The sleeping watch dog: aka the Securities and Exchange Commission
Purpose - The purpose of this paper is to expose inefficient regulatory policies and organizational weaknesses at the Securities and Exchange Commission (SEC) that have contributed to a series of regulatory oversights that have produced some of the largest fraud schemes perpetrated on investors. Design/methodology/approach - Sources of information consisted of scholarly articles and articles retrieved from the web. Findings - Findings suggest that although weaknesses that have been exposed at the SEC may not account for any one securities fraud oversight, cumulatively, the weaknesses create negative synergy that increases the probability that a regulatory oversight will occur. Originality/value - This paper serves as a useful guide to alert and educate securities regulators and enforcement, regardless of the country they may operate in, to examine their own regulatory policies and organizational structures for weakness that may be similar to the SEC.
Visionaries or False Prophets
Theory and research on the psychology of white-collar offenders has historically been underdeveloped, and our understanding of the prototypical high-socioeconomic-status offenders, such as today’s chief executive officers and chief financial officers, first identified by Edwin Sutherland, has not benefited from the application of psychological trait theory. In this article, the author examines the negative synergy that develops when criminal thinking traits combine with the psychological traits of narcissism and psychopathy to create risk factors for white-collar offending. Psychological trait theory may be especially applicable to those who hold some of the highest positions in corporate organizations, who influence corporate culture, and who, at times, are considered visionaries in their respective industries.
Comparative Risk Sensitivity with Reference-Dependent Preferences
Experimental evidence suggests that individuals are risk averse over gains and risk seeking over losses (i.e., they have S-shaped utility functions in an expected utility setting) and that they are loss averse. Furthermore, the evidence leads to a single definition of S-shaped utility, but it has led to several alternative specifications of loss aversion. This paper characterizes the relations “more S-shaped than” and “more loss averse than” for a utility function, and in so doing arrives at a new definition of loss aversion based on average instead of marginal utility.