Forensic Finance (Digest Summary)

Forensic finance is the use of financial data, such as prices, quantities, and timing, to
find patterns that may be the result of individuals or firms taking unfair advantages in
the financial markets. Some patterns are found initially as a result of academic
research, and other patterns are discovered when academics seek large-sample evidence of
isolated or anecdotal cases. The author summarizes four cases to show the use of
forensic finance techniques.

The first case involves the late trading of mutual funds. Buy and sell orders for
open-end mutual funds placed during the day are executed at the end of the trading day.
The trade price is usually based on the values of the assets in the fund determined at
the 4:00 p.m. close. For illiquid and international securities, the closing values may
be “stale,” thereby providing profitable trading opportunities for some
investors. This market-timing activity is not illegal, but permitting investors to place
orders after the 4:00 p.m. close, which is late trading, is illegal. The effects of
market-timing activity were being studied when Canary Capital Partners agreed to fines
and penalties resulting from late-trading activities. Less than a week later, financial
researchers provided evidence that 60 percent of mutual funds engaged in late trading at
an annual cost to investors of $400 million. Late-trading activities seem to have
diminished since 2003, attributable in part to U.S. SEC–mandated reforms and the
$2.44 billion paid in restitution and fines by mutual funds during the 2003–07
period.

The second case involves backdating stock option grants. Significant positive abnormal
returns were found in the month after the granting of employee stock options.
Allegations of backdating were made when later research indicated negative abnormal
returns in the month before the grant and a disproportionate tendency for companies to
issue options on the day of the month with the lowest closing price. The effect is
stronger for unscheduled versus scheduled grants, and the effect diminished after
passage of the Sarbanes–Oxley Act of 2002, which shortened the time required for
executives to report receipt of stock option grants to two days.

When circumstantial evidence began to emerge, editors at financial journals and
regulators were skeptical about whether the evidence was strong enough to support the
accusation of backdating. Then, in November 2005, the Wall Street
Journal (WSJ) published a story about a backdating case.
Researchers contacted the authors, which led to subsequent articles in the
WSJ, a willingness by financial journal editors to publish the
findings, and more serious investigations by regulators. As a result, more than 50
company officers resigned, about 30 class-action suits have been filed, and the
WSJ won a Pulitzer Prize.

The third case concerns the allocation of underpriced IPOs to top corporate
executives—a practice known as “spinning.” Spinning involves two
issues: (1) the underpricing of IPOs and (2) the successful practice of using the IPO
allocations to influence the actions of corporate executives. Companies with executives
who are allocated underpriced IPOs are more likely to use the same investment bank for
their next offering. Research based on court records, government documents, and
newspaper accounts indicate that spinning costs an average of $14.5 million per company.
Changes in regulation occurred before academic research into the practice became public,
but the research provides useful insight into the general effect of spinning.

The last case involves Thomson I/B/E/S, which provides a database of analyst historical
earnings forecasts and recommendations. Researchers discovered major differences in the
information provided when they compared the data from 18 July 2002 with the data from 29
March 2003. These differences included changes in analyst recommendations, additions and
deletions of recommendations, and changes in the code number for the analyst who made
the recommendation. The concern was the possibility of data tampering, but an
investigation found the problems resulted from programming errors, processing errors,
adjustment to a new system, and general sloppiness. The outcome of the investigation led
Thomson Reuters to clean the database and implement a system to avoid further problems.
Unfortunately, the errors may have affected published research.