“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion and a bubble regime represented by the stochastic super-exponential Sornette and Andersen (Int J Mod Phys C 13(2):171–188, 2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005–2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is some predictability obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.     

Defect Detection Using Unanimous Vote Among Mahalanobis Classifiers for Each Color Component

In manufacturing industries, product inspection is automated and the use of image data is increasingly being employed for defect detection. A manufacturing company in Japan produces an item and inspects the produced products using image data. Reducing the error rate is important in product inspection because poor inspection of products might lead to the delivery of defective products to consumers (consumer’s risk) and strict inspection increases production cost (producer’s risk). To reduce the error rate, we highlighted fault points using a two-dimensional moving range filter and discriminated defect production through a unanimous vote among Mahalanobis classifiers for each color component. For results, we achieved a lower error rate than the current system. This research is an empirical study of how to use image data in defect detection.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.     

Supervisors and Presenteeism: How do Supervisors Accommodate and Support Skilled Workers with Chronic Health Concerns?

There is a broad literature linking social support to health, which includes evidence that social support from supervisors is positively associated with worker health. In addition, once workers experience a health concern, supervisors may play a key role in supporting workers, keeping them on the job despite health issues. However, there are few studies that investigate the day-to-day strategies supervisors utilize to support their workers and help them stay on the job despite health concerns. The study presented in this paper addresses this gap. Thirty-two supervisors from three companies were interviewed about their experiences supporting and accommodating skilled workers during flare-ups of a health condition. A grounded theory approach was used to analyze the text data and identify themes. Three key themes emerged from the data: 1) workplace policies provide the structure for mutual problem solving; 2) communication skills and existing relationships increase supervisor effectiveness; and 3) strategies were aimed at individual, interpersonal, and institutional supports. Supervisors regularly interact with workers to support and accommodate episodes of illness or pain. While supervisors employ novel and effective strategies, there is an opportunity to improve the range of strategies, better understand and utilize company policies, and facilitate appropriate communication about worker health concerns.     

Testing measurement invariance using multigroup CFA: differences between educational groups in human values measurement

This article applies the testing procedures for measurement invariance using multigroup confirmatory factor analysis (MGCFA). It illustrates these procedures by investigating the factorial structure and invariance of the Portraits Value Questionnaire (PVQ, Schwartz et al.: J. Cross Cult. Psychol. 32(5), 519–542 (2001)) across three education groups in a population sample (N  =  1,677). The PVQ measures 10 basic values that Schwartz postulates to comprehensively describe the human values recognized in all societies (achievement, hedonism, self-direction, benevolence, conformity, security, stimulation, power, tradition and universalism). We also estimate and compare the latent means of the three education groups. The analyses show partial invariance for most of the 10 values and parameters. As expected, the latent means show that less educated respondents attribute more importance to security, tradition, and conformity values.