ABSTRACT This study examines whether auditors can effectively use nonfinancial measures (NFMs) to assess the reasonableness of financial performance and, thereby, help detect financial statement fraud (hereafter, fraud). If auditors or other interested parties (e.g., directors, lenders, investors, or regulators) can identify NFMs (e.g., facilities growth) that are correlated with financial measures (e.g., revenue growth), inconsistent patterns between the NFMs and financial measures can be used to detect firms with high fraud risk. We find that the difference between financial and nonfinancial performance is significantly greater for firms that committed fraud than for their nonfraud competitors. We also find that this difference is a significant fraud indicator when included in a model containing variables that have previously been linked to the likelihood of fraud. Overall, our results provide empirical evidence suggesting that NFMs can be effectively used to assess fraud risk.

Charities that use direct mailings or other activities that combine a public education effort with fundraising appeals must allocate the joint costs related to these activities to programs, fundraising, and administration. This study investigates whether charities use joint-cost allocations to manage the program ratio—a widely used measure of spending efficiency. Using a hand-collected dataset of 708 organization-year observations from 1992 to 2000, we find evidence that charities use joint costs to mitigate changes in the program ratio.

ABSTRACT We examine the returns of stocks in Cowles Industrial Index before and after the introduction of personal income taxes in 1917. This is distinct from earlier studies because we cross‐sectionally analyze the relationship between the returns of the individual stocks and measures of tax‐loss selling potential and size. We find that excess returns at the turn‐of‐the‐year and for the month of January were not significant until after 1917. These results provide strong support for the tax‐loss selling hypothesis as an explanation for the January seasonal in the returns of small firms.