• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.74-84
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• 2013

Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disrup-tion is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and op-erational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.1-11
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• 2022

The study incorporates model uncertainty into the private equity (PE) valuation model (SWY model) (Sorensen et al., 2014) to evaluate how model uncertainty distorts the leverage and valuations of PE funds. This study applies a continuous-time model to PE project valuation, modeling the LPs' goal as multiplier preferences provided by Anderson et al. (2003), and assuming that LPs' aversion to model uncertainty causes endogenous belief distortions with entropy as a measure of model discrepancies. Concerns regarding model uncertainty, according to the theoretical model, have an unclear effect on LPs' risk attitude and GPs' decision, which is based on the value of the PE asset. It also demonstrates that model uncertainty lowers the certainty-equivalent valuation of the LPs. Finally, we compare the outcomes of the Full-spanning risk model with the Non-spanned risk model, and they match the intuitive economic reasoning. The most important implication is that model uncertainty will have negative effects on the LPs' certainty-equivalent valuation but has ambiguous effects on the portfolio allocation choice of liquid wealth. Our works contribute to two literature streams. The first is the literature that models the PE funds. The second is the literature introduces model uncertainty into standard finance models.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.133-152
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• 2007

In this paper, we examine a portfolio selection model in which a safety-first investor maximizes expected return subject to a downside risk constraint. We use the Value-at-Risk as the downside risk measure. We exploit the fact that returns are fat-tailed, and use a semi-parametric method suggested by Jansen, Koedijk and de Vries(2000). We find a more realistic asset allocation than the one suggested by the literature based on the traditional mean-variance framework. For the robustness check, we provide empirical analyses using empirical quantiles. The results highlight that for optimal portfolio selection involving downside risks that are far in the tails of the distribution, our mean-VaR model with a fat-tailed distribution is superior.

• Journal of the Korean housing association
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• v.26 no.1
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• pp.109-118
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• 2015

The purpose of this research is to examine generational distinctions of housing investment and household wealth for two different age cohorts - 'baby-boomers' and 'eco-boomers'. In so doing, national survey data of two different periods were analyzed and the primary results are summarized as follows; aggregate assets of both generations have risen, and the rising household debt for baby-boomers was related to loans for living expenses while eco-boomer's debt was ascribed to home-buying loans. In the midst of economic slowdown, the age cohorts had conservative asset allocation in preference for risk-averting investment like savings. The main purpose of saving and investment was distinctive across the groups? retirement for baby-boomers and home purchase for eco-boomers. Both groups prioritized reduction of household liabilities and also were cautious on investing in real estate. Still home-buying was considered to be an important driver for asset accumulation. While baby-boomers were unwilling to dispose any form of owned real estate, eco-boomers found it challenging to take on a long-term investment like home-buying, especially in economic uncertainties. Rather the young generation would diversify asset allocation with better-returning investment commodities like stocks, bonds and derivatives.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.11-16
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• 2013

This paper presents a unified control algorithm of independent braking and steering for collision avoidance. The desired motion of the vehicle in the yaw plane is determined using the probabilistic risk assessment method based on target state estimation. For the purpose of coordinating the independent braking and steering, a non-linear vehicle model has been developed, which describes the vehicle dynamics in the yaw plane in both linear and extended non-linear ranges of handling. A control allocation algorithm determines the control inputs that minimize the difference between the desired and actual vehicle motions, while satisfying all actuator constraints. The performance of the proposed control algorithm has been investigated via computer simulations conducted using the vehicle dynamics software CARSIM and Matlab/Simulink.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.1-10
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• 1998

Testing high reliability devices under nomal operating condition is difficult, because the devices are not likely to fail in the relatively short time available for tests. For most applications it is necessary to accelerate the causes of failure by increasing a stress above its nomal value. Previous accelerated life test(ALT) plans have shown how to find optimum allocation, lowest stress and sample size subject to minimizing the variance of mean life estimator. In these ALT plans, the highest acceptable test-stress was assumed to be specified in advance by the experimenter but there is no guidance for selecting it. This assumption is, however, inappropriate for many applications. Testing devices at too-high stress levels can invalidate the extrapolation model, or introduce failure mechanisms that are not anticipated under nomal operating conditions. In this paper, we propose new 4:2:1 compromise plans using Min-Max method to minimize this risk and present minimized test-stress levels(max, middle, min), and find sample allocation based on Min-Max 4:2:1 compromise plans. In result, we compare previous 4:2:1 compromise plans specified maximum test-stress with Min-Max 4:2:1 compromise plans minimized maximum test-stress.

• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.55-75
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• 1986

This article is to analyse the economic efficiency of capital market, which plays a role of resource allocation in terms of financial claims such as stock and bond. It provides various contributions to the welfare theoretical aspects of modern capital market theory. The key feature that distinguishes the theory described here from traditional welfare theory is the presence of uncertainty. Securities has time dimensions and the state and outcome of the future are really uncertain. This problem resulting from this uncertainty can be solved by complete market, but it has a weak power to explain real stock market. Capital Market is faced with the uncertainity because it is a kind of incomplete market. Individuals and firms in capital market made their consumption-investment decision by their own criteria, i. e. the maximization of expected utility form intertemporal consumption and the maximization of the market value of firm. We noted that allocative decisions that had to be made in the economy could be naturally subdivided into two groups. One set of decisions concerned the allocation of first-period resources among consumption $C_i$, investment in risky firms $I_j$, and riskless investment M. The other decisions concern the distribution among individuals of income available in the second period $Y_i(\theta)$. Corresponing to this grouping, the theoretical analysis of efficiency has also been dichotomized. The optimality of the distribution of output in the second period is distributive efficiency" and the optimality of the allocation of first-period resources is 'the efficiency of investment'. We have found in the distributive efficiency that the conditions for attainability is the same as the conditions for market optimality. The necessary and sufficient conditions for attainability or market optimality is that (1) all utility functions are such that -$\frac{{U_i}^'(Y_i)}{{U_i}^"(Y_i)}={\mu}_i+{\lambda}Y_i$-linear risk tolerance function where the coefficients ${\mu}_i$ and $\lambda$ are independent of $Y_i$, and (2) there are homogeneous expectations, i. e. ${\Large f}_i(\theta)={\Large f}(\theta)$ for every i. On the other hand, the efficiency of investment has disagreement about optimal investment level. The investment level for market rule will not generally lead to Pareto-optimal allocation of investment. This suboptimality is caused by (1)the difference of Diamond's decomposable production function and mean-variance valuation model and (2) the selection of exelusive investment or competitive investment. In conclusion, this article has made an analysis of conditions and processes of Pareto-optimal allocation of resources in capital marker and tried to connect with significant issues in modern finance.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Korean Medicine Rehabilitation
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• v.24 no.4
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• pp.49-60
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• 2014

Objectives The purpose of this study is to research current trends of acupuncture treatment of lumbar herniated intervertebral disc using the PubMed database. Methods We set up the search strategy and investigated clinical trials on acupuncture treatment of lumbar herniated intervertebral disc through PubMed search. This study analyzed previous researched papers published from January 1st, 2000 to April 30th, 2014, and classified them by publication year, journal names, types of literature, treatment methods and evaluation scales. To assess the quality of the reviewed literature, randomized controlled trial (RCT) studies were assessed by Cochrane's risk of bias (ROB) tool and non-RCT studies were assessed by risk of bias for non-randomized studies (RoBANS). Results We found 35 studies on the acupuncture treatment of lumbar herniated intervertebral disc. Papers on this topic have been published, on average, three to four times annually in 9 journals since the mid-2000's. The journal with the largest number of publications was Chinese Acupuncture & Moxibustion, and most of articles were classified as RCT. Acupuncture treatment was performed individually or together with other treatments. The most frequently used pain evaluation index was visual analogue scale (VAS). The index of effective rate was used frequently but there was a lack of objectivity. In regards to the quality of the studies, outcome assessment in RCT showed that random sequence generation, allocation concealment, and the blinding of participants and personnel increase potential of risk of bias. For non-RCT assessment, outcome showed that confounding variable, measurement of intervention were at high risk of bias. Conclusions In order to obtain objective clinical evidence of acupuncture treatment of lumbar herniated intervertebral disc, further clinical studies should be designed to minimize the risk of bias, using STRICTA with larger sample sizes.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2323-2326
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• 2015

It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (<50%; $X_{111}$), environmental factors like population density ($148-169pop/km^2$; $X_{73}$), and land use as wetland ($X_{64}$), were correlated with the liver fluke disease distribution at 0.000, 0.034, and 0.006 levels, respectively. Multiple regression analysis, by equations OV= -0.599 + 0.005(population density ($148-169pop/km^2$); $X_{73}$) + 0.040 (human attitude (<50%); $X_{111}$) +0.022 (land used (wetland; X64), was used to predict the distribution of liver fluke. OV is the patients of liver fluke infection, R Square= 0.878, and, Adjust R Square= 0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.