Three professors at the University of Minnesota’s Carlson School of Management have assembled a mathematical model to explain why it is that financial fraud increases in good times and decreases in not-so-good times. Write Paul Povel, Rajdeep Singh and Andrew Winton, in a paper in the July Review of Financial Studies:
Our model highlights two key determinants of a firm’s fraud decision. The first is investors’ prior beliefs about the state of the economy, measured by the proportion of ‘good’ firms among firms seeking financing. When investors’ priors reflect low or average numbers of good firms, there is little or no fraud. … When priors are fairly optimistic, however, investors do not monitor a firm with positive public information carefully, because this merely confirms their view that the firm is very likely to be good, but they do monitor firms with negative public information. Here, incentives for fraud are high. …
We also highlight the role of investors’ costs of monitoring firms. Although intuition suggests that lowering such costs would reduce fraud, we show that this is not always the case. … Throughout the 1990s, improved computing and communications technologies greatly reduced investors’ costs of examining firms’ prospects, yet at the end of the decade–a period of very high investor expectations–a wave of frauds occurred.
So there you have it: The only reliable antidote to financial fraud is a good old-fashioned bust.