Lloyd Tanlu
Northeastern University
18 Papers
42 Citations
Lloyd Tanlu is an academic researcher from Northeastern University. The author has contributed to research in topics: Dividend & Bond credit rating. The author has an hindex of 11, co-authored 17 publications. Previous affiliations of Lloyd Tanlu include University of Washington & Washington and Lee University.
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Papers
Conflicts Of Interest And The Case Of Auditor Independence: Moral Seduction And Strategic Issue Cycling
TL;DR: A series of financial scandals revealed a key weakness in the American business model: the failure of the U.S. auditing system to deliver true independence as mentioned in this paper, and a two-tiered analysis of what went wrong.
On the Timing of Dividend Initiations
TL;DR: In this paper, the authors studied the timing and significance of dividend initiations in the life cycle of a firm and found that large firms with relatively high profitability and cash balances, and low growth rates are the initiators.
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The effect of organizational complexity on earnings forecasting behavior
TL;DR: In this article, the authors examine three aspects of organizational complexity, namely business, geographical, and cost structure complexity, and find evidence consistent with geographical complexity reducing the quantity and quality of management's communication with external market participants.
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Evaluating Proposed Remedies for Credit Rating Agency Failures
TL;DR: In this article, the credit ratings of M.B.A. students assume the role of credit rating analysts, and the authors conduct an experiment to explore how credit ratings are affected by two proposals for reform: (1) changing who pays the CRAs, and (2) requiring analysts to justify departures from a quantitative model.
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Because of “Because”: Examining the Use of Causal Language in Relative Performance Feedback
Serena Loftus,Lloyd Tanlu +1 more
TL;DR: This article examined how the use of causal language in conveying relative performance feedback impacts subsequent task performance, and found that causal language has a differential effect when used in negative versus positive feedback.
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