A majority of active investment strategies underperform index benchmarks, as even savvy managers are relentlessly matched by competitors, and few insights or information sources are unique. When a successful fund maintains an edge over time, distinguishing luck from skill can remain difficult. Strong vested interests and marketing agendas have long sustained an industry reliant on high fees, although a decades-long shift toward passive indexing underscores a growing recognition that efficient markets do not necessarily justify the expenditures.

Approaching Wall Street as an ‘information food chain,’ the InsideTrack^® portfolios began in 1996 by examining whether ‘smart money’ existed in trackable forms that could be mimicked to improve risk/reward metrics across a diversified portfolio. Offered almost exclusively to public company officers and directors, the portfolios attracted a unique brain trust for understanding industry drivers and trends while merging practitioner perspectives with academic research to become increasingly quantitative over time. A constant willingness to scrub and refine overlooked possible ‘smart money’ datasets and test hypotheses has led to many original insights which drive our investment approaches.

Key to our success has been strong 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 districts. In the end, the legislators showed no edge.

Markets meanwhile evolve, and the value of a dataset can increase or diminish over time. The InsideTrack^® portfolios put early work into evaluating the recommendation records of Wall Street analysts, running 50,000 Monte Carlo simulations on each of several thousand individuals 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 remaining 98% were not necessarily 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 the analysis to institutional investors, and we find the information no longer contains an edge.

The InsideTrack^® portfolios develop and follow signals around publicly disclosed insider trading activity. The disclosure filings can be complex, and our work with insider clients made us aware early that the commercial services reporting insider trades from the SEC website did not offer consistently accurate data. We accordingly spent several thousand hours refining a better extraction process, an effort which afforded us an 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 arisen in any of the nearly 200 academic papers exploring 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 that rarity of trading correlates positively to an insider’s performance. We have multiple publications pending that we expect will be important contributions to the field, and we actively apply these to our strategies.

Stay tuned.