ABSTRACT We examine whether analysts' incentives to maintain good relationships with management contribute to the optimistic/pessimistic within‐period time trend in analysts' forecasts. In our experiments, 81 experienced sell‐side analysts from two brokerage firms predict earnings based on historical information and management guidance. Analysts' forecasts exhibit an optimistic/pessimistic pattern across the two timing conditions (early and late in the quarter), and the effect is significantly stronger when the analysts have a good relationship with management than when their only incentive is to be accurate. Debriefing results indicate that analysts are aware of this pattern of forecasts, and believe that this benefits their future relationships with management and with brokerage clients. The analysts most frequently cite favored conference call participation and information access when describing benefits from maintaining good relationships with management. Our results suggest the following: The optimistic/pessimistic pattern in forecasts is in part a conscious response to relationship incentives, information access is perceived to be a major benefit of management relationships, and recent regulatory changes may have lessened but have not eliminated this conflict of interest source.

ABSTRACT: The audit risk model was used to generate hypotheses concerning the effect that internal control evaluation exerts on audit planning decisions. Specifically, directional predictions concerning the contingent nature of the effects of the susceptibility of accounting processes to error, the strength of the internal control design, and the strength of the related compliance tests were developed. These hypotheses were then compared to the behavior exhibited by a group of experienced auditors who completed a highly realistic series of case studies. The auditors' decisions were consistent with the predictions developed from the audit risk model. In addition, the paper introduces a modification of the standard policy-capturing method that allows the use of complex realistic case materials in a powerful, internally valid experimental design which decreases problems with experimental demand. It also provides initial evidence on experts' perceptions of the effectiveness of different approaches to compliance testing and further evidence on auditor consensus in a more structured audit environment.

We design an adaptive framework for the detection of illegal trading behavior. Its key component is an extension of a pattern recognition tool, originating from the field of signal processing and adapted to modern electronic systems of securities trading. The new method combines the flexibility of dynamic time warping with contemporary approaches from extreme value theory to explore large-scale transaction data and accurately identify illegal trading patterns. Importantly, our method does not need access to any confirmed illegal transactions for training. We use a high-frequency order book dataset provided by an international investment firm to show that the method achieves remarkable improvements over alternative approaches in the identification of suspected illegal insider trading cases.