• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.9
• /
• pp.39-49
• /
• 2020

This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.317-327
• /
• 2020

R&D(Research and Development) efficiency analysis is a very important issue in academia and industry. Although many studies have been conducted to analyze R&D(Research and Development) efficiency since the past, studies that analyzed R&D(Research and Development) efficiency considering both patentability and patent quality efficiency according to the financial performance of a company do not seem to have been actively conducted. In this study, measuring the patent application and patent quality efficiency according to financial performance, patent quality efficiency according to patent application were applied to corporate groups related to artificial intelligence hardware technology defined as GPU(Graphics Processing Unit), FPGA(Field Programmable Gate Array), ASIC(Application Specific Integrated Circuit) and Neuromorphic. We analyze the efficiency empirically and use Data Envelopment Analysis as a measure of efficiency. This study examines which companies group has high R&D(Research and Development) efficiency about artificial intelligence hardware technology.

• Journal of Construction Engineering and Project Management
• /
• v.2 no.4
• /
• pp.42-45
• /
• 2012

In construction industry, the term 'procurement' is considered as a project based job where clients and contractors are always keen to observe performance indicators. These indicators represent financial and non-financial efficiency of project activities. Among these, the monitoring of financial indicators such as cost monitoring is an ongoing process and its importance cannot be undermined during the project life cycle. It can be monitored by using traditional approach of direct reporting of actual cost against budget. However, the comparison of budget versus actual spending does not indicate the worth of the work which is completed at any given time. This approach does not represent the true cost performance of the project. Because of these limitations, this paper discusses the applications of Earned Value Analysis (EVA) for cost monitoring of construction projects in Malaysia. Besides traditional approach, EVA is a three-dimensional approach that compares three cost indicators i.e. the budgeted value of work scheduled with the earned value of physical work completed and the actual cost of work completed. Therefore, cost monitoring by EVA is an objective measure of actual work performed. This paper uses a case study, an example application of EVA as a cost monitoring tool. This case study reaffirms the benefits of using EVA for project cash flow analysis and forecasting.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.41 no.1
• /
• pp.127-143
• /
• 2016

This study applied DEA analysis to measure the relative efficiency of 35 companies that produce automobile body components. First, the input and output, the improvement target value of the calculated variables, and the reference group for benchmarking for inefficient groups to become efficient groups were established through DEA analysis. In addition, whether inefficiency was due to technical inefficiency or size was analyzed in connection with the cases of the actual companies through the measurement of scale efficiency. Second, a route for efficiency improvement was derived through DEA-Tier analysis by defining the possible group for benchmarking in actuality within the production industry of automobile body components where the primary cooperative company belonged. Third, the financial variables that generate the difference between efficient and inefficient groups were derived through logistic regression analysis. Financial strategies that determine the direction the indices should be improved to allow the inefficient group to become an efficient one were recommended. This research is expected to provide diagnostic methods for management efficiency and the direction of improvement to enhance the management efficiency of automotive parts makers by identifying the causes of the inefficiency of domestic automotive parts makers empirically. The study also provides financial strategies together with the target values of efficiency improvement for each individual company.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.75-83
• /
• 2016

We propose methodology to analyze the dynamic mechanisms of financial market contagion under market integration using a biological contagion analytical approach. We employ U-statistic to measure market integration, and a dynamic model based on an error correction mechanism (single equation error correction model) and latent factor model to examine market contagion. We also use quantile regression and Wald-Wolfowitz runs test to test market contagion. This methodology is designed to effectively handle heteroscedasticity and correlated errors. Our simulation results show that the single equation error correction model fits well with the linear regression model with a stationary predictor and correlated errors.

• World Technopolis Review
• /
• v.4 no.3
• /
• pp.144-156
• /
• 2015

Globalization and knowledge-based economy have increased the importance of local areas as the units of global competition. Therefore, the meaning of localities has been emphasized as the core value of economic activities. In this context, innovation cluster has been recognised and emphasized as effective policy measure for innovation. Therefore, most countries have been trying to develop innovation clusters with their expectation for a rapid growth of economy. Nevertheless, there have been minimal empirical researches on innovation cluster. Therefore, for suggesting implications that activation factors of innovation cluster are to have an effect on tenant's business activities, this study conducted a literature review for the theories of regional innovation system(RIS) and innovation cluster. As a result, the activation factors of innovation cluster were classified into institutional, physicals, and social factor. The case of Gyeonggi province's innovation cluster policy was examined for an empirical analysis. Data were analyzed using ordered logistic regression. The results were as follows:First, Institutional and Infra factors had a positive influence on firms' business activities in every empirical test, so they were the most important activation factors of innovation cluster. Second, regarding the interactive effects of financial support, the interactive effects between financial support and Infra factor had a positive influence on the firms' business activities, according to the result of the empirical test.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.8
• /
• pp.289-296
• /
• 2020

The study investigates the association between the effectiveness of the board of directors and the likelihood that a company receives a modified audit opinion (as a measure of the quality of companies' external financial reporting) in Malaysia. The sample companies were extracted from the population of publicly-available information mainly the annual reports of publicly-listed companies on the Bursa Malaysia. 136 firm-year observations listed on Bursa Malaysia were identified to examine the relationship between the effectiveness of the board of directors and a modified audit opinion. Data used in this study are collected from two separate sources - annual reports and Datastream. Any missing financial figure from Datastream was acquired from the annual reports. To test the study's hypotheses, we use the pooled cross-sectional logistic regression analysis for 136 firm-year observations listed on Bursa Malaysia over the period 2009-2011. The evidence we have uncovered is consistent with the hypothesis that companies with large score of the board of directors' effectiveness are less possible to receive a modified audit opinion. Therefore, the result confirms that the combined effect of the board of directors' characteristics has a significant negative association with the likelihood of the companies receiving a modified audit opinion.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.12
• /
• pp.2240-2250
• /
• 2016

MIFARE Classic is the most popular contactless smart card, which is primarily used in the management of access control and public transport payment systems. It has several security features such as the proprietary stream cipher Crypto 1, a challenge-response mutual authentication protocol, and a random number generator. Unfortunately, multiple studies have reported structural flaws in its security features. Furthermore, various attack methods that target genuine MIFARE Classic cards or readers have been proposed to crack the card. From a practical perspective, these attacks can be partitioned according to the attacker's ability. However, this measure is insufficient to determine the optimal attack flow due to the refined random number generator. Most card-only attack methods assume a predicted or fixed random number, whereas several commercial cards use unpredictable and unfixable random numbers. In this paper, we propose optimal MIFARE Classic attack procedures with regards to the type of random number generator, as well as an adversary's ability. In addition, we show actual attack results from our portable experimental setup, which is comprised of a commercially developed attack device, a smartphone, and our own application retrieving secret data and sector key.

• KDI Journal of Economic Policy
• /
• v.33 no.4
• /
• pp.27-48
• /
• 2011

This paper investigates the extent of global and regional integration in East Asia using stock price index as a measure of economic performance. We employ a structural VAR model to separate the underlying shocks into "global", "regional" and "country-specific" shocks. The estimation results show that country-specific shocks still play a dominant role in East Asia although their role appears to have declined over time, especially after the 1997 financial crisis. Global and regional shocks are responsible for small but increasing shares of stock price fluctuations in all countries. The results indicate that the stock markets in East Asia remain dissimilar and are subject to asymmetric shocks in comparison to European countries.

• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.682-693
• /
• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.