1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

This paper surveys capital structure theories based on agency costs, asymmetric information, product/input market interactions, and corporate control considerations (but excluding tax-based theories). For each type of model, a brief overview of the papers surveyed and their relation to each other is provided. The central papers are described in some detail, and their results are summarized and followed by a discussion of related extensions. Each section concludes with a summary of the main implications of the models surveyed in the section. Finally, these results are collected and compared to the available evidence. Suggestions for future research are provided.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1991.tb03753.x

The Theory of Capital Structure

https://www.mit.edu/~kothari/attach/KLW%20paper%20May%202001.pdf

Performance matched discretionary accrual measures

A review is presented of the book “Heuristics and Biases: The Psychology of Intuitive Judgment,” edited by Thomas Gilovich, Dale Griffin, and Daniel Kahneman.

Heuristics and Biases: The Psychology of Intuitive Judgment

Much research in the last two decades has demon- strated that human responses deviate from the performance deemed normative according to various models of decision mak- ing and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be inter- preted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experi- menter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implica- tions of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance er- rors are a minor factor in the gap; computational limitations un- derlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Un- expected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X00003435

Individual differences in reasoning: Implications for the rationality debate?

This paper examines cross-sectional differences in the optimistic behavior of financial analysts. Specifically, we investigate whether the predic- tive accuracy of past information (e.g., time-series of earnings, past returns, etc.) is associated with the magnitude of the bias in analysts' earnings fore- casts. We posit that there is higher demand for non-public information for firms whose earnings are difficult to accurately predict than for firms whose earnings can be accurately forecasted using public information. Assuming that opti- mism facilitates access to management's non-public information, we hypoth- esize that analysts will issue more optimistic forecasts for low predictability firms than for high predictability firms. Our results support this hypothesis.

Earnings Predictability and Bias in Analysts' Earnings Forecasts

In empirical studies of earnings management (EMI), a central issue is the estimation of the managed component (discretionary accruals) when outsiders observe only the sum of the managed and unmanaged (nondiscretionary) accounting numbers. Important methodological issues arise in part because the variables most useful in predicting the unmanaged components are themselves accounting numbers which are likely to be affected by EM. In this paper, we propose a simple method of addressing some of these issues in testing for EM in context-specific cases.1 Our method builds on the approach used by McNichols and Wilson [1988]. By modeling a specific type of accruals (bad debt expense), they enhance precision but do not capture accrual manipulations in many other accounts. Other studies attempt to capture a larger portion of managed accruals by modeling total accruals. However, we suggest that models following this approach are subject to simultaneity, errors-in-variables,

https://www.jstor.org/stable/pdfplus/2491492.pdf

Issues in Testing Earnings Management and an Instrumental Variable Approach

We study the determinants of contract choice in offshore software development projects and examine how the choice of contract and other factors in the project affect project profits accruing to the software vendor. Using data collected on 93 offshore projects from a leading Indian software vendor, we provide evidence that specific vendor-, client-, and project-related characteristics such as requirement uncertainty, project team size, and resource shortage significantly explain contract choice in these projects. Our analysis suggests that contract choice significantly determines project profit. Additionally, some ex ante vendor-, client-, and project-related characteristics known at the time of choosing the contract continue to significantly influence project profits after controlling for contract choice. We also provide evidence to show that project duration and team size affect project profits.

Contracts in Offshore Software Development: An Empirical Analysis

Existing empirical evidence indicates that analyst forecasts of corporate earnings do not meet the strict rationality standards prescribed by econometric tests of the rational expectations hypothesis.1 In this paper, we address the question of whether the expectation formation process underlying analyst forecasts is adaptive, or whether these forecasts are influenced by noninformational factors, such as incentives arising from the market for their forecasts (O'Brien [1988], Lin and McNichols [1993], Francis and Philbrick [1993], and Dugar and Nathan [1992]), and interaction between judgmental and/or statistical forecasting methods (Bunn and Wright [1991], Fan [1990], and Hoerl and Kennard [1970]). The rejection of rational expectations gives rise to two views of the forecasting process of analysts. The first, based on an informational argument, is that the forecasting process is adaptive (Marcet and Sargent

Analysts' Interim Earnings Forecasts: Evidence on the Forecasting Process

Recent corporate governance reforms focus on the board’s independence and encourage equity ownership by directors We analyze the efficacy of these reforms in a model in which both adverse selection and moral hazard exist at the level of the firm’s management Delegating governance to the board improves monitoring but creates another agency problem because directors themselves avoid effort and are dependent on the CEO We show that as directors become less dependent on the CEO, their monitoring efficiency may decrease even as they improve the incentive efficiency of executive compensation contracts Therefore, a board composed of directors that are more independent may actually perform worse Moreover, higher equity incentives for the board may increase equity-based compensation awards to management (JEL G31, G34, D82)

/pdf/who-monitors-the-monitor-the-effect-of-board-independence-on-146u9hjy5e.pdf

K. Sivaramakrishnan

Papers

Earnings Predictability and Bias in Analysts' Earnings Forecasts

Issues in Testing Earnings Management and an Instrumental Variable Approach

Contracts in Offshore Software Development: An Empirical Analysis

Analysts' Interim Earnings Forecasts: Evidence on the Forecasting Process

Who Monitors the Monitor? The Effect of Board Independence on Executive Compensation and Firm Value