Purpose -The purpose of this paper is to compare the relative power of operating cash flow and earnings in the prediction of dividends. Design/methodology/approach -A linear mixed effects model is used in terms of selected model fit criteria. Findings -Based on the selected model fit criteria, cash flow per share is shown to produce a better fit than earnings per share, but it cannot be said how much better. Research limitations/implications -Quarterly CRSP and Compustat data from 2000 to 2006 for 1,902 dividend-paying firms are analyzed. Future work would need a different methodology to determine how much better cash flow is as a predictor of dividends. Practical implications -Both earnings per share and cash flow per share are found to be reasonable dividend predictors. Social implications -Additional insight is provided on modeling factors that contribute to a firm's decision to engage or disengage in a dividend payment policy. Originality/value -The study described in this paper continues work on predicting dividends per share. Results show cash flow per share is a better predictor than earnings per share. Investors and analysts predict dividends as part of their stock valuation work. This study suggests focusing attention on using cash flow per share as the predictor of dividends.

Purpose – The purpose of this paper is to describe and compare the mean response for selected financial variables in three dividend paying groups before and after the financial crisis of 2008. Dividend initiators are expected to be rewarded by investors over traditional dividend paying firms. Design/methodology/approach – Quarterly CRSP data from 2000 to 2012 are used to define dividend paying groups. Highly unbalanced financial data on dividend paying firms are analyzed in two truncated sample periods defined before and after the financial crisis. Fitted models describing differences in dividend paying groups are based on the linear mixed model representation of penalized splines with random effects to account for repeated measures over time. Findings – Results are presented on the important differences in selected financial variables before and after the financial crisis by dividend paying pattern group (traditional, initiators, residual/catering). Special emphasis is given to the analysis of market/book value ratio. Results demonstrate dividend initiators are rewarded over traditional dividend firms by investors. Firms with an intermittent paying pattern have no advantage. All dividend paying firms grow during the 2008 financial crisis. Traditional dividend payers have larger size than other dividend payers. The size effect explains results for several of the financial variables studied. Research limitations/implications – Future work can include an industry effect on the three dividend paying groups. Practical implications – Investors appear to prefer certainty as to when they receive a dividend over uncertainty, especially in times of economic downturn and economic recovery. Social implications – Enhanced awareness that different payment patterns exist and are rewarded differently over time on both the corporate issuer and investor sides. Originality/value – This study adds to body of knowledge of practical dividend payment patterns around a financial crisis. It also provides added support for dividend initiators.

Given a criterion variable and two or more predictors, applied linear prediction usually entails some form of OLS regression. But when there are several predictors, and especially when these are subject to non-ignorable errors of measurement, applications of OLS methods are often fraught with problems. Weighted structural regression (WSR) methods can mitigate many difficulties through the incorporation of prior structural models into analyses. WSR methods are sufficiently general to include OLS, ridge, reduced rank regression, as well as most covariance structural regression models, as special cases; many other regression methods, heretofore not available, are also included. In this article adaptive forms of WSR are developed and discussed. According to our bootstrapping studies the new methods have potential to recover known population regression weights and predict criterion score values routinely better than OLS with which they are compared. These new methods are scale free as well as simple to compute; they seem well suited to many prediction applications in behavioral research.

This abstract was created post-production by the JFI Editorial Board. The purpose of this study is to identify characteristics of large, industrial, dividend-paying firms in the United States. Several interesting conclusions and future research implications can be drawn from the estimated models based on annual data from 1995 to 2004: (1) as expected, dividend per share changes are positively related to changes in market/book value ratio for the two low dividend level groups; (2) as expected, dividend per share changes are positively related to company size changes for all but the low risk, low dividend group; (3) dividend per share changes are negatively related to debt ratio changes over time only for the high risk, high dividend level group; (4) dividend per share changes are negatively related to changes in common shares outstanding for the two high dividend groups, regardless of risk; and (5) dividend per share changes are positively related to net profit changes only for the high risk, high dividend level group. A five-year moving time span is applied to each group as a means to provide additional insight into parameter changes; however, very few consistent relationships are observed over time. This may be due to the limited sample sizes in each group.

The market break of 2000 appears to have changed how companies perceive dividends. This study shows dividends appear to be more important during the post-2000 period. While some financial variables had significant relationships with dividends per share (DPS) over both pre-2000 and post-2000 periods, others such as current ratio, beta risk measure, and net profit had significant relationships with DPS in only one period. This knowledge may help investors improve decisions regarding dividend-paying firms.