Insiders and Market Efficiency
Finnerty independently reinforced the early finding that insiders’ own portfolios differ from market benchmarks, while leaving outsider implementability as a separate question.
Finnerty is an early confirmation of the result associated with Jaffe: registered corporate insiders, considered as a group, realized returns that were inconsistent with the strongest form of market efficiency. The paper forms insider buy and sell portfolios and evaluates them with the asset-pricing tools then in common use. Its concise treatment helped make the distinction between insiders’ own performance and the information available to an outside reader of disclosed transactions part of the field’s core vocabulary.
That distinction remains essential. An insider acts on the trade date; an outsider sees a filing later and faces spreads, commissions, price movement, and a different portfolio construction problem. Evidence about the first object does not automatically establish anything about the second. Later work by Seyhun and Jeng, Metrick, and Zeckhauser makes this separation explicit and quantifies how costs and sample design narrow the interpretation.
Finnerty’s sample also belongs to an era of paper reporting and relatively coarse benchmarks. It does not classify Rule 10b5-1 plans, compensation-related codes, routine calendar activity, or clusters of distinct insiders. The paper is therefore strongest as historical confirmation that legal insider activity deserved systematic measurement. It is not a basis for treating every displayed row as equivalent. Insider Atlas places transaction code, direction, role, filing provenance, and cluster membership next to each observation so that those later distinctions remain visible.
Abstract excerpt
The publisher and RePEc records do not provide an abstract for this short 1976 article:
“No abstract is available for this item.”
Connections in Insider Atlas
The NTSK cluster event and NTSK company page show how the current site preserves the individual filings beneath an aggregated event.