The author presents four cases that demonstrate the interplay of academic researchers, journalists, regulators, and law enforcement officers. By using financial data and knowledge of financial markets and institutions, academia is able to provide large-sample evidence to support or refute anecdotal allegations. The cases the author discusses include late trading of mutual fund shares, backdating of stock options, improper allocation of IPOs, and suspicious changes in a widely used database.
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
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.