Intangible asset | EPFL Graph Search

An intangible asset is an asset that lacks physical substance. Examples are patents, copyright, franchises, goodwill, trademarks, and trade names, as well as software. This is in contrast to physical assets (machinery, buildings, etc.) and financial assets (government securities, etc.). Intangible assets are usually very difficult to value. They suffer from typical market failures of non-rivalry and non-excludability. Today, a large part of the corporate economy (in terms of net present value) consists of intangible assets. Definition Intangible assets may be one possible contributor to the disparity between "company value as per their accounting records", as well as "company value as per their market capitalization". Considering this argument, it is important to understand what an intangible asset truly is in the eyes of an accountant. A number of attempts have been made to define intangible assets:

The Australian Accounting Standards Board included examples of intangibl

THE SIGNALING VALUE OF REPUTATION AND STATUS: EVIDENCE FROM VC-BACKED ENTREPRENEURIAL VENTURES IN THE CLEANTECH SECTOR

Giovanni Liotta, Anu Wadhwa

We contribute to the ongoing debate on the differential effects of two important intangible assets on organizational outcomes: reputation and status. We explore whether they exert distinct signaling effects on external parties whom evaluate the quality of a company under uncertainty, and if their signaling value depends upon the information asymmetry of external parties receiving the signal. We investigate these questions using a sample of 1,339 U.S. entrepreneurial ventures in the clean technology sector that received the first round of venture capital (VC) finance between the years 1990 and 2012. By looking at their ability to gather supplemental financial resources for survival and growth through initial public offerings (IPOs) or acquisitions, we find that reputation of a venture’s VC ties – a performance-based signal – has a positive signaling effect on public investors at IPOs but does not influence managers of acquiring companies. Furthermore, status of VC ties – a signal of social-standing – does not seem to affect external parties’ judgments neither for IPOs nor for acquisitions.

2016

COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the indices' optimal weights dynamically in line with a customized data-driven Bellman's procedure. We use basic asset classes (equities, fixed income, cash and cash equivalents) and five corresponding indices for the development of optimal strategies. In our multi-period setup, the probability model describing the uncertainty about the value of asset returns changes over time and is scenario-specific. Given a high enough variation of model parameters, this allows to account for possible crises events. In this article, we construct optimal allocation strategies accounting for the influence of the COVID-19 pandemic on financial returns. We observe that the growth in the number of infections influences financial markets and makes assets' behavior more dependent. Solving the multi-stage asset allocation problem dynamically, we (i) propose a fully data-driven method to estimate time-varying conditional probability models and (ii) we implement the optimal quantization procedure for the scenario approximation. We consider optimality of quantization methods in the sense of minimal distances between continuous-state distributions and their discrete approximations. Minimizing the well-known Kantorovich-Wasserstein distance at each time stage, we bound the approximation error, enhancing accuracy of the decision-making. Using the first-stage allocation strategy developed via our method, we observe ca. 10% wealth growth on average out-of-sample with a maximum of ca. 20% and a minimum of ca. 5% over a three-month period. Further, we demonstrate that monthly reoptimization aids in reducing uncertainty at a cost of maximal wealth. Also, we show that optimistically off setted distribution parameters lead to a reduction in out-of-sample wealth due to the COVID-19 crisis.

AMER INST MATHEMATICAL SCIENCES-AIMS2021

Proceedings of EVA-TREN’s 1st Experts’ Workshop on Transport and Energy Infrastructure Appraisal in Europe : Theoretical Basis in Perspective

Tristan Chevroulet

The European Union needs harmonised guidelines to improve the appraisal of large-scale infrastructure dedicated to the Trans-European Networks (TEN); in order to contribute to such guidelines, EU-funded EVA-TREN analyses ex-ante studies and projects outcomes at European level. EVA-TREN’s First Experts’ Workshop has gathered experts of large-scale infrastructure planning and operation of the domain of transport and energy. Investigation of their practices highlights the following issues: first, the appraisal frameworks EU countries apply for transport and energy projects differ considerably in scope, sophistication, methodology and parameter values; second, the EU Member States share only a small part of all research results; and third, transnational projects are still problematic within the Union. As a result, cost overruns appear in the majority of projects. Investigation of the EU Cohesion Fund programme reveals that one project in four costs more than 20% above budget, while only one in five stands below + 10%. The main problems are modifications to the project (30%) and delays (25%); inadequate cost estimates and technical reasons are quoted in only 20% of overcosts. Sustainability does not explicitly appear in the appraisal process, even though it is repeatedly quoted as a central aspect of the decision whether an infrastructure should be built or not. Transport and energy projects essentially differ in finance and elasticity: most infrastructure investments in the transport sector require public funding, whereas those in the energy sector usually do not need any; the situation is similar for operation at regional level. In the transport sector, provision of new road capacities induces additional transport demand, while provision of new electricity lines has very little effect on demand. In terms of methodology, the quality of evaluations would benefit from increased transparency and from improved feed-back, as would provide for instance peer review of ex-ante assessment and more systematic ex-post evaluations. Combination of methods may as well contribute to better appraisal. Two approaches based on Cost Benefit Analysis (CBA) look promising: Netherlands’ Overview Effects Infrastructure (OEI) and Japan’s combination of CBA and Multi-Criteria Analysis (MCA). OEI puts emphasis on the exploration of the problem and on the survey of effects, while Japan’s procedure relies on MCA for ranking projects amongst those that score sufficiently well in CBA outcome and it allows other projects to be reassessed considering intangible values, which gives them a second chance. Experts consider macroeconomic models very bad at providing data that are meaningful for CBA; in the case spatial dynamics is tackled at national level only, and therefore produces data that are not detailed enough for assessments at regional or local level, the missing data should be generated through regional/local scenarios. In practice, the match between evaluation results and project outcomes would be improved if authorities take actions on four topics: to start with, they should use masterplans; then, they should only select mature projects - for which they request measurable and quantified goals, results and impacts; third, they should establish a clear managerial body; and fourth, they should provide assistance on administrative and financial matters as well as methodological support on assessment procedure.