Accurate predictive models are the heart and soul of portfolio management in today's rapid-fire trading environment. Investors are constantly seeking any advantage — no matter how small — when choosing instruments to add to, retain in or remove from portfolios. No stone is thus left unturned in the pursuit of good modeling and forecasting.
Could the daily news, or even the social interactions between corporate directors and analysts, potentially tip off investors to price moves minutes, hours or days before new earnings information becomes public?
One Stevens researcher decided to find out, and learned something surprising.
Social relationships, foretelling earnings
To test his theories about the 'corporate interlock' of financial social networks, Stevens business professor German Creamer created a model of relationships between directors of publicly traded S&P 500 companies and the analysts who provide coverage of those companies.
He then designed algorithms to determine whether analysts making so-called earnings-surprise predictions for companies with whose leadership they were already socially close were more likely to be accurate than statistically normal.
Those specific analysts' predictions, Creamer found, were indeed more accurate. Surprisingly strong earnings forecasts correlated much more strongly to actual surprise results when analysts were socially close to directors.
Interestingly, Creamer found this effect diminished somewhat after 2001, when the federal Regulation Fair Disclosure brought far greater transparency to company financial information and curtailed the process of selective corporate disclosures to smaller groups of analysts.
Deutsche Bank's influential newsletter Market Research singled out the research for special interest.
"This approach could help to disentangle the conflicts of interests between company management, investors and analysts, and hence providing better forecasts for earnings surprises and abnormal returns," the newsletter's authors noted.
Now, working with a team from Columbia University, Creamer is creating new algorithms that parse published news and social media with the goal of predicting the future directions of stocks listed on European markets.
"The correlation we are already seeing is between 10 percent and 30 percent," Creamer explains. "This is a signal, not strong enough to use alone, but something that adds to equities research and refines it."
Creamer's work is part of a comprehensive program of financial research and education at Stevens, much of it enabled by the Hanlon Financial Systems Lab — a leading-edge facility featuring Bloomberg terminals streaming real-time market data and the capability to analyze historical data with state-of-the-art software packages.
Stevens' financial offerings also include the nation's first undergraduate major in quantitative finance; leadership of a respected annual HFT (high-frequency trading) conference; pathbreaking business intelligence and analytics (BI&A) graduate coursework; and comprehensive financial engineering programming and research.