• International Journal of Contents
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• v.11 no.1
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• pp.31-40
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• 2015

This paper primarily intends to explore whether smartphones accelerate the customer education paradox. Smartphone usage is becoming a mainstream habit, and it is changing people's shopping experience and conventional practices, hence presenting new challenges to the market. A smartphone affects customers strongly when they are trying to choose a product/service among a variety of options, and making purchase decisions. With smartphones bringing such changes and challenges to the market, especially to the companies and stores, it is important to understand market trends in order to retain the loyalty of existing customers as well as to attract new buyers. Therefore, companies and stores should offer enhanced and better technical service quality, along with the use of tools such as QR codes. Further, mobile based websites would offer a suitable approach in assisting customers using smartphones to obtain better information of greater value. The results of this study imply that there is an opportunity for organizations to design various methods of imparting customer education by using smartphones, such as loading applications on a smartphone that lead to more information with good quality and present real benefits regarding the products/services.

• New & Renewable Energy
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• v.8 no.2
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• pp.33-43
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• 2012

Why solar companies preferred vertical integration of whole value chain? Major solar companies have built internally strong vertical integration of entire PV value chain. We raise a question whether such integration increases the corporate value and whether market situation affects the result. To test these questions, we conducted multi-variant analysis where characteristic factors mainly affect the corporate value measured in terms of Tobin'Q, based on the financial and non-financial data of PV companies listed in US stock market between 2005 and 2010. We hypothesize that since integration increases the overall efficiency but decreases the flexibility to adjust to various market situation, the combined effect of the efficiency gain and the flexibility loss ultimately determines the sign of integration effect on the corporate vale. We infer that the combined effect will be influenced heavily by business cycle, as in boom market (Seller's market) the efficiency gain may be larger than the flexibility loss and vice versa in bust market. We test whether the sign of combined effect changes after the year of 2009 and which factors influence most the sign. Year of 2009 is known as the year when market shifted from Seller's to Buyer's market. We show that 1) integration increases corporate value in general but after 2009 integration significantly decreases the value, 2) the ratios such as Production/Total Cost, Cash turnover period chosen for reversal of the flexibility measure are negatively affect Tobin's Q and especially stronger after 2009. This shows the flexibility improves corporate value and stronger in the recess period (Buyer's market). These results imply that solar company should set up integration strategy considering the tradeoff between efficiency and flexibility and the impact of the business cycle on both factors. Strategy only based on the price competitiveness determined in boom time can bring undesirable outcomes to the company. In addition, Strategic alliances in some value chains as a flexible bondage should be taken in account as complementary choice to the rigid integration.

• Korean Journal of Veterinary Service
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• v.42 no.2
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• pp.67-71
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• 2019

A variety of livestock feed resources were used in Korean dairy farm due to a lack of the endemic feed. However, there is inadequate real farm data to support farmers' decisions on the choice of options. The main objective of this study was to evaluate the nutritional value of total mixed ration (TMR) as well as the metabolic diseases status in Korean dairy farms. TMR samples were collected from nine feed companies and eight selected self-formulated by the dairy farms. The nutrient contents were examined by AOAC methods. The frequency of metabolic diseases such as ketosis and hypocalcemia were surveyed. The average moisture content was 36.2% although the min. and max. value were varied from 21.7% and 50.6% among farms. The mean${\pm}$standard deviation of crude fiber (CF), crude ash (CA), ether extract (EE), and crude protein (CP) were $21.4{\pm}2.5$, $4.6{\pm}0.4$, $3.2{\pm}0.5$ and $9.8{\pm}1.7$, respectively. However, the average ADF and NDF was $17.3{\pm}3.7$ and $31.0{\pm}5.7$, respectively. The compositions of TMR were varied significantly among the dairy farms. The frequency of clinical Ketosis (CK), subclinical ketosis (SCK) and hypocalcemia were higher in early lactation period with 4.5%, 11.0% and 3.0%, respectively. Also, the frequency of SCK was higher than CK and hypocalcemia throughout the lactation. Periodic TMR nutrient analysis based on herd production or physiology change would maximize the effects of TMR feeding. Furthermore, the study results would be useful to the farm practitioner and producer for their farm management.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.645-668
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• 2022

Hydrogen energy is emerging as an important means of carbon neutrality in the various sectors including power, transportation, storage, and industrial processes. Fuel cell power plants are the fastest spreading in the hydrogen ecosystem and are one of the key power sources among means of implementing carbon neutrality in 2050. However, high volatility in system marginal price (SMP) and renewable energy certificate (REC) prices, which affect the profits of fuel cell power plants, delay the investment timing and deployment. This study applied the real option methodology to analyze how the dual uncertainties in both SMP and REC prices affect the investment trigger price level in the irreversible investment decision of fuel cell power plants. The analysis is summarized into the following three. First, under the current Renewable Portfolio Standard (RPS), dual price uncertainties passed on to plant owners has significantly increased the investment trigger price relative to one under the deterministic price case. Second, reducing the volatility of REC price by half of the current level caused a significant drop in investment trigger prices and its investment trigger price is similar to one caused by offering one additional REC multiplier. Third, investment trigger price based on gray hydrogen and green hydrogen were analyzed along with the existing byproduct hydrogen-based fuel cells, and in the case of gray hydrogen, economic feasibility were narrowed significantly with green hydrogen when carbon costs were applied. The results of this study suggest that the current RPS system works as an obstacle to the deployment of fuel cell power plants, and policy that provides more stable revenue to plants is needed to build a more cost-effective and stable hydrogen ecosystem.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.3
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• pp.303-321
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• 2011

The availability and efficient use of the feed resources in Asia are the primary drivers of performance to maximise productivity from animals. Feed security is fundamental to the management, extent of use, conservation and intensification for productivity enhancement. The awesome reality is that current supplies of animal proteins are inadequate to meet human requirements in the face of rapidly depleting resources: arable land, water, fossil fuels, nitrogenous and other fertilisers, and decreased supplies of cereal grains. The contribution of the ruminant sector lags well behind that of non-ruminant pigs and poultry. It is compelling therefore to shift priority for the development of ruminants (buffaloes, cattle, goats and sheep) in key agro-ecological zones (AEZs), making intensive use of the available biomass from the forage resources, crop residues, agro-industrial by-products (AIBP) and other non-conventional feed resources (NCFR). Definitions are given of successful and failed projects on feed resource use. These were used to analyse 12 case studies, which indicated the value of strong participatory efforts with farmers, empowerment, and the benefits from animals of productivity-enhancing technologies and integrated natural resource management (NRM). However, wider replication and scaling up were inadequate in project formulation, including systems methodologies that promoted technology adoption. There was overwhelming emphasis on component technology applications that were duplicated across countries, often wasteful, the results and relevance of which were not clear. Technology delivery via the traditional model of research-extension linkage was also inadequate, and needs to be expanded to participatory research-extension-farmer linkages to accelerate diffusion of technologies, wider adoption and impacts. Other major limitations concerned with feed resource use are failure to view this issue from a farming systems perspective, strong disciplinary bias, and poor links to real farm situations. It is suggested that improved efficiency in feed resource use and increased productivity from animals in the future needs to be cognisant of nine strategies. These include priorities for feed resource use; promoting intensive use of crop residues; intensification of integrated ruminant-oil palm systems and use of oil palm by-products; priority for urgent, wider technology application, adoption and scaling up; rigorous application of systems methodologies; development of adaptation and mitigation options for the effects of climate change on feed resources; strengthening research-extension-farmer linkages; development of year round feeding systems; and striving for sustainability of integrated farming systems. These strategies together form the challenges for the future.

• Journal of Distribution Science
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• v.13 no.3
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• pp.69-77
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• 2015

Purpose - The purpose of this study is to examine whether the cash policies of retail firms listed on Korean stock markets are consistent with the evidence provided in the study of Almeida et al. (2004). Liquidity management is an important issue for financially constrained firms relative to financially unconstrained firms. Because there are few sources of external funding, the optimal liquidity policies of financially constrained firms should reflect their own earnings or cash inflows to create opportunities for current and future real investments. According to this simple idea, we estimate the sensitivity of cash to cash flows and simply check whether the estimated sensitivity to cash flows of the cash retained by constrained retail firms is greater than that of the cash retained by unconstrained retail firms. Through this work, we aim to explain why the cash policies of the retail firms listed on the Korean stock markets differ from those of listed manufacturing enterprises. Research design, data, and methodology - To explain a firm's cash holdings, we use only three explanatory variables: earnings before interest and taxes (EBIT), Tobin's q, and size. All the variables are defined as the value of the numerator divided by aggregate assets. Thanks to this definition, it is possible to treat all the sample firms as a single large firm. The sample financial data for this study are collected from the retail enterprises listed on the KOSPI and KOSDAQ markets from 1991 to 2013. We can obtain these data from WISEfn, the financial information company. This study's methodology has its origin in Keynes's simple idea of precautionary liquidity demand: When a firm faces financial constraints, cash savings from earnings or cash inflows become important from the corporate finance perspective. Following this simple idea, Almeida et al. (2004) developed their theoretical model and found empirical evidence that the sensitivity of cash to cash flows varies systematically according to different types of financing frictions. To find more empirical evidence for this idea, we examined the cash flow sensitivity of the cash held by Korean retail firms. Results - Through several robustness tests, we empirically showed that financially constrained Korean retail firms display significant positive propensity to save cash from earnings before interest and taxes, while the estimated cash flow sensitivity of the cash held by unconstrained retail firms is not significant. Despite the relatively low earnings of retail firms, their sensitivity is three times greater than that of manufacturing enterprises. This implies that Korean retail firms have greater intentions of facilitating future investments rather than current investments. Conclusions - The characteristics of the cash policies of Korean retail firms differ from those of manufacturing firms. This contrast may be attributable to industry-oriented policy planning, regulations, and institutional differences. However, the industrial policymakers should observe signals of the long-term growth options of retail firms based on their high propensity to save from their cash inflows.