What is an Investment Project's Implied Rate of Return?

Bornholt, Graham N.

doi:10.1111/abac.12093

Abacus

2016

DOI: 10.1111/abac.12093

|View full text |Cite

What is an Investment Project's Implied Rate of Return?

Graham N. Bornholt

Abstract: How to measure a project's implied rate of return has long been an unresolved problem, except for some special cases. This paper derives return on present cost (ROPC) as the correct measure of an investment project's implied rate of return. The IRR is a biased measure except for projects classified as simple projects, and this bias is likely to be substantial in many real-world applications. Thus while net present values should be used to determine whether to accept/reject projects, I recommend that analysts u… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

2019

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(), Patatoukas (), Christodoulou et al . () and Bertomeu (); Bornholt (); and for other valuation contexts see Alcock and Steiner (), and Van der Spek () and Bertomeu and Cheynel ().…”

mentioning

confidence: 99%

Corporate distress prediction in China: a machine learning approach

Jiang

Jones

2018

Accounting & Finance

View full text Add to dashboard Cite

Rapid growth and transformation of the Chinese economy and financial markets coupled with escalating default rates, rising corporate debt and poor regulatory oversight motivates the need for more accurate distress prediction modelling in China. Given China's historical, social and cultural intolerance towards corporate failure, this study examines the Special Treatment system introduced by Chinese regulators in 1998. Regulators can assign Special Treatment status to listed Chinese companies for poor financial performance, financial abnormality and other events. Using an advanced machine learning model known as TreeNet Ò we model more than 90 predictor variables, including financial ratios, market returns, macro-economic indicators, valuation multiples, audit quality factors, shareholder ownership/control, executive compensation variables, corporate social responsibility metrics and other variables. Based on out-of-sample tests, our TreeNet Ò model is 93.74 percent accurate in predicting distress (a Type I error rate of 6.26 percent) and 94.81 percent accurate in predicting active/healthy companies (a Type II error rate of 5.19 percent). Variables with the strongest predictive value in the TreeNet Ò model includes market capitalization and annual market returns, macro-economic variables such as gross domestic product growth, financial ratios such as retained earnings to total assets and return on assets; and certain non-traditional variables such as executive compensation.

show abstract

“…(), Patatoukas (), Christodoulou et al . () and Bertomeu (); Bornholt (); and for other valuation contexts see Alcock and Steiner (), and Van der Spek () and Bertomeu and Cheynel ().…”

mentioning

confidence: 99%

Corporate distress prediction in China: a machine learning approach

Jiang

Jones

2018

Accounting & Finance

View full text Add to dashboard Cite

show abstract

A machine learning analysis of citation impact among selected Pacific Basin journals

Jones

Alam

2019

Accounting & Finance

View full text Add to dashboard Cite

This study uses a machine learning approach to identify and predict factors which influence citation impacts across five Pacific Basin journals: Abacus, Accounting & Finance, Australian Journal of Management, Australian Accounting Review and the Pacific Accounting Review from 2008 to 2018. The machine learning results indicate that citation impact is mostly influenced by: length of a journal article; the field of research (particularly environmental accounting), sample size; whether the sample is local or international; choice of research method (e.g., archival vs survey/interview); academic rank of the first author; institutional status of the first author; and number of authors of the article. The results may be useful for predicting future trends in citation impact as well as providing strategies for authors and editors to improve citation impact.

show abstract

Early Termination Clauses for Leasing Contracts with APR Cap

Quattrocchio¹,

Tibiletti²,

Uberti³

2018

IJBM

View full text Add to dashboard Cite

Terminating a leasing contract early may entail the payment of additional charges attributable to penalty and late payment costs. The occurrence of these extra charges push the lease effective Annual Percentage Rate (APR) up. The aim of this note is to discuss the contract early termination extra charge conditions which guarantee the no exceedance of a given APR threshold, whenever the contract expires. In the event the contract provides the lessee the option to terminate the lease prior to the first payment, extra charges shoot APR extraordinarily up and so the penalty costs should be set at zero. In the occurrence that the contract terminates upon the first payment date due to lessee’s exercise of the early termination option, the most severe compliance conditions are those if the termination occurs at the first payment date. If the early termination occurs for lessee’s insolvency, the most severe compliance condition is at correspondence of the first admissible date of the contract redemption for insolvency after a minimum number of unpaid payments. The late payment cost compliance condition requires only that the late payment interest rate not exceeds the given APR cap. Our findings hold whatever the payback amortization is set at the date of entering into the contract. If the French amortization (fixed installments) is used, the compliance constrains turn out looser than those valid for a general amortization.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

What is an Investment Project's Implied Rate of Return?

Cited by 3 publications

References 8 publications

Corporate distress prediction in China: a machine learning approach

Corporate distress prediction in China: a machine learning approach

A machine learning analysis of citation impact among selected Pacific Basin journals

Early Termination Clauses for Leasing Contracts with APR Cap

Contact Info

Product

Resources

About