beyond semantic analysis utilizing social finance data sets to improve quantitative investment...
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Beyond Semantic Analysis: Utilizing Social Finance Data Sets to Improve Quantitative Investment Models
Leigh DrogenEstimize Founder and CEO
New Social Finance Data Sets
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New Social Finance Data Sets
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Wait a minute….
Why is Bloomberg on that slide?
New Social Finance Data Sets
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Did you know that Bloomberg sells access to the entire corpus of its users’ anonymized chat history?
New Social Finance Data Sets
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Please collect your blown minds from the floor now so that we can move on.
Moving Beyond Sentiment and Semantic Analysis
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Moving Beyond Sentiment and Semantic Analysis
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•Proxy for investor attention
•Identifying market influencers
•Larger and more diverse sample sets
•Removing the sell side bias
Academic Studies
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• Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media - Hailiang Chen, Prabuddah De, Jeffrey Hu, and Byoung Hwang
• Crowdsourcing Forecasts: Competition for Sell Side Analysts? - Rick Johnston
• Generating Abnormal Returns Using Crowdsourced Estimates from Estimize - Leigh Drogen and Vinesh Jha
• The Value of Crowdsourcing: Evidence From Earnings Forecasts - Barbara Bliss
Estimize Pre Earnings Drift
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Estimize Pre Earnings Drift
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Estimize Post Earnings Drift
Estimize Post Earnings Drift
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Estimize Post Earnings Drift
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Estimize Post Earnings Drift
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Estimize Notable Estimates
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• Differ from the average estimate by a minimum percentage, or by a minimum standard deviation, reflecting fact that some stocks naturally have less uncertainty built into their estimates.
• If stock’s earnings have previously deviated significantly from expectations, Notable Estimate must be even more differentiated - as these companies are likely to report farther away from the crowd’s expectations.
• Estimate must be made at least five days in advance of expected earnings in order to be considered Notable.
• Contributor must have a strong track record of providing accurate earnings estimates to Estimize within the sector.
Stock Groupings
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• GICS industry and sector groupings are generated based on how companies produce revenue.
• While sell side analysts organize along these lines, the vast majority of market participants do not.
• Social finance platforms like Estimize, SeekingAlpha, StockTwits or Twitter allow users to contribute across whatever stocks they wish.
• By looking at the overlap in these stocks we are able to create higher correlated groupings.
• These groupings can be used as an alternative risk factor when constructing portfolios.
Investor Attention Proxy
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• Social finance platforms give an opportunity to measure what investors are looking at and talking about, enabling us to measure several heuristics including information availability.
• The immediate price and volume reaction to a firm’s earning surprise is much stronger, and the post-earnings announcement drift is much weaker for firms with crowd following.
• Higher levels of trading volume surrounding earnings announcements for firms that have an active investor following.
• Rise and decline in investor attention on other platforms (OpenFolio, Slingshot Insights, StockTwits) may provide similar insights.
