scholarly journals Optimal dividend-payout in random discrete time

2011 ◽  
Vol 28 (3) ◽  
pp. 251-276 ◽  
Author(s):  
Hansjörg Albrecher ◽  
Nicole Bäuerle ◽  
Stefan Thonhauser
Keyword(s):  
Discrete Time ◽  
Dividend Payout ◽  
Optimal Dividend

scholarly journals Optimal dividend payout under stochastic discounting

2022 ◽  
Author(s):  
Elena Bandini ◽  
Tiziano De Angelis ◽  
Giorgio Ferrari ◽  
Fausto Gozzi
Keyword(s):  
Dividend Payout ◽  
Optimal Dividend

scholarly journals Optimal Dividend Policy In Discrete Time

2014 ◽  
Vol 47 (1) ◽  
Author(s):  
Ewa Marciniak ◽  
Jakub Trybuła
Keyword(s):  
Discrete Time ◽  
Dividend Policy ◽  
Value Function ◽  
Bank Loan ◽  
Numerical Example ◽  
A Value ◽  
Optimal Dividend ◽  
Optimal Dividend Policy

AbstractA problem of optimal dividend policy for a firm with a bank loan is considered. A regularity of a value function is established. A numerical example of calculating value function is given

scholarly journals The Level Of Corporate Dividend Payout To Stockholders: Does Optimal Dividend Policy Exist For Firms Quoted At The Nairobi Stock Exchange?

2010 ◽  
Vol 9 (3) ◽  
Author(s):  
Bitok Kibet ◽  
Tenai Joel ◽  
Cheruiyot Thomas ◽  
Maru Loice ◽  
Kipsat Mary
Keyword(s):  
Dividend Policy ◽  
Stock Exchange ◽  
Secondary Data ◽  
High Growth ◽  
Dividend Payout ◽  
Optimal Dividend ◽  
Tax Systems ◽  
Optimal Dividend Policy ◽  
Blue Chip ◽  
Ownership Control

The objective of this study was to determine the level of corporate dividend payout to stockholders and establish if the optimal dividend policy exists for the firms quoted at the Nairobi Stock Exchange (NSE).  An analysis was done for the all the 43 firms trading in the main investment market at the Nairobi Stock Exchange.  Secondary data was obtained from the Nairobi Stock Exchange library, Internet & company libraries. Companies that were quoted at the stock exchange for a period of thirteen years and paid and/or did not pay dividends during that period were sampled. According to the findings of this study, the aggregate dividend payout ratio for the Kenyan market was obtained to be 44.14% for the period between 1991- 2003. The findings of this research suggest that the average corporate dividend payout to stockholders for 40% of the firms is low and stable and that 28% of the firms quoted paid out high and stable dividends. It was also observed that most of the firms that paid high and stable dividends are the blue chip firms, which are the main movers of trading at the NSE. The dividend model provides a summary of the factors that influenced and continue to influence the dividend decisions for this market including and not limited to the tax systems, clientele preferences, signaling, sustainability, low liquidity, high growth, ownership control and dividends as residual etc. From the model it is possible to predict the likely dividend decisions of the firms in future.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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