The dissemination of socially sourced, non-financial information to Wall Street and the investing public is inefficient, leading to information imbalances that can last from seconds to weeks. As global mass adoption of social networks progresses the speed, reach, and mechanics of modern communication - the arc of data dissemination flattens. This evolution provides an opportunity for the interpretation of social metrics, cues, and warnings depicting the present in close to real time.
However, to the degree that transactional, usage, and geolocation alt data sources are reveled for their precision - social data can be opaque, contextually noisy, and highly interpretable. The inherent hurdles and perceived weaknesses of social data present an opportunity for individual quanta mental traders to develop peerless and differentiated expertise in its measurement and interpretation.
The discussion will dissect key differences between non-financial social mention frequency analysis and financial social sentiment analysis, which while vastly dissimilar are often mistakenly grouped as one in the same. Financial social sentiment analysis attempts to derive trading signals from a relatively small universe of influential financial chatter and related social sentiment. On the other hand, non-financial social mention frequency analysis involves mining, cleansing, validating, correlating, and analyzing terabytes of unstructured non-financial social data to derive actionable trading signals from interrelated non- financial information determined via mapping to be meaningfully relevant to a specific underlying security, sector, or market.
Applications for non-financial social mention frequency analysis generally fall into three categories:
1. Investment Idea Formation / Event Detection
Surface meaningful fluctuations in conversational frequency providing the earliest detection of off-radar change that could impact a key underlying metric of a business, sector, or macro-economic indicator.
Arbitrage conventional forms of information dissemination where time frames for broader market digestion, as reflected in the underlying asset price, can range from minutes to weeks.
2. Risk Management
Initiate critical and timely hedges against existing equity/credit exposure where, when there is an irregularity in conversational frequency, execution of such a hedge is either performed manually via a human sense check or automatically via an algorithm based on an accepted/validated pre-determined relationship between conversational data and metric/asset price.
As a real time barometer of change, conversational data can also be used to confirm the absence of change, making it a powerful tool for risk management even in quarters where it does not directly lead to hedges. Regular, or conforming conversational frequency would suggest low relative likelihood of unknown or off-radar factors that could adversely impact one's existing investment thesis, supporting one’s trade conviction.
Derive differentiated intra-quarter insights driven by conversational frequency and sentiment data identified to have high correlations to known key performance indicators or market pricing at the company, sector, or macro level.
Understand when social data and its corresponding narrative are either meaningfully supportive or in contradiction to an existing investment thesis or consensus KPI estimate.
The discussion will examine recent case studies exemplifying a range of successful and flawed social listening approaches for capital markets. Learn how social data sets have been predictive indicators of streaming service subscriber growth, mobile game downloads, corporate adoption of cloud computing, retailer foot traffic, ecommerce sales, box office revenue, and even storm related housing damage.