Jean-François Casta

Synergy modelling and financial valuation: the contribution of Fuzzy Integrals

Financial valuation are characterised by: the importance of the role assigned to human judgement in decision making, the use of qualitative information and the dominant role of subjective evaluation. The aim of the article is to examine the specific problems posed by modelling of the synergy between the assets of a firm. As a process which aggregates information and subjective opinions, the financial evaluation of the company raises very many problems relating to ideas of measurement, imprecision and uncertainty. The methods used in the process of financial evaluation are based on classic operators of aggregation possessing properties of additivity. Through their construction, these methods abandon the idea of expressing the phenomena of synergy (or redundancy) linked to over-additivity (or under-additivity) that may be observed between the elements of an organised set such as a firms assets. This synergy effect (or conversely, redundancy) may lead to the value of the set of assets being superior (inferior) to the sum of the values of each asset. This is particularly the case in the presence of intangible assets as goodwill. We will explore the possibilities offered by non-additive aggregation operators (Choquet, 1953; Grabisch et al., 1995; Sugeno, 1977) with the aim of modelling this effect with the help of fuzzy integrals (Casta and Bry, 1998; Casta and Lesage, 2001). More specifically, considering mainly subjective evaluation problems, we propose a formal model of financial valuation including the synergy.cf. working paper : BRY X. et J. F. CASTA : Synergy Modelling and Financial Valuation : Contribution of Fuzzy Integrals, Cahier de Recherche CEREG, n° 2003-4, Universite Paris Dauphine, 2003.http://www.dauphine.fr/cereg/cahiers.php?date=2003

Accounting and controlling in uncertainty: concepts,techniques and methodology

The fuzzy set approach has progressively been introduced into many areas of organisational science in order to compensate for certain inadequacies in traditional tools. Indeed behaviourists and expected utility researchers have long been studying the role of ambiguity and vagueness in the human decision making process (e.g., Einhorn and Hogarth, 1986) and have highlighted the paradoxes linked to the use of probability theory (e.g., Tverski et at, 1984). The organisational sciences are parti-cularly representative of systems with human interaction, in which information is affected by fuzziness (Zadeh, 1965). The areas of application for fuzzy set theory are characterised by: the importance of the role assigned to human judgement in decision making, the use of qualitative information, the dominant role of subjective evaluation and, more generally, the processing of information affected by non probabilistic uncertainty.

An Explanation of the Nature of Internally Generated Goodwill Based on Aggregation of Interacting Assets

Increasing internally generated goodwill (IGG) is another way of depicting the rising gap between market and accounting values sometimes referred as the “book-to-market black box”. Existing methods propose to value internally generated goodwill as the present value of abnormal earnings (e.g., residual income models) or to measure it indirectly through the excess of the enterprise value over the fair value of assets in a business combination. The critical drawback of these approaches is that they do not explain how the goodwill is created. In other words they do not enter into the “black box”. We propose an alternative valuation method based on the recognition that using an asset in combination with other assets leads to an interaction affecting firm value. In this context IGG emerges from an inadequate theory of aggregation of assets. Using Choquet’s capacities, which are non-additive aggregation operators, allows solving this adequacy issue as IGG arises as a consequence of specific synergies between assets. Our model is tested on the U.S. High Technology sector and benchmarked against the residual income model. To the extent of the accuracy to forecast enterprise value, our model performs better than the standard residual income model.

Financial Reporting and Fair Value: Where Do We Stand?

Over the past two decades, the accounting standards under which large companies determine and report their performance measures have led to much debate. Indeed, a wide-reaching movement, originally initiated by the U.S. Financial Accounting Standards Board (FASB), and spread at an international level by the International Accounting Standards Board (IASB), aimed to replace historical cost with the market-based concept of fair value. Fair value can potentially be used for measuring a large number of non-financial assets and liabilities (e.g. goodwill, post-retirement scheme values, share-based payments) and can therefore serve as the basis for a new corporate accounting model aiming to provide a more accurate view of the future cash flow estimates’ and investment opportunities’ uncertainties within financial reports. Based on the extent literature, this article discusses the usefulness of financial information disclosed under the fair value approach. In this respect, the key question—is fair value relevant?—will be analysed in a threefold way: (1) Do fair value-based “accounting numbers” help better estimate the value of a company and the intrinsic risk relating to its activity? (2) How informative are they for financial statements’ users? (3) How useful is fair value information for decision-making?

Connexionist Approach and Corporate Distress Diagnosis

Over the last thirty years, and in particular up to the mid-1980s, evaluation of the risk of corporate bankruptcy has been the subject of many empirical research projects, using mostly linear discriminant analysis. The methodology used in that research has generated a great deal of criticism, thus favouring the emergence of various alternative approaches. The most recent one, called the connexionist approach, allies the dynamics of complex systems and the neuro-mimetic paradigm. It applies the neural networks’ properties of learning and generalization (or self-organization) to the prediction of corporate bankruptcy. Paradoxically, given the degree of sophistication of the statistical techniques used, higher than in any other numerical induction procedure, empirical research on corporate bankruptcy remains dependent on the quality of the data and in particular their degree of completeness. This problem may be solved by employing one of the following two techniques: the elimination cf those companies whose data are incomplete or the use of econometric methods to complete the series (Hachette 1994). The removal of some companies from the sample — the most commonly used procedure — introduces a methodological bias whereby the incompleteness does not affect distressed companies and the others equally.