Thu. Jan 20th, 2022

Your article “Lax rules allow corporate fat cats to dump inventory” (Opinion, December 23) referred to a study by David Larcker et al that explains how the U.S. Securities and Exchange Commission created a special exemption in the early 2000s that allowed insiders to sell shares even if they had something substantial. knew. It works according to a pre-programmed sales schedule, known as a 10b5-1 plan.

To qualify for the exemption, the study proposes a cooling-off period of four months. It may begin to reduce the likelihood that insiders will time such transactions to gain unfair advantage, and will spread multiple transactions, excluding the sale of shares before earnings announcements, while increasing the disclosure of those transactions.

The SEC regulation limits managers to a single sales plan per 12 months, and prevents the cancellation of planned sales at short notice.

While the regulation focuses more on “sell” trades, we think an additional recommendation might be to balance the automatic sell trades with the buy trades.

There is evidence that insiders are likely to avoid lowering the share price when selling inside information due to the risk of attracting regulatory scrutiny and potential shareholder litigation.

The Larcker study focused on the timing of sales transactions related to earnings announcements, which are predictable and part of the regulatory disclosure, rather than other voluntary news announcements that are likely to come as a surprise to the market, and therefore more impact will have.

However, with the knowledge of the dates of pre-arranged automated transactions, insiders can make their private voluntary disclosures coincide with those transactions.

One such recent case involved the CEO of Pfizer, the US drug manufacturer, who sold $ 5.6 million when the company announced that its Covid-19 vaccine was highly effective and that its shares had risen by 7 percent.

If the announcement had been made the next day, the sale would have raised only $ 4.8 million.

Meziane Lasfer and Xiaoke Ye
Bayes Business School, City, University of London, London EC1, UK

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