Engineering and technology students’ perceptions of courses

As cultural, social, political and economic changes take place, the secondary or high school curriculum should reflect and respond to changing needs and aspirations of students. Technology Education has been proactive in this arena as it has transformed over the decades to meet ever-changing societal needs. The most recent change to the discipline has been to add engineering and, as a result, adopting a new name and curriculum-Engineering and Technology Education. The added component and name change in Technology Education is causing discussions about what the new direction means, what professional preparation changes will be incurred, and what work graduating students will be prepared to do. In light of these changes, this study investigated perceptions of high school students in the United States of America about engineering and technology courses they take. To investigate whether students’ perceptions are in accord with current changes in Engineering and Technology Education, 316 students enrolled in engineering and technology courses in Georgia schools that have an affiliation with the Georgia Engineering and Technology Education Association (GETEA) were surveyed. According to data analyses, students’ perceptions can be divided into two factors. Educational Value of Course (factor 1) was extracted from statements measuring the degree to which the courses prepare students for employment and provide them with information regarding future employment. Personal Relevance of Course (factor 2) was derived from statements measuring students’ perceptions about links between engineering and technology education and their personal lives. Such findings suggest these students valued their engineering and technology courses, planned to continue their education, made good grades, and had varied types of career expectations for jobs such as design engineers and architects.     

R&D competition versus R&D cooperation in oligopolistic markets with evolving structure

This paper considers investment behavior of duopolistic firms subject to technological progress. It is assumed that initially both firms offer a homogeneous product, but after a stochastic waiting time they are able to implement a product innovation. Production capacities of both firms are product specific. It is shown that firms anticipate a future product innovation by under-investing (if the new product is a substitute to the established product) and higher profits, and over-investing (in case of complements) and lower profits, compared to the corresponding standard capital accumulation game. This anticipation effect is stronger in the case of R&D cooperation. Furthermore, since due to R&D cooperation firms introduce the new product at the same time, this leads to intensified competition and lower firm profits right after the new product has been introduced. In addition, we show that under R&D competition the firm that innovates first, overshoots in new-product capacity buildup in order to exploit its temporary monopoly position. Taking into account all these effects, the result is that, if the new product is neither a close substitute nor a strong complement of the established product, positive synergy effects in R&D cooperation are necessary to make it more profitable for firms than R&D competition.     

Employee mobility,spin‐outs,and knowledge spill‐in: How incumbent firms can learn from new ventures

Research summary: We consider conditions in which incumbent firms are particularly poised to benefit from knowledge spilling in from new ventures that employ individuals previously employed by the focal incumbent firm. We distinguish between inventors who leave their incumbent employers to found spin‐outs and those who become non‐founding employees of existing new ventures. Using a sample of new ventures and incumbent firms in the U.S. information technology (IT) sector, we find that incumbents are more likely to benefit from patented knowledge that spills in from their spin‐outs than from new ventures that employ non‐founding inventors formerly employed by the respective incumbent. Any advantage that parent firms have in reaping such knowledge quickly dissipates, however, when these parents have a history of misappropriating the intellectual property of others. Managerial summary: It has long been acknowledged that new ventures can acquire valuable knowledge from their larger and more established counterparts by hiring away their talented employees. We consider the possibility of a reverse flow of knowledge where established firms learn from those new ventures that have poached employees from them. We find that established information technology (IT) firms are more likely to learn and build on the technology of their spin‐outs (i.e., new ventures founded by their former inventors) than from new ventures that simply employ non‐founding inventors formerly employed by the respective IT firm. Any advantage that these IT firms had in reaping technical know‐how from their spin‐outs quickly dissipated, however, when they had a history of misappropriating the intellectual property of others. Copyright © 2016 John Wiley & Sons, Ltd.     

Asset Pricing Information in Vintage REIT Returns: An Information Subset Test

REIT return data prior to the new REIT era offer important asset pricing information. At issue is whether empiricists should focus attention on returns series covering only the new period. We use a generalized asset pricing and information subset test to disentangle REIT information from information available in several benchmark series. Results indicate that REIT returns are informative about the discounting process during the pre–new-era period. Thus, the distribution of vintage REIT returns is not fully explained by either broad market indexes or from size-based anomalies. This study should be viewed as a useful empirical precedent for those studying REIT data preceding the new REIT era.     

Valuation of default-sensitive claims under imperfect information

We propose a valuation method for financial assets subject to default risk, where investors cannot observe the state variable triggering the default but observe a correlated price process. The model is sufficiently general to encompass a large class of structural models and can be seen as a generalization of the model of Duffie and Lando (Econometrica 69:633–664, [2001]). In this setting we prove that the default time is totally inaccessible in the market’s filtration and derive the conditional default probabilities and the intensity process. Finally, we provide pricing formulas for default-sensitive claims and illustrate in particular examples the shapes of the credit spreads.      

Semiparametric diffusion estimation and application to a stock market index

The analysis of diffusion processes in financial models is crucially dependent on the form of the drift and diffusion coefficient functions. A new model for a stock market index process is proposed in which the index is decomposed into an average growth process and an ergodic diffusion. The ergodic diffusion part of the model is not directly observable. A methodology is developed for estimating and testing the coefficient functions of this unobserved diffusion process. The estimation is based on the observations of the index process and uses semiparametric and non-parametric techniques. The testing is performed via the wild bootstrap resampling technique. The method is illustrated on S&P 500 index data.     

Modeling default risk with support vector machines

Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our analysis is based on the Creditreform database. In all tests performed in this paper the nonlinear model classified by SVM exceeds the benchmark logit model, based on the same predictors, in terms of the performance metric, AR. The empirical evidence is in favor of the SVM for classification, especially in the linear non-separable case. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of SVM parameters. In terms of the empirical results obtained by SVM, the eight most important predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Some of the financial ratios selected by the SVM model are new because they have a strong nonlinear dependence on the default risk but a weak linear dependence that therefore cannot be captured by the usual linear models such as the DA and logit models.     

Basket trading under co-integration with the logistic mixture autoregressive model

In this paper, we propose a co-integration model with a logistic mixture auto-regressive equilibrium error (co-integrated LMAR), in which the equilibrium relationship among cumulative returns of different financial assets is modelled by a logistic mixture autoregressive time series model. The traditional autoregression (AR) based unit root test (ADF test), used in testing co-integration, cannot give a sound explanation when a time series passes the ADF test. However, its largest root in the AR polynomial is extremely close to, but less than, one, which is most likely the result of a mixture of random-walk and mean-reverting processes in the time series data. With this background, we put an LMAR model into the co-integration framework to identify baskets that have a large spread but are still well co-integrated. A sufficient condition for the stationarity of the LMAR model is given and proved using a Markovian approach. A two-step estimating procedure, combining least-squares estimation and the Expectation-Maximization (EM) algorithm, is given. The Bayesian information criterion (BIC) is used in model selection. The co-integrated LMAR model is applied to basket trading, which is a widely used tool for arbitrage. We use simulation to assess the model in basket trading strategies with the statistical arbitrage feature in equity markets. Data from several sectors of the Hong Kong Hang Seng Index are used in a simulation study on basket trading. Empirical results show that a portfolio using the co-integrated LMAR model has a higher return than portfolios selected by traditional methods. Although the volatility in the return increases, the Sharpe ratio also increases in most cases. This risk–return profile can be explained by the shorter converging period in the co-integrated LMAR model and the larger volatility in the ‘mean-reverting’ regime.     

The representation of American options prices under stochastic volatility and jump-diffusion dynamics

This paper considers the problem of pricing American options when the dynamics of the underlying are driven by both stochastic volatility following a square-root process as used by Heston [Rev. Financial Stud., 1993, 6, 327–343], and by a Poisson jump process as introduced by Merton [J. Financial Econ., 1976, 3, 125–144]. Probability arguments are invoked to find a representation of the solution in terms of expectations over the joint distribution of the underlying process. A combination of Fourier transform in the log stock price and Laplace transform in the volatility is then applied to find the transition probability density function of the underlying process. It turns out that the price is given by an integral dependent upon the early exercise surface, for which a corresponding integral equation is obtained. The solution generalizes in an intuitive way the structure of the solution to the corresponding European option pricing problem obtained by Scott [Math. Finance, 1997, 7(4), 413–426], but here in the case of a call option and constant interest rates.