The Impact on Electric Utility Bond Ratings of Substituting Debt for Preferred Stock

1979 ◽  
Vol 8 (1) ◽  
pp. 51 ◽  
Author(s):  
Richard B. Edelman
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rodrigo E. Peimbert-García ◽  
Jesús Isaac Vázquez-Serrano ◽  
Jorge Limón-Robles

PurposeLiterature shows that the economics of early failures in maintenance and electric utilities have not been deeply analyzed. This study aims to focus on quantifying the economic impact that early failures in current transformers have on total maintenance costs. The empirical study is conducted in a regional transmission division of an electric utility located in Mexico.Design/methodology/approachThe utility's database was accessed to collect 219 maintenance records. Clustering techniques were used to identify early failures from a bimodal distribution of failures. Confirmatory goodness-of-fit procedures followed the analysis, and finally, direct and opportunity costs were estimated by adapting the cost-of-quality (PAF) Model.FindingsAround 11% of all maintenance activities are triggered by early failures, and they account for up to US$2.2m during the eight-year period under study, which represents 16% of total maintenance costs. Additionally, opportunity costs represent close to two-thirds of the total costs due to early failures. This was obtained after finding and validating a clear-cut border of 3.5 months between early failures and the rest.Originality/valueFailures in energy grids and power transmission can have a large economic impact on the power industry and the society in general. Thus, the maintenance function in equipment such as current transformers is a crucial entry of the budget of any electric utility. This study is one of the very few that highlights the magnitude and importance of direct and opportunity costs derived from early failures.


2020 ◽  
Vol 17 (1) ◽  
pp. 15-23
Author(s):  
Zainal Abidin Sahabuddin ◽  
Bram Hadianto

Issuing bonds is one of the alternative ways for non-financial companies to get money from the public besides borrowing money from banks. Compared with getting money banks, obtaining money from the bond market is slightly economical because the companies are not essential to borne the intermediation cost anymore. As a consequence, the companies in the bond market will get the assessment from the appointed agency. Furthermore, the rating of bonds will determine their reputation. Mentioning the literature review, the bond ratings are affected by the features of the supervisory board: size, independence, and audit committee. Therefore, this research intends to attain two goals. Firstly, it aims to prove and analyze the impact of the supervisory board size and independence, as well as the audit committee size on the company’s possibility to get a high bond rating with profitability as the control variable. Secondly, it intends to know the accuracy rate of grouping the company bond ratings through the classification matrix.The population originates from the non-financial companies. The total samples are determined by the Slovin formula with a boundary of the fault of 10%. Based on this formula, the total samples are 36 companies. Furthermore, they are randomly grabbed from the population. The ordered probit regression model and the classification matrix are utilized to analyze the data. Based on the data analysis, this research finds out that the supervisory board size and independence, the audit committee size, and profitability positively affect the bond ratings. It means that the number of the commissioner board and the members of the audit committee have to be added until achieving the maximum level to monitor the performance of the directors so that the company can reach a high bond rating. To sum up, board governance is effective in improving the company’s bond rating.


2018 ◽  
Vol 19 (1) ◽  
pp. 51-73 ◽  
Author(s):  
John A. Dove ◽  
Courtney A. Collins ◽  
Daniel J. Smith

1964 ◽  
Vol 19 (4) ◽  
pp. 693
Author(s):  
Donald E. Fischer

2018 ◽  
Vol 25 (5) ◽  
pp. 572-574 ◽  
Author(s):  
Dustin McEvoy ◽  
Michael L Barnett ◽  
Dean F Sittig ◽  
Skye Aaron ◽  
Ateev Mehrotra ◽  
...  

Abstract Objective To assess the impact of electronic health record (EHR) implementation on hospital finances. Materials and Methods We analyzed the impact of EHR implementation on bond ratings and net income from service to patients (NISP) at 32 hospitals that recently implemented a new EHR and a set of controls. Results After implementing an EHR, 7 hospitals had a bond downgrade, 7 had a bond upgrade, and 18 had no changes. There was no difference in the likelihood of bond rating changes or in changes to NISP following EHR go-live when compared to control hospitals. Discussion Most hospitals in our analysis saw no change in bond ratings following EHR go-live, with no significant differences observed between EHR implementation and control hospitals. There was also no apparent difference in NISP. Conclusions Implementation of an EHR did not appear to have an impact on bond ratings at the hospitals in our analysis.


2006 ◽  
Vol 22 ◽  
pp. 97-121 ◽  
Author(s):  
Aaron D. Crabtree ◽  
Duane M. Brandon ◽  
John J. Maher

The Winners ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 49
Author(s):  
Erin Wijayanti ◽  
Indah Yuliana

The research aimed to assess the impact of the Risk Profile on the banking industry bond ratings in Indonesia Stock Exchange (IDX) and have a rating for bonds at PT PEFINDO. Sampleswere selected by purposive sampling method. The population were banks listed on the Indonesia Stock Exchange in 2015-2018. The population was 44 banks and 16 banks were selected as samples. The analysis a used descriptive statistics and Partial Least Square (PLS) for testing structural and structural models. The results show that Non-Performing Loan (NPL)and Loan to Deposit Ratio (LDR) directly have a significant direct positive effect on bond ratings, and security directly do not have a significant effect on bond ratings, security strengthen risk relationships credit with a bond rating. However, security weakens the relationship between liquidity risk and the bond rating. The variables indicate that these variables can explain the bond rating of 44,4% while the remaining 55,6% is influenced by other variables not contained in the research model.


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