nep-cfn New Economics Papers
on Corporate Finance
Issue of 2018‒10‒01
five papers chosen by
Zelia Serrasqueiro
Universidade da Beira Interior

  1. Government Intervention, Innovation, and Entrepreneurship By Meng Wei Chen; Yu Chen; Zhen-Hua Wu; Ningru Zhao
  2. Young SMEs: Driving innovation in Europe? By Veugelers, Reinhilde; Ferrando, Annalisa; Lekpek, Senad; Weiss, Christoph T.
  3. Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange By Mabe, Queen Magadi; Lin, Wei
  4. How does the state destroy incentives in innovation financing? By Berlinger, Edina
  5. Model Risk in Real Option Valuation By Carol Alexander; Xi Chen

  1. By: Meng Wei Chen (Indiana University at Bloomington, USA); Yu Chen (University of Graz, Austria); Zhen-Hua Wu (Nanjing University, China); Ningru Zhao (Nanjing Audit University, China)
    Abstract: We study how government intervention affects innovation and entrepreneurship, using a model in which two agents (e.g., one entrepreneur and one venture capitalist) engage in teamwork to launch a new business in which a moral hazard problem may persist for both parties. One feature of our model is that the government's financial support (grant) may have (positive) externalities on the teamwork of both parties, but is also constrained by budget costs. We compare two major forms of government intervention: indirect intervention and direct intervention. In the former, government intervention always raises the efforts of both parties and promotes social surplus (welfare). In the latter, government intervention may not always raise the efforts of both parties or promote social surplus relative to the case without government intervention. It may, however, deliver even higher social surplus than indirect financing when the government's share in the enterprise is dominant and its marginal contribution to the project is sufficiently high.
    Keywords: Government intervention; Double moral hazard; Direct financing; Indirect financing; Innovation, entrepreneurship
    JEL: G24 G32 G34 D80 D86
    Date: 2018–09
  2. By: Veugelers, Reinhilde; Ferrando, Annalisa; Lekpek, Senad; Weiss, Christoph T.
    Abstract: Using large scale EIB Investment Survey evidence for 2016 covering 8,900 non-financial firms from all size and age classes across all sectors and all EU Member States, we identify different innovation profiles based on a firm's R&D investment and/or innovation activities. We find that "basic" firms - i.e. firms that do not engage in any type of R&D or innovation - are more common among young SMEs, while innovators - i.e. firms that do R&D and introduce new products, processes or services- are more often old and large firms. This hold particularly for "leading innovators", ie those introducing innovations new to the market. To further explore why young SMEs are not more active in innovation, we explore their access to finance. We confirm that young small leading innovators are the most likely to be credit constrained. Grants seem to at least partly addressing the external financing access problem for leading innovators, but not for young SMEs.
    Keywords: young small companies,innovation,access to finance
    JEL: G24 O31 O38
    Date: 2018
  3. By: Mabe, Queen Magadi; Lin, Wei
    Abstract: The aim of this paper is to estimate the probability of default for JSE listed companies. Our distinctive contribution is to use the multi-sector approach in estimating corporate failure instead of estimating failure in one sector, as failing companies are faced with the same challenge regardless of the sectors they operate in. The study creates a platform to identify the effect of Book-value to Market-value ratio on the probability to default, as this variable is often used as a proxy for corporate default in asset pricing models. Moreover, the use of Classification and Regression Trees uncovers other variables as reliable predictors to estimate corporate failure as the model is designed to choose the covariates with respect to classification ability. Our study also serves to add to the literature on how Logistic model performance compares to Machine Learning methods such as Classification and Regression Trees and Support Vector Machines. The study is the first to apply Support Vector Machines to predict failure on South African listed companies.
    Keywords: Corporate default, Logistic Regression, Support Vector Machines, Classification and Regression Trees.
    JEL: C61 G33
    Date: 2018–08–08
  4. By: Berlinger, Edina
    Abstract: We investigate the effect of state subsidy on the behavior of entrepreneur and venture capitalist in a double moral hazard and fixed investment model under positive externalities. We infer that investment subsidy and success fee improve the incentives, ease credit rationing, hence boost private financing, which explains the popularity of hybrid venture capital systems. The main disadvantage of these systems is, however, that the entrepreneur is encouraged to minimize his/her own capital investment and to ask for the maximal state subsidy available. It may happen that public sources go to entrepreneurs capable to finance their projects privately, so state subsidies increase state deficit (and private profits) without any effects on public welfare leaving other important areas underfinanced. We also prove that state guarantee definitely creates perverse incentives, hence it is not recommended in our model.
    Keywords: innovation financing, venture capital, state subsidy, moral hazard
    JEL: D21 G38 H32 H50 O38
    Date: 2018–09
  5. By: Carol Alexander; Xi Chen
    Abstract: We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercise opportunities, the size of the project relative to initial wealth, the investor's risk tolerance (and how it changes with wealth) and several other choices about model structure. For instance, contrary to stylized facts from previous literature, real option values can actually decrease with the volatility of the underlying project value and increase with investment costs.
    Date: 2018–09

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