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This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

This paper studies hedging strategies that use the KOSDAQ50 index futures to hedge the price risk of the KOSDAQ50 index spot portfolio. This study uses the minimum variance hedge model and bivariate ECT-GARCH (1,1) model as hedging models, and analyzes their hedging performances. The sample period covers from January 31, 2001 to December 31, 2002. The most important findings may be summarized as follows. First, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios of the risk-minimization tend to be higher than those of GARCH model. Second, for the in-sample data, hedging effectiveness of GARCH model is higher than that of the risk-minimization, while for the out-of-sample data, hedging effectiveness of the risk-minimization with constant hedge ratios is not far behind the GARCH model in its hedging performance. Third, the hedging performance of KOSDAQ50 index futures is lower than that of KOSPI200 index futures, but higher than that of KTB futures. In conclusion, in the KOSDAQ50 index market, investors are encouraged to use the simple risk-minimization model to hedge the price risk of KOSDAQ50 spot portfolios.

The purpose of this paper is to propose a methodology to reduce the potential risk of failures in high‐volume manufacturing.

The research documented here represents an even balance of theoretical and practical development, with validation of the methodology – referred to as integrated risk minimisation (IRM).

The success of the IRM is measured in terms of reduced defect rates, through either prediction or earlier detection of defects. The combination of an inline design and immediate operator feedback on all detected defects was a critical design element.

The electroplating process was chosen as a suitable case study to provide the platform from which the IRM could be developed and tested. Implementation into industry has generated the proof to support the IRM as a methodology that can successfully reduce potential risks in high‐volume manufacturing.

In a unique approach, failure modes and effects analysis is built into the IRM methodology, thus establishing a “closed loop” process. The IRM methodology is suitably generic, to allow the achievement of similar results for any high‐volume process.

Although the modern portfolio theory (MPT) asset allocation framework can be adopted to enable decision making for international and direct real estate investing, and that many institutional investors adopt it to support their decision making, this framework can be enhanced to capture the multi‐causal factors influencing international and direct real estate investing. The purpose of this paper is to explain how a fuzzy decision‐making approach is a more intuitive, yet rigorous alternative in this regard.

This paper is concerned with the model formation and estimation of a unique fuzzy tactical asset allocation (FTAA), which in turn comprises the FTAA flexible programming model and the FTAA robust programming model.

Both these FTAA models enhance the classical, Markowitz MPT portfolio theory on asset allocation through making it more intuitively appropriate for decision making in international and direct real estate investing.

These two FTAA models achieve the benefits of intuitively greater risk diversification by city or real estate sector and enable effective risk management. These two short‐run fuzzy models would be accepted and more such models would emerge as an effective extension of quadratic programming optimization, as more computable software programs of this kind are widespread.

Fuzzy approaches to asset allocation in the short run, are limited by some drawbacks. Fuzzy models possess the common feature of converting the equality function under quadratic programming optimization into inequality functions. Such inequality optimization replaces the point solution of the MPT TAA optimization problem, obtained through the rigid intersection of all functions, via a generalized or intuitive answer over a defined space of alternatives. The product of the fuzzy process with fuzzy inputs, in the form of fuzzy outcome is in actual fact a more natural and intuitive approach to asset optimization.

Standard market risk optimization tools, based on assumptions of normality, are ineffective for evaluating credit risk. In this article, the authors develop three scenario optimization models for portfolio credit risk. They first create the trading risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk‐return efficient frontier is constructed using parametric programming. While scenario optimization of quantile‐based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. The three models are applied to optimizing the risk‐return profile of a portfolio of emerging market bonds.

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.

The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

Contrary to the mainstream born global (BG) perspective, some previous studies report the incremental expansion of BGs. In addition, the reasons behind BGs initiating specific steps, if any, and BGs’ entry market choices are still unknown or rather contrasting. This study views that such contrasting findings may be attributed to the contexts in which BGs operate. Within the context of consumer goods BGs, the purpose of this paper is to examine the entry market choices and post-entry growth patterns, and investigate the underlying reasons.

This study adopted in-depth historiographic case research from seven Korean BGs in the consumer goods sector that demonstrated success in internationalization. Multiple sources were used to gather data from each case. A total of 14 interviews, approximately two one-on-one interviews per firm, were the major means of data collection.

The findings revealed that first entry market choices among BGs functioned largely as attempts at emergent opportunities. However, after the first wave of entry into countries with available selling opportunities, entry market choice became a simultaneous pursuit of strategic markets and emergent selling opportunities. BGs focusing on image-oriented consumer goods appeared more strategic when entering the world’s leading markets to gain brand reputation. The analyses of internationalization processes revealed three patterns, which collectively implied that each move to the next stage came from a strategic decision to solve the problems related to survival and strategic visions for growth.

One contribution of this paper is the provision of empirical evidence for entry market choices among consumer goods BGs. The findings suggest that BGs’ entry market choices may not be a simple matter of simultaneous expansion to the world’s lead market. Instead, they may comprise more strategic decision. While previous studies have suggested such evolutionary or path-dependent internationalization processes, this study is among the first to reveal specific growth patterns and the possible reasons behind them.

Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining.

The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining.

The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis.

Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

Although there has been a commitment to develop a policy framework to support vulnerable adults at risk of abuse, there remains concern around its lack of use within National Health Service inpatient settings and mental health services in particular. A gap between policy and practice appears to have developed, which leaves inpatients vulnerable to inadequate responses to allegations of adult abuse. This article will provide a critical overview of the policy and practice issues that affect the use of adult protection procedures.