various trends and market perspectives.

However, challenges in filtering such data have emerged from sources such as the twitter firehose as vast amounts of information generated are mostly noise about news – yet that noise could help reveal sentiment.

Therefore, a filtered index that applies a systematic approach and methodology for its calculation could be of immense value in providing a mirrored image of the collective sentiment across various related financial assets.

Social sentiment and news

The industry for financial market sentiment from social has come a long way in recent years. Finance Magnates covered a related development in 2013 regarding a startup firm called FSWire, along with other related stories.

A 2014 paper from the University of Virginia that was authored by scholars supported by the U.S. National Science Foundation under grant IIS-1236970 outlined a number of approaches in a report titled Benchmarking Twitter Sentiment Analysis Tools.

An excerpt from the report can be seen below, highlighting a number of tools used to analyze twitter sentiment across industries:


Source: University of Virginia
Source: University of Virginia

The paper concluded with trade-offs between using stand-alone and workbench tools, and that the error analysis data noted in the report could help future twitter sentiment analysis algorithm development.

Fast forward to today and machine learning and big-data are still key themes along with artificial intelligence and solutions such as Kensho that help search millions of sources of data for information about market sentiment.

It will be interesting to see how the CBOE series of sentiment benchmarks work as they are planned to be released this summer as new approaches to financial benchmarking further evolves to include social media data.


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