Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA–discriminant analysis

This study compares data envelopment analysis–discriminant analysis (DEA–DA) with Altman’s financial ratio analysis to identify the position of DEA–DA in financial performance analysis. Then, this study applies DEA–DA to examine whether Research and Development (R&D) expenditure influences the financial performance of Japanese machinery industry and electric equipment industry. The investigation of DEA–DA identifies that the R&D expenditure makes a positive impact on the financial performance of Japanese machinery industry, but it yields a negative impact on Japanese electric equipment industry. The result implies that the influence of R&D expenditure on financial performance (including the avoidance of bankruptcy) depends upon the type of a manufacturing industry. A rationale regarding why such a discrepancy has occurred between the two Japanese manufacturing industries is because the life cycle of electric equipments is shorter than that of the machinery products. Furthermore, the electric equipment industry faces more fierce competition than the machinery industry. This study suggests that the Japanese electric equipment industry needs R&D expenditure for competition in its global market. However, it is a high risk and high return investment. In contrast, the Japanese machinery is a technologically mature industry where the R&D expenditure influences positively its financial performance. In this sense, the R&D expenditure is a low risk and necessary investment.     

Strategic optimization in R&D investment

We use d'Aspremont and Jacquemin's strategic optimal R&D investment in a duopoly Cournot competition model to construct myopic optimal discrete and continuous R&D dynamics. We show that for some high initial production costs, the success or failure of a firm is very sensitive to small variations in its initial R&D investment strategies.     

Climate change and optimal energy technology R&D policy

Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.     

Real R&;D options with time-to-learn and learning-by-doing

We model R&D efforts to enhance the value of a product or technology before final development. Such efforts may be directed towards improving quality, adding new features, or adopting technological innovations. They are implemented as optional, costly and interacting control actions expected to enhance value but with uncertain outcome. We examine the interesting issues of the optimal timing of R&D, the impact of lags in the realization of the R&D outcome, and the choice between accelerated versus staged (sequential) R&D. These issues are also especially interesting since the history of decisions affects future decisions and the distributions of asset prices and induces path-dependency. We show that the existence of optional R&D efforts enhances the investment option value significantly. The impact of a dividend-like payout rate or of project volatility on optimal R&D decisions may be different with R&D timing flexibility than without. The attractiveness of sequential strategies is enhanced in the presence of learning-by-doing and decreasing marginal reversibility of capital effects.     

R&;D Equilibrium Strategies with Surfers

A typical assumption in the game-theoretic literature on research and development (R&D) is that all firms belonging to the industry under investigation pursue R&D activities. In this paper, we assume that the industry is composed of two groups; the first (the investors) is made of firms that have R&D facilities and are involved in this type of activity. The second group corresponds to firms that are inactive in R&D (the surfers). The latter group benefits from its competitors’ R&D efforts, thanks to involuntary spillovers. This division of the industry is in line with actual practice, where indeed not all firms are engaged in costly and risky R&D. We adopt a two-stage game formalism where, in the first stage investors decide on their levels of investment in R&D, and in the second stage all firms compete à la Cournot in the product market. We characterize and analyze the unique subgame perfect Nash equilibrium. Research supported by NSERC, Canada. F. Ben Abdelaziz is on leave at The College of Engineering, American University of Sharjah, UAE.     

Construction of Nonlinear Stabilizer for Trajectories of Economic Growth

A dynamic model of optimization of R&D intensity is studied for analyzing the effect of the spillover technology assimilation on techno-economic growth. The research focuses on the issue of a reasonable balance in the R&D investment policy between the indigenous technology stock and exogenous technology flow. On the basis of the concavity properties of the Hamiltonian, a nonlinear stabilizer sustaining proportional techno-economic growth is constructed. Trends of optimal R&D intensity are examined depending on the values of the model macroeconomic parameters and the feedback variables. Econometric analysis shows that additional investments and restructuring of these sources for knowledge absorption could have the effect of increasing returns and provide a strong leverage for reaching qualitatively higher levels of sales, technology development, and consumption index. The research was sponsored by the SIMOT Program of the Japanese Ministry of Education, Science and Technology. A.M. Tarasyev was supported by the Russian Fund for Basic Research, Grants 05-01-00601, 05-01-08034, Russian Fund for Humanities, Grant RFH 05-02-02118a, and by the Program for the Sponsorship of Leading Scientific Schools, Grant NSCH-791.2003.1.     

A generalized real option pricing method of R&D investments: jump diffusion and external competition

ABSTRACT

Numerous studies have assessed Research and Development (R&D) investment using the real option pricing approach. This paper proposes a more general real option pricing method that both considers the specificity of R&D investment (such as uncertainty) and the R&D investment opportunity of a business in a market environment with external competitors. Specifically, we adopt a jump diffusion model to evaluate R&D investments that incorporate the uncertainties of these activities. The model values a pioneer's R&D investment opportunity allowing the chance that competitors may enter the market and the project value may vary with time. By construction and analysis of the model, we then analyse the optimal timing to realize profit on an investment. Overall, this model should facilitate a more comprehensive evaluation for R&D investments.     

Are economically advanced countries more efficient in basic and applied research?

Research and development (R&D) of countries play a major role in a long-term development of the economy. We measure the R&D efficiency of all 28 member countries of the European Union in the years 2008–2014. Super-efficient data envelopment analysis (DEA) based on robustness of classification into efficient and inefficient units is adopted. We use the number of citations as output of basic research, the number of patents as output of applied research and R&D expenditures with manpower as inputs. To meet DEA assumptions and to capture R&D characteristics, we analyze a homogeneous sample of countries, adjust prices using purchasing power parity and consider time lag between inputs and outputs. We find that the efficiency of general R&D is higher for countries with higher GDP per capita. This relation also holds for specialized efficiencies of basic and applied research. However, it is much stronger for applied research suggesting its outputs are more easily distinguished and captured. Our findings are important in the evaluation of research and policy making.     

OPTIMAL R&D FOR NONRENEWABLE RESOURCES: THE CASE OF COST REDUCING TECHNOLOGY

A model is developed to determine optimal R&D spending and completion time when R&D results in lower extraction costs of a nonrenewable resource deposit. Examples of R&D projects for which the model is designed are in-situ leaching for mining and carbon dioxide injection in petroleum. The model is a combined R&D/nonrenewable resource model (CM). Results from the CM are compared to simulations of an R&D model which ignores the nonrenewable resource. The comparison demonstrates the importance of including resource parameters in the R&D spending model. The CM extends the literature by considering R&D spending and exhaustible natural resource production simultaneously. It demonstrates the importance of including the resource deposit when R&D affects the deposit. This is important because more accurate models of R&D will increase the profitability of the R&D projects.     

Evaluating pharmaceutical R&D under technical and economic uncertainty

This study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic uncertainty is modeled as a standard diffusion process which incorporates both up-and downward shocks. Practical application of this method is emphasized through a case analysis. We show that both uncertainties have a positive impact on the R&D option value. Moreover, from the sensitivity analysis, we find that the sensitivity of the option with respect to economic uncertainty and market introduction cost decreases when technical uncertainty increases.     

Multiobjective data envelopment analysis

Data envelopment analysis (DEA) is popularly used to evaluate relative efficiency among public or private firms. Most DEA models are established by individually maximizing each firm's efficiency according to its advantageous expectation by a ratio. Some scholars have pointed out the interesting relationship between the multiobjective linear programming (MOLP) problem and the DEA problem. They also introduced the common weight approach to DEA based on MOLP. This paper proposes a new linear programming problem for computing the efficiency of a decision-making unit (DMU). The proposed model differs from traditional and existing multiobjective DEA models in that its objective function is the difference between inputs and outputs instead of the outputs/inputs ratio. Then an MOLP problem, based on the introduced linear programming problem, is formulated for the computation of common weights for all DMUs. To be precise, the modified Chebychev distance and the ideal point of MOLP are used to generate common weights. The dual problem of this model is also investigated. Finally, this study presents an actual case study analysing R&D efficiency of 10 TFT-LCD companies in Taiwan to illustrate this new approach. Our model demonstrates better performance than the traditional DEA model as well as some of the most important existing multiobjective DEA models.     

On finding the most BCC-efficient DMU: A new integrated MIP–DEA model

This paper proposes a new integrated mixed integer programing – data envelopment analysis (MIP–DEA) model to improve the integrated DEA model which was introduced by Toloo & Nalchigar [M. Toloo, S. Nalchigar. A new integrated DEA model for finding most BCC–efficient DMU. Appl. Math. Model. 33 (2009) 597–60]. In this study some problems of applying Toloo & Nalchigar’s model are addressed. A new integrated MIP–DEA model is then introduced to determine the most BCC-efficient decision making unit (DMU). Moreover, it is mathematically proved that the new model identifies only a single BCC-efficient DMU by a common set of optimal weights. To show applicability of proposed models, a numerical example is used which contains a real data set of nineteen facility layout designs (FLDs).     

Evaluation of credit risk based on firm performance

This paper investigates whether productive inefficiency measured as the distance from the industry’s ‘best practice’ frontier is an important ex-ante predictor of business failure. We use samples of French textiles, wood and paper products, computers and R&D companies to obtain efficiency estimates for individual firms in each industry. These efficiency measures are derived from a directional technology distance function constructed empirically using non-parametric data envelopment analysis (DEA) methods. Estimating binary and ordered logit regression models we find that productive efficiency has significant explanatory power in predicting the likelihood of default over and above the effect of standard financial indicators.     

On the prisoner’s dilemma in R&D with input spillovers and incentives for R&D cooperation

This paper considers a standard model of strategic R&D with spillovers in R&D inputs, and extends the result that duopoly firms engaged in a standard two-stage game of R&D and Cournot competition end up in a prisoner’s dilemma situation for their R&D decisions, whenever spillover effects and R&D costs are relatively low. In terms of social welfare, this prisoner’s dilemma always works to the advantage of both consumers and society. This result allows a novel and enlightening perspective on some issues of substantial interest in the innovation literature. In particular, the incentive firms face towards R&D cooperation in the form of an R&D cartel is shown to be maximal for the case of zero spillovers, which is when the prisoner’s dilemma has the largest scope.     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.     

A dynamic analysis of corporate investments and emission tax policy in an oligopoly market with network externality

We investigate the product innovation, green R&D investments and the emission tax policy in an oligopoly market with network externality. It is shown that an appropriate tax policy should be deployed to effectively control pollution and motivate innovation. At the early stage of the market, the emission tax should gradually reduce to motivate firms to achieve optimal investments. Later at the mature stage, the emission tax policy should carefully consider both the market competition and green technology levels.     

Relating the perspectives of the balanced scorecard for R&D by means of DEA

The objective of this article is to propose a framework for analysis of the relationships between the four perspectives of the balanced scorecard (BSC) of Kaplan and Norton. To this end, several different models of efficiency have been developed, utilising data envelopment analysis (DEA). Each of the variables has been extracted from a model of the BSC for research and development (R&D) activities. A study has been carried out with 90 companies to illustrate a case of this analysis.     

Optimizing venture capital investments in a jump diffusion model

We study two practical optimization problems in relation to venture capital investments and/or Research and Development (R&D) investments. In the first problem, given the amount of the initial investment and the cash flow structure at the initial public offering (IPO), the venture capitalist wants to maximize overall discounted cash flows after subtracting subsequent investments, which keep the invested company solvent. We describe this problem as a mixture of singular stochastic control and optimal stopping problems. The second problem is concerned with optimal dividend policy. Rather than selling the company at an IPO, the investor may want to harvest technological achievements in the form of dividend when it is appropriate. The optimal control policy in this problem is a mixture of singular and impulse controls. E. Bayraktar was supported in part by the National Science Foundation, under grant DMS-0604491.     

Expenditure patterns and timing of patent protection in a competitive R&D environment

In this paper we consider a stochastic R&D decision model for a single firm operating in a competitive environment. The study focuses on the firm's optimal policy which maximizes the expected discounted net return from the project. The firm's policy is composed of two ingredients: a stopping time which determines when the developed technology should be introduced and protected by a patent, and an investment strategy which specifies the expenditure rate throughout the R&D program. The main findings of the study are:

• (a) 

  Under a constant expenditure rate strategy, the optimal stopping time of the project is a control limit policy of the following form: stop whenever the project's state exceeds a fixed critical value, or when a similar technology is introduced and protected by one of the firm's rivals, whichever occurs first.

• (b) 

  For a R&D race model in which the winner-takes-all competition and the loser's return is zero, we show that the firm's optimal expenditure rate throughout the R&D program increases monotonically as a function of the project's state.

In order to gain a better insight regarding optimal R&D programs in competitive markets we examine the effect of key economic parameters on the firm's optimal policy.