A new clustering methodology for the analysis of sorted or categorized stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed. Wayne S. DeSarbo is the S. S. Kresge Distinguished Professor of Marketing and Statistics, and Michael D. Johnson is Associate Professor of Marketing, both at the University of Michigan's School of Business Administration. Kamel Jedidi is Assistant Professor of Marketing at Columbia University's Graduate School of Business. The authors gratefully acknowledge DuPont Incorporated for providing financial support for this research.     

Blockholder Exit Threats and Financial Reporting Quality

Recent theoretical and empirical studies suggest that blockholders (shareholders with ownership ≥ 5 percent) exert governance through the threat of exit. Blockholders have strong incentives to gather private information and sell their shares when managers are perceived to underperform. To prevent blockholders from selling their shares and the firm from suffering a stock price decline, managers align their actions with the interests of shareholders. As a result of the greater manager‐shareholder alignment, managers' actions are more likely to be in shareholders' best interest, and consequently there is less need for managers to manipulate earnings. Consistent with these predictions from economic theory, we find evidence that as exit threat increases, firms have higher financial reporting quality. Theory also predicts that the impact of blockholders' exit threat on financial reporting quality (FRQ) should increase as the manager's wealth is tied more closely to the stock price, and this is what we find. Our study contributes to the research on the impact of shareholders on FRQ and to an emerging literature on the impact of blockholders in financial markets. Blockholders play an important role in managers' reporting outcomes through their actions as informed investors.     

Spurious Regressions in Financial Economics?

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974) . Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious.