Analyzing market performance via social media: a case study of a banking industry crisis

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine-learning disciplines.However,the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market.This article aims to address these challenges by proposing a multistage dynamic analysis framework.In this framework,we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm.We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters.Then,we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis.Finally,we discuss some interesting and significant results,which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis.     

System architecture evaluation using modular performance analysis: a case study

Performance analysis plays an increasingly important role in the design of embedded real-time systems. Time-to-market pressure in this domain is high while the available implementation technology is often pushed to its limit to minimize cost. This requires analysis of performance as early as possible in the life cycle. Simulation-based techniques are often not sufficiently productive. We present an alternative, analytical, approach based on Real-Time Calculus. Modular performance analysis is presented through a case study in which several candidate architectures are evaluated for a distributed in-car radio navigation system. The analysis is efficient due to the high abstraction level of the model, which makes the technique suitable for early design exploration. This work has been carried out as part of the boderc project under the responsibility of the Embedded Systems Institute     

Critical evaluation of quadratic logistic discriminant analysis methods: a case study

Electrocardiographic measurements from two groups, patients with left ventricular hypertrophy and clinically normal cases, were analyzed to illustrate the performance of the linear logistic discrimination method in the case of very unequal covariance matrices. Two quadratic extensions of the linear model were critically investigated. The use of graphical methods to check the model has been stressed and illustrated.     

Adaptability in smart business networks: An exploratory case in the insurance industry

A focal characteristic of Smart Business Networks (SBN) is their ability to adapt to the environment. We think that the capacity to adapt, or adaptability of Smart Business Networks, requires more attention and formalization in the managerial discourse since we consider that it is one core capability of SBN to be considered “Smart”. The purpose of this paper is to employ theories of learning from the educational and organizational literature to develop a framework for modes of network. These modes, which are a function of the organizational awareness and the resources employed, are: 1) automatic responses, 2) assimilation, 3) accommodation, and 4) environmental enactment. We show the applicability of the framework with a case study about a SBN in the home insurance claims and repairs industry. The paper highlights the need for SBN management to design process and technology infrastructures that appropriately allocate limited organizational awareness and resources to optimize the process of adaptation in Smart Business Networks. We consider that the theoretical propositions may serve to guide future research in Smart Business Networks.     

DEA window analysis for assessing efficiency of blistering process in a pharmaceutical industry

Neural Computing and Applications - This research evaluates the efficiency of blistering lines over a 2-year period starting January 2013 till December 2014 using data envelopment analysis models....     

Using gravity models for the evaluation of new university site locations: A case study

A fundamental aspect of competitive spatial models is the choice behaviour of potential customers to patronize a facility. Most of the models used to describe this phenomenon are essentially based on the adaptation of Newton's law of gravitation to the economic case (gravity model).     

Reengineering with reflexion models: a case study

Reengineering large and complex software systems is often very costly. The article presents a reverse engineering technique and relates how a Microsoft engineer used it to aid an experimental reengineering of Excel-a product that comprises about 1.2 million lines of C code. The reflexion technique is designed to be lightweight and iterative. To use it, the user first defines a high-level structural model, then extracts a map of the source code and uses a set of computation tools to compare the two models. The approach lets software engineers effectively validate their high-level reasoning with information from the source code. The engineer in this case study-a developer with 10-plus years at Microsoft-specified and computed an initial reflexion model of Excel in a day and then spent four weeks iteratively refining it. He estimated that gaining the same degree of familiarity with the Excel source code might have taken up to two years with other available approaches. On the basis of this experience, the authors believe that the reflexion technique has practical applications     

Simulation smoothing for state-space models: A computational efficiency analysis

Simulation smoothing involves drawing state variables (or innovations) in discrete time state-space models from their conditional distribution given parameters and observations. Gaussian simulation smoothing is of particular interest, not only for the direct analysis of Gaussian linear models, but also for the indirect analysis of more general models. Several methods for Gaussian simulation smoothing exist, most of which are based on the Kalman filter. Since states in Gaussian linear state-space models are Gaussian Markov random fields, it is also possible to apply the Cholesky Factor Algorithm (CFA) to draw states. This algorithm takes advantage of the band diagonal structure of the Hessian matrix of the log density to make efficient draws. We show how to exploit the special structure of state-space models to draw latent states even more efficiently. We analyse the computational efficiency of Kalman-filter-based methods, the CFA, and our new method using counts of operations and computational experiments. We show that for many important cases, our method is most efficient. Gains are particularly large for cases where the dimension of observed variables is large or where one makes repeated draws of states for the same parameter values. We apply our method to a multivariate Poisson model with time-varying intensities, which we use to analyse financial market transaction count data.     

Artificial neural network models for lot-sizing problem: a case study

Lot-sizing is one of the most difficult problems in production planning. The main purpose of this study is to propose a new lot-sizing based on artificial neural network (ANN), which may lead to a better performance than commonly used lot-sizing heuristics (SM, EOQ, PPB, LUC, and LTC). The data obtained are the results of years 2004 thru 2009 for 186 different types of stock items from the 2nd Air Supply and Maintenance Centre Command, a state-funded factory in Kayseri, Turkey. Factual data were applied under the coverage of the study, and the system from which the data have been obtained is still in live and active status. In the study, the purchasing costs, holding costs, and set-up costs were taken into consideration. These data were obtained from the administration data system of the enterprise. The solutions of this lot-sizing heuristics were found by WinQSB software accordingly. The ANN was constituted by using the NeuroSolutions software. The criterion of deviation from the optimum solution and the criterion of percentage of times obtaining the optimum order pattern were taken into account for the comparison purposes. The performance values of 400 ANNs were compared to lot-sizing methods. MS Excel and Visual Basic Macro were utilized for all calculations applied after this stage. The results showed that the proposed ANN-based method outperformed all lot-sizing methods taken into account in this study.     

Observability analysis of nonlinear tubular (bio)reactor models: a case study

This paper deals with the observability analysis of nonlinear tubular bioreactor models. Due to the lack of tools for the observability analysis of nonlinear infinite-dimensional systems, the analysis is performed on a linearized version of the model around some steady-state profile, in which coefficients can be functions of the spatial variable. The study starts from an example of tubular bioreactor that will serve as a case study in the present paper. It is shown that such linear models with coefficients dependent on the spatial variable are Sturm–Liouville systems and that the associated linear infinite-dimensional system dynamics are described by a Riesz-spectral operator that generates a C₀ (strongly continuous)-semigroup. The observability analysis based on infinite-dimensional system theory shows that any finite number of dominant modes of the system can be made observable by an approximate point measurement.     

Formal models of communication services: a case study

Formal methods can play an important role in exploring new communication systems services. The telecommunications and data communications communities have long accepted the need for formally describing protocols, but only recently have they considered formally describing a service by abstracting specifications from a particular protocol that provides that service. Specifying a service at an abstract level meets two important needs: standardization and customization. The author presents a simplified atomic multicast as an example service and input/output automata for the formal model. He shows how to represent the service specification, a protocol, and implementations of that protocol. He also sketches how to prove the correctness of the protocol and implementation, that is, how to show that the specified service is actually provided     

Integer linear programming for a cutting problem in the wood-processing industry: a case study

In this paper, the authors present a case study from the wood-processing industry. It focuses on a cutting process in which material from stock is cut down in order to provide the items required by the customers in the desired qualities, sizes, and quantities. In particular, two aspects make this cutting process special. Firstly, the cutting process is strongly interdependent, with a preceding handling process, which, consequently, cannot be planned independently. Secondly, if the trim loss is of a certain minimum size, it can be returned into stock and used as input to subsequent cutting processes. In order to reduce the cost of the cutting process, a decision support tool has been developed that incorporates an integer linear programming model as a central feature. The model is described in detail, and experience from the application of the tool is reported.     

Applications of uncertainty-based mental models in organizational learning: A case study in the Indian automobile industry

In this paper, we discuss the applicability of qualitative and quantitative reasoning techniques to study the process of Organizational Learning. We have used cognitive maps of a company (for the past five years) taken from the Indian automobile industry to understand the Organizational Learning process. We have conducted stochastic simulation experiment on an uncertainty-based cognitive map (the latest year). We generated scenarios for the future and analysed each scenario with respect to data obtained from the past five-years cognitive maps, in light of the theory on Organizational Learning.     

Global optimization models in data networks: a case study

In this paper we present a methodology for a rural and semi-urban data network placement. In order to optimally place the network and to ensure that the network is realistic and viable we address four key issues, namely, the demographic and socio-economic issues, geographical estimation, optimization of the network placement and financial optimization. A digital representation of the map of the region where the network has to be placed is used. A continuous optimization algorithm is applied to optimally place the backbone rings, and a combinatorial optimization algorithm is applied to obtain the optimal rollout order for the network. Mathematical formulations for both the optimization problems are presented. Optimal financial indicators are obtained.     

Operational methods for improving manufacturing control plans: case study in a semiconductor industry

This study presents operational methods which improves tools control plan. To face challenges linked with quality, cost, cycle time, development and environment, semiconductors industries set classical process control methods. However many interactions between product-processes and tools are not exploited in practice for fine tuning controls operations and detecting premises of non conformities occurrences.     

Preliminary research on customer satisfaction models in Taiwan: A case study from the automobile industry

For our research we applied the TCSI model to the automobile industry in Taiwan. The subjects of our research were consumers of new cars in Taiwan during the years 2006–2008. According to statistics provided by the Taiwan Transportation Vehicle Manufacturers Association (TTVMA) Toyota, Ford, Nissan, and Mitsubishi Motors made up for 79% of all cars sold in Taiwan from January through June, 2008. Questionnaires were issued randomly to four companies. We used structural equation modeling (SEM) to manifest the model’s suitability and develop a standard questionnaire to obtain the customer’s opinions. The conclusions reached provide valuable insight into the companies and industry and can help them to develop strategies to increase their competitiveness.     

Robustness analysis: a case study

The stability test of polynomials whose coefficients depend multilinearly on interval parameters is considered. The authors describe and compare four brute-force solution approaches. These are eigenvalue calculation, zero exclusion from a specified value set, algebraic tests of real and complex Hurwitz roots, and the parameter space method. They are applied to a simple example with two parameters and third-order polynomial. An interesting feature of the example is that it can have an isolated unstable point. The example may be useful as a benchmark for future approaches to the multilinear problem. All four methods are shown to be feasible for the simple example, but they require effort     

Performance evaluation of the efficiency of Philippine Private Higher Educational Institutions: application of frontier approaches

This paper evaluates the efficiency performance of Philippine Private Higher Educational Institutions (PHEIs), using frontier approaches known as data envelopment analysis – Malmquist indices and stochastic frontier analysis. The sample consists of 30 private educational institutions, which were granted autonomy over the time period 1999–2003, with a total of 150 observations. The paper also analyzes whether technical inefficiency is systematically related to age, ownership (private sectarian versus private non‐sectarian), and autonomy status. Significant new findings show that higher technological progress boosted the higher productivity growth of the majority of PHEIs (about 66.67%). We also found that age and ownership have positive and statistically significant effects on its technical inefficiency. PHEIs obtained a technical efficiency of 71.2%, and therefore need to increase their output revenues by 28.8% to be efficient. These new findings contribute significantly to educational performance.     

Models of data envelopment analysis and stochastic frontier analysis in the efficiency assessment of universities

This paper systematizes the empirical results on efficiency concepts applied to higher education institutions, data envelopment analysis (DEA) adjusted to heterogeneous samples, inputs and outputs chosen for these institutions and factors tended to make universities efficient. Special attention is paid to the consistency of results yielded by different models.