The Internal Rate of Return Approach and the AIRR Paradigm: A Refutation and a Corroboration

Magni, Carlo Alberto

doi:10.1080/0013791x.2012.745916

Cited by 84 publications

(75 citation statements)

References 28 publications

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“…More recently, other unsuspected flaws of the IRR were unearthed (see Magni 2013a for a compendium of 18 flaws) and a complete solution to the conceptual and mathematical problems of the rate-of-return notion has been advanced in Magni (2010a) and then developed in a subsequent series of papers (e.g., Magni, 2013aMagni, ,b, 2014aMagni, ,b, 2015a. The author turns the issue on its head by getting rid of the IRR equation and of the assumption that the growth rate for capital is constant.…”

Section: Hartman Andmentioning

confidence: 99%

“…The value-weighted average of such rates is (34). ∎ (See also Magni 2013aMagni , 2014a. 22 Remark 7.3 The above proposition says that the IC-AIRR is the first-period disequilibrium rate of return generated in an efficient market.…”

Section: Bailey (1959) and Efficient Markets ̶ Shareholder Rate Of Rementioning

confidence: 99%

“…For example, as seen in section 8, book-value depreciation (and, therefore, AROA) seems to be compelling for capital investments, corporate projects, project financing transactions, where pro forma financial statements are available (see Magni 2013a, Bosch-Badia et al 2014, Magni 2015a, Mørch et al 2016 or for capturing a firm's economic profitability in a given interval of time (see also Peasnell 2012, 2015), for which accounting data are often available. In these cases, the use of the IRR is erroneous, for it is associated with a depreciation class [ ] which negates the depreciation class [ ] which has been used for deriving the prospective cash flows.…”

Section: Insights For Future Research (Iii)mentioning

confidence: 99%

“…And if only some values are known, while others are unknown, they can be interpolated in various ways (see Magni 2014b, Jiang 2017. Even the IRR benefits from the AIRR approach in case of time-variant COCs (see Magni 2013a, pp. 105-107, Magni 2014b, section 6.…”

Section: Insights For Future Research (Iii)mentioning

confidence: 99%

“…A rate of return is singled out only if a well-defined statement upon capital depreciation is made by the analyst, implicitly or explicitly. The resulting approach is the so-called average internal rate of return (AIRR), which has been introduced in Magni (2010a) and developed in a vast array of subsequent papers (e.g., Magni 2011aMagni , 2013aMagni ,b 2014aMagni ,b, 2015a.…”

Section: Introductionmentioning

confidence: 99%

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Capital depreciation and the underdetermination of rate of return: A unifying perspective

Magni

2016

Journal of Mathematical Economics

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Abstract. This paper shows that the notion of rate of return is best understood through the lens of the average-internal-rate-of-return (AIRR) model, first introduced in Magni (2010a). It is an NPVconsistent approach based on a coherent definition of rate of return and on the notion of Chisini mean, it is capable of solving the conundrums originated by the rate-of-return notion and represents a unifying theoretical paradigm under which every existing measure of wealth creation can be subsumed. We show that a rate of return is underdetermined by the project's cash-flow stream; in particular, a unique return function (not a unique rate of return) exists for every project which maps depreciation classes into rates of return. The various shapes a rate of return can take on (internal rate of return, average accounting rate of return, modified internal rate of return etc.) derive from the (implicit or explicit) selection of different depreciation patterns. To single out the appropriate rate of return for a project, auxiliary assumptions are needed regarding the project's capital depreciation. This involves value judgment. On one side, this finding opens terrain for a capital valuation theory yet to be developed; on the other side, it triggers the creation of a toolkit of domainspecific and purpose-specific metrics that can be used, jointly or in isolation, for analyzing the economic profitability of a given project. We also show that the AIRR perspective has a high explanatory power that enables connecting seemingly unrelated notions and linking various disciplines such as economics, finance, and accounting. Some guidelines for practitioners are also provided.

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Section: Hartman Andmentioning

confidence: 99%

Section: Bailey (1959) and Efficient Markets ̶ Shareholder Rate Of Rementioning

confidence: 99%

Section: Insights For Future Research (Iii)mentioning

confidence: 99%

Section: Insights For Future Research (Iii)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Capital depreciation and the underdetermination of rate of return: A unifying perspective

Magni

2016

Journal of Mathematical Economics

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A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Babaei

Bamdad

2020

Intell Sys Acc Fin Mgmt

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Summary This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.

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