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Iain Watson - One of the best experts on this subject based on the ideXlab platform.
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Dividend Policy and Its Relationship to Investment and Financing Policies: Empirical Evidence Using Irish Data
1993Co-Authors: Peter Green, Michael Pogue, Iain WatsonAbstract:INTRODUCTION This paper investigates the relationship between dividend, investment and financing decisions. The empirical evidence is based upon a questionnaire survey of those companies listed on the Irish Stock Market in 1989. Recent research has concentrated on examining the dividend policies pursued by Irish companies by way of an analysis of published financial data (see, for example, Barrett and Cotter 1990 and Green and McIlkenny 1991). A questionnaire approach is adopted in this paper, so that the perceptions of those managers who actually formulate dividend policy can be examined. Furthermore, the relationship between dividend, investment and financing decisions can be explicitly investigated. The results of the survey support the contention that the level of dividend payments is not residually determined, ie dividend levels are not totally dependent upon the values selected for investment and financing variables. The question of independence between dividend and investment policies is a more contentious issue. The empirical evidence presented here, however, does suggest that at least at the aggregate level, dividend decisions are taken with reference to the exogenous factor of dividend stability, but consideration is also given to investment and/or financing decisions. Thus it would appear that a simultaneous dividend policy, ie the dividend decision is neither totally residual nor totally independent, is pursued by Irish companies. The remainder of this paper is organised as follows: First a brief review of previous literature is provided; second the sample and methodology employed in the study are then outlined; thirdly, the results and implications are evaluated; next, some of the limitations of the study are discussed; and the paper concludes with a brief summary of the main results of the study. PREVIOUS LITERATURE There have been previous surveys of dividend policy, although none have been conducted in Ireland. Lintner (1956) developed a model to explain the intertemporal behaviour of dividend levels as a result of interviews with the managements of 28 US firms. The model assumes that a firm's target dividend level in year t (Dt*) is related to the earnings in that year (E sub t ) by a target payout ratio (r). This payout ratio is a function of the firm's borrowing and investment opportunities and shareholders' marginal tax bands: (1) Dt* = rE sub t Furthermore, it is argued that, in any given year, the firm will only partially adjust towards the target dividend level of the year. The extent of the adjustment is represented by the speed of adjustment factor (c), which reflects the degree of acceptance of the new target. Hence, the actual change in dividends from period t-1 to t is given by: (2) D sub t -D sub t-1 =a + c(D sub t *-D sub t 1) + U sub t The constant term (a) is introduced as a somewhat arbitrary way of reflecting the reluctance of management to reduce dividends. U sub t is an error term. Substituting Equation (1) into Equation (2) produces the familiar Equation used in studies of dividend policy: (3) D sub t -D sub t-1 =a + crE sub t -cD sub t-1 , + U sub t Stewart (1987), Barrett and Cotter (1990) and Green and McIlkenny (1991) provide empirical evidence from an analysis of published financial data, both at the aggregate and individual firm level, which supports the contention that the Lintner (1956) model is descriptive of the dividend policies pursued by Irish companies. Green and McIlkenny (1991), however, find the constant term in the model to be statistically insignificant, whilst Barrett and Cotter (1990) suggest that there is a strong tendency for Irish companies to maintain dividends at constant levels. Such studies have provided a valuable insight into the dividend policies of Irish companies, but have provided relatively little information about the relationship between dividend, investment and financing decisions. …
David Trafimow - One of the best experts on this subject based on the ideXlab platform.
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The intelligibility of r or r2 as an effect size statistic: dichotomous variables
Frontiers in psychology, 2015Co-Authors: David TrafimowAbstract:There have been differences in the use of the correlation coefficient (r) or the coefficient of determination (r2) for indexing the effect size (see Rosenthal and DiMatteo, 2001; Borenstein, 2009; Elis, 2010, for reviews). I intend to investigate this issue by considering it from the point of view of matching the findings with the implied prediction. In essence my argument follows from a simplification of the correlation coefficient to the case where both variables are dichotomous and where there are equal frequencies of each possible response on both variables. Based on this simplified case, the question is whether the correlation coefficient or the coefficient of determination most closely resembles the actual proportion of agreements (successes) between the two variables after controlling for chance. To flesh out the idea, suppose that there are two variables and each of these is dichotomous and scored 0 or 1. From the point of view of a researcher who believes that the relation between the two variables is important, each case of matching scores (0 on both variables or 1 on both variables) is a success whereas each case of mismatching (0 on one variable and 1 on the other, or the reverse) constitutes a failure. The straightforward way to index the ability of the two variables to produce successes (agreements with respect to zeroes and ones) would be to use the proportion of obtained successes. However, because a 50% success rate would be expected due to chance, this proportion likely would be misleading. I suggest controlling for chance by computing an adjusted proportion of successes or adjusted success rate (SA) using Equation (1) below, where s refers to the proportion of successes and C refers to the proportion of successes that would be expected based on chance alone. SA=s−C1−C (1) In correlation terms, given the simplification mentioned previously, the usual phi correlation coefficient reduces to the Equation made famous by Rosenthal and Rubin (1982) rendered below as Equation (2), where r denotes the correlation between the two variables. s=0.5+r2 (2) Substituting Equation (2) into Equation (1) renders Equation (3). SA=(0.5+r2)−C1−C (3) Remembering that when there are two variables, we expect a 50% success rate by chance, 0.5 can be substituted for C rendering Equation (4). SA=(0.5+r2)−0.51−0.5 (4) In turn, Equation (4) simplifies to Equation (5). SA=r (5) Put into words, in the dichotomous case when there are equal numbers of zeroes and ones for both variables, the success rate adjusted for chance equals the correlation coefficient! In summary, then, my argument is simple. Because the proportion of successes, controlling for chance, is a straightforward and easy way to understand an effect size, this should be the preferred effect size statistic. Happily, the correlation coefficient equals this under the simplified conditions that I set up. Therefore, in terms of straightforward intelligibility, the correlation coefficient is superior to the coefficient of determination as an effect size index. Although my main point has been made, there are additional issues worth mentioning. First, there are additional reasons to favor r over r2. One such reason is that the former is directional whereas the latter is not. Another reason is that r has a straightforward interpretation in terms of standardized slope (the implications that a change in one variable has for a change in the other). Thus, Equation 5 is not the only reason to favor r over r2. A second issue is that it is possible for r to be a problematic measure of effect size even though it is superior to r2. Baguley (2009) contrasted standardized vs. unstandardized effect size measures. Both r and r2 are standardized effect size measures and the reliabilities of the measures of the variables have a strong influence on standardized effect size measures. As reliabilities decrease standard deviations increase, and so effect size measures that are standardized via standard deviations (in the denominator) decrease. For those researchers who wish to have their effect size measures uninfluenced by reliability issues, they either can use the famous correction formula from classical test theory or use an effect size measure that is not standardized. Each of these involves considerations that go beyond the present scope. The final issue I will consider pertains to the use of the present logic when one is considering correlation coefficients that are not based on dichotomous data with equal frequencies. To address this issue, it is important to remember that Equation 2 played an important role in getting to Equation 5 and that there has been much discussion about it in the literature. Rosenthal and Rubin (1982) and Rosenthal et al. (2000) argued that there usually is a tolerable amount of distortion when Equation (2) is applied outside the restricted domain involving dichotomous data with equal frequencies whereas Hsu (2004) suggested that there is an important amount of bias when the frequencies (or variances) are too unequal. A possible compromise conclusion is that generalization of Equation (2) outside the present case is justifiable when frequencies or variances are reasonably similar but not when they are extremely dissimilar.
Peter Green - One of the best experts on this subject based on the ideXlab platform.
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Dividend Policy and Its Relationship to Investment and Financing Policies: Empirical Evidence Using Irish Data
1993Co-Authors: Peter Green, Michael Pogue, Iain WatsonAbstract:INTRODUCTION This paper investigates the relationship between dividend, investment and financing decisions. The empirical evidence is based upon a questionnaire survey of those companies listed on the Irish Stock Market in 1989. Recent research has concentrated on examining the dividend policies pursued by Irish companies by way of an analysis of published financial data (see, for example, Barrett and Cotter 1990 and Green and McIlkenny 1991). A questionnaire approach is adopted in this paper, so that the perceptions of those managers who actually formulate dividend policy can be examined. Furthermore, the relationship between dividend, investment and financing decisions can be explicitly investigated. The results of the survey support the contention that the level of dividend payments is not residually determined, ie dividend levels are not totally dependent upon the values selected for investment and financing variables. The question of independence between dividend and investment policies is a more contentious issue. The empirical evidence presented here, however, does suggest that at least at the aggregate level, dividend decisions are taken with reference to the exogenous factor of dividend stability, but consideration is also given to investment and/or financing decisions. Thus it would appear that a simultaneous dividend policy, ie the dividend decision is neither totally residual nor totally independent, is pursued by Irish companies. The remainder of this paper is organised as follows: First a brief review of previous literature is provided; second the sample and methodology employed in the study are then outlined; thirdly, the results and implications are evaluated; next, some of the limitations of the study are discussed; and the paper concludes with a brief summary of the main results of the study. PREVIOUS LITERATURE There have been previous surveys of dividend policy, although none have been conducted in Ireland. Lintner (1956) developed a model to explain the intertemporal behaviour of dividend levels as a result of interviews with the managements of 28 US firms. The model assumes that a firm's target dividend level in year t (Dt*) is related to the earnings in that year (E sub t ) by a target payout ratio (r). This payout ratio is a function of the firm's borrowing and investment opportunities and shareholders' marginal tax bands: (1) Dt* = rE sub t Furthermore, it is argued that, in any given year, the firm will only partially adjust towards the target dividend level of the year. The extent of the adjustment is represented by the speed of adjustment factor (c), which reflects the degree of acceptance of the new target. Hence, the actual change in dividends from period t-1 to t is given by: (2) D sub t -D sub t-1 =a + c(D sub t *-D sub t 1) + U sub t The constant term (a) is introduced as a somewhat arbitrary way of reflecting the reluctance of management to reduce dividends. U sub t is an error term. Substituting Equation (1) into Equation (2) produces the familiar Equation used in studies of dividend policy: (3) D sub t -D sub t-1 =a + crE sub t -cD sub t-1 , + U sub t Stewart (1987), Barrett and Cotter (1990) and Green and McIlkenny (1991) provide empirical evidence from an analysis of published financial data, both at the aggregate and individual firm level, which supports the contention that the Lintner (1956) model is descriptive of the dividend policies pursued by Irish companies. Green and McIlkenny (1991), however, find the constant term in the model to be statistically insignificant, whilst Barrett and Cotter (1990) suggest that there is a strong tendency for Irish companies to maintain dividends at constant levels. Such studies have provided a valuable insight into the dividend policies of Irish companies, but have provided relatively little information about the relationship between dividend, investment and financing decisions. …
Michael Pogue - One of the best experts on this subject based on the ideXlab platform.
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Dividend Policy and Its Relationship to Investment and Financing Policies: Empirical Evidence Using Irish Data
1993Co-Authors: Peter Green, Michael Pogue, Iain WatsonAbstract:INTRODUCTION This paper investigates the relationship between dividend, investment and financing decisions. The empirical evidence is based upon a questionnaire survey of those companies listed on the Irish Stock Market in 1989. Recent research has concentrated on examining the dividend policies pursued by Irish companies by way of an analysis of published financial data (see, for example, Barrett and Cotter 1990 and Green and McIlkenny 1991). A questionnaire approach is adopted in this paper, so that the perceptions of those managers who actually formulate dividend policy can be examined. Furthermore, the relationship between dividend, investment and financing decisions can be explicitly investigated. The results of the survey support the contention that the level of dividend payments is not residually determined, ie dividend levels are not totally dependent upon the values selected for investment and financing variables. The question of independence between dividend and investment policies is a more contentious issue. The empirical evidence presented here, however, does suggest that at least at the aggregate level, dividend decisions are taken with reference to the exogenous factor of dividend stability, but consideration is also given to investment and/or financing decisions. Thus it would appear that a simultaneous dividend policy, ie the dividend decision is neither totally residual nor totally independent, is pursued by Irish companies. The remainder of this paper is organised as follows: First a brief review of previous literature is provided; second the sample and methodology employed in the study are then outlined; thirdly, the results and implications are evaluated; next, some of the limitations of the study are discussed; and the paper concludes with a brief summary of the main results of the study. PREVIOUS LITERATURE There have been previous surveys of dividend policy, although none have been conducted in Ireland. Lintner (1956) developed a model to explain the intertemporal behaviour of dividend levels as a result of interviews with the managements of 28 US firms. The model assumes that a firm's target dividend level in year t (Dt*) is related to the earnings in that year (E sub t ) by a target payout ratio (r). This payout ratio is a function of the firm's borrowing and investment opportunities and shareholders' marginal tax bands: (1) Dt* = rE sub t Furthermore, it is argued that, in any given year, the firm will only partially adjust towards the target dividend level of the year. The extent of the adjustment is represented by the speed of adjustment factor (c), which reflects the degree of acceptance of the new target. Hence, the actual change in dividends from period t-1 to t is given by: (2) D sub t -D sub t-1 =a + c(D sub t *-D sub t 1) + U sub t The constant term (a) is introduced as a somewhat arbitrary way of reflecting the reluctance of management to reduce dividends. U sub t is an error term. Substituting Equation (1) into Equation (2) produces the familiar Equation used in studies of dividend policy: (3) D sub t -D sub t-1 =a + crE sub t -cD sub t-1 , + U sub t Stewart (1987), Barrett and Cotter (1990) and Green and McIlkenny (1991) provide empirical evidence from an analysis of published financial data, both at the aggregate and individual firm level, which supports the contention that the Lintner (1956) model is descriptive of the dividend policies pursued by Irish companies. Green and McIlkenny (1991), however, find the constant term in the model to be statistically insignificant, whilst Barrett and Cotter (1990) suggest that there is a strong tendency for Irish companies to maintain dividends at constant levels. Such studies have provided a valuable insight into the dividend policies of Irish companies, but have provided relatively little information about the relationship between dividend, investment and financing decisions. …
Ronald H. Huesman - One of the best experts on this subject based on the ideXlab platform.
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Effects of temporal modeling on the statistical uncertainty of spatiotemporal distributions estimated directly from dynamic SPECT projections
Lawrence Berkeley National Laboratory, 2001Co-Authors: Bryan W. Reutter, Grant T. Gullberg, Ronald H. HuesmanAbstract:SUBMITTED TO THE 2001 IEEE MEDICAL IMAGING CONFERENCE LBNL-47795 Effects of Temporal Modeling on the Statistical Uncertainty of Spatiotemporal Distributions Estimated Directly from Dynamic SPECT Projections Bryan W. Reutter † , Grant T. Gullberg ‡ , and Ronald H. Huesman † Center for Functional Imaging, Lawrence Berkeley National Laboratory University of California, Berkeley, CA 94720, USA Medical Imaging Research Laboratory, Department of Radiology University of Utah, Salt Lake City, UT 84108, USA the SPECT projection data [1], along with the covariance ma- trix for the coefficients [2]. Denoting the projection of the m th spatial basis function along ray i at angle j by u m , and the integral of the n th tempo- ij ral basis function during the time interval associated with an- n gle j of rotation k by v jk , the projection Equations can be ex- M N I. I NTRODUCTION RTIFACTS can result when reconstructing a dynamic image sequence from inconsistent single photon emis- sion computed tomography (SPECT) projections acquired by a slowly rotating gantry. The artifacts can lead to biases in ki- netic parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. To overcome these biases in conventional image based dynamic data analysis, we have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribu- tion of the radiopharmaceutical throughout the projected field of view. In previous work we developed computationally efficient methods for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions [1] and their statistical uncertain- ties [2] from dynamic SPECT projection data, using a spatial segmentation and temporal B-splines. In addition, we studied the bias that results from modeling various orders of temporal continuity and using various time samplings [1]. In the present work, we use the methods developed in [1, 2] and Monte Carlo simulations to study the effects of the temporal modeling on the statistical variability of the reconstructed distributions. II. F AST C OMPUTATION OF S TATISTICAL U NCERTAINTY Time-varying activity concentrations within volumes of in- terest encompassing the projected SPECT field of view can be modeled by selecting a set of temporal basis functions capable of representing typical time variations and having desired smooth- ness properties. Similarly, the spatially nonuniform activity con- centration within a particular volume of interest can be modeled by selecting an appropriate set of spatial basis functions. Given a set of temporal basis functions and sets of spatial basis func- tions for the volumes of interest, coefficients for the resulting spatiotemporal basis functions can be estimated directly from This work was supported by the National Heart, Lung, and Blood Institute of the US Department of Health and Human Services under grants R01-HL50663 and P01-HL25840 and by the Director, Office of Science, Office of Biological and Environmental Research, Medical Sciences Division of the US Department of Energy under contract DE-AC03-76SF00098. This work was developed in part using the resources at the US Department of Energy National Energy Re- search Scientific Computing (NERSC) Center. A n pressed as p ijk = m=1 n=1 a mn u m v jk , where the p ijk are ij the modeled projections, the a mn are linear coefficients, and M , N are the numbers of spatial and temporal basis func- tions, respectively. The coefficients a mn are varied to find the values a mn that minimize the sum of squares function χ 2 = I J K i=1 j=1 k=1 (p ijk − p ijk ) , where the p ijk are the mea- sured projections, I is the number of projection rays per angle, J is the number of angles per rotation, and K is the number of rotations. The integral of the time-activity curve model for volume of interest m, during the time interval associated with angle j of N n rotation k, can be expressed as n=1 a mn v jk . Thus, given the covariance matrix for the spatiotemporal basis function coeffi- cients a mn , it can be shown that the variance of each time inte- gral is N N σ jkm = n n v jk cov(ˆ mn , a mn ) v jk . a n=1 n =1 Methods for quickly estimating the covariance matrix for the coefficients were presented, benchmarked, and validated in [2]. As a figure of merit related to the global precision of the time- activity curve model for volume of interest m, the following expression yields a squared noise-to-signal ratio (NSR) calcu- lated as the mean (over all of the time intervals) of the expected values of the squared errors between the integrated intervals of the “true” and modeled curves, normalized by the mean square value of the integrated intervals of the “true” curve: ξ m = J j=1 J j=1 K k=1 K k=1 σ jkm n a mn v jk N n=1 Substituting Equation (1) into Equation (2), the squared NSR, ξ m , can be calculated quickly by rearranging the summations, precomputing the inner products of the temporal basis functions,
