Building on our work with insider clients, we offer fee-based investment management services for individuals, families and institutional investors. We seek superior long-term returns emphasizing risk management and broad diversification in passive strategies together with active approaches through Global Key's InsideTrack^® portfolios.

What are the InsideTrack^® portfolios? Most active investment strategies underperform because the talents and resources of even savvy managers are relentlessly matched by competitors, and few information sources or approaches are unique. In instances where a successful manager maintains an edge over time, distinguishing luck from skill can be difficult. Notwithstanding these facts on the ground, strong vested interests and marketing agendas have long sustained an industry reliant on high fees, although a decades-long shift away from active management toward passive indexing underscores a growing recognition that efficient markets do not necessarily justify the expenditures.

Grasping this landscape early, the InsideTrack^® portfolios began in 1996 by posing a different question: is there such a thing as ‘smart money’ that can be mimicked for constructing diversified investment approaches with better risk/reward metrics over time? Building upon academic and practical research in launching the undertaking, the InsideTrack^® portfolios were offered almost exclusively to officers and directors of public companies who came to serve as a collective brain trust for understanding unique industry drivers and trends. The portfolios became increasingly quantitative over time with significant efforts put into scrubbing and refining publicly available datasets, especially those pertaining to insider (officer and director) share trading activity around the world.

Success in pursuing this unconventional approach has relied upon strong scientific curiosity combined with a willingness to recognize that some datasets that ‘ought’ to produce viable investment signals may fall short of expectations. As an example, we put considerable time into cleaning up and studying the transactional disclosures of US Senators and Congresspeople in an effort that included singling out trades that were unusually large for a given legislator or that bore possible connections to committee assignments or home district ties. In the end, the legislators exhibited no information edge. In some cases, the value of a dataset can diminish over time. The InsideTrack^® portfolios put substantial early work into evaluating the recommendation track records of Wall Street analysts, running 50,000 Monte Carlo simulations on each of several thousand analysts over multiple years at several hundred firms. We identified only 2% of analysts as having a 75% chance or better of being ‘above average’ the rest amounting to randomness. This is not to say the remaining 98% of analysts were unskilled; a better analogy would be professional athletes on a field mostly cancelling one another out. These insights served the InsideTrack^® portfolios well until eventually arbitraged away by large teams of PhD’s organized to study and sell these insights to institutional investors, and we find the information no longer contains an information edge.

The InsideTrack^® portfolios currently rely primarily upon signals around publicly disclosed insider trading activity. The disclosure filings can be complex, and one unique advantage we have is our close involvement with insider clients which made us aware early that none of the commercial services extracting and reporting insider trades from the SEC website were doing so in an inaccurate manner. We accordingly spent several thousand hours refining a better extraction process, an effort which afforded us a significant edge in attracting top research talent. Working closely with insiders has also spurred us to consider novel questions around insider trading performance that have not been asked in any of the nearly 200 academic papers that have explored the topic. We have found that behavioral factors matter. As one example, we were the first to apply a time series analysis toward insider trading, publishing academic work showing that that insiders over time get worse at acquiring shares while improving at selling, and moreover, that the rarity of an insider’s trading correlates positively to performance. We have multiple additional publications pending that we believe will be important contributions to the field, and we actively apply these to our portfolio strategies.

Stay tuned.