• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.161-186
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• 2012

Recently, the flash memory consistently increases the storage capacity while the price of the memory is being cheap. This makes the mass storage SSD(Solid State Drive) popular. The flash memory, however, has a lot of defects. In order that these defects should be complimented, it is needed to use the FTL(Flash Translation Layer) as a special layer. To operate restrictions of the hardware efficiently, the FTL that is essential to work plays a role of transferring from the logical sector number of file systems to the physical sector number of the flash memory. Especially, the poor performance is attributed to Erase-Before-Write among the flash memory's restrictions, and even if there are lots of studies based on the log block, a few problems still exists in order for the mass storage flash memory to be operated. If the FAST based on Log Block-Based Flash often is generated in the wide locality causing the random writing, the merge operation will be occur as the sectors is not used in the data block. In other words, the block thrashing which is not effective occurs and then, the flash memory's performance get worse. If the log-block makes the overwriting caused, the log-block is executed like a cache and this technique contributes to developing the flash memory performance improvement. This study for the improvement of the random writing demonstrates that the log block is operated like not only the cache but also the entire flash memory so that the merge operation and the erase operation are diminished as there are a distinct mapping table called as the offset mapping table for the operation. The new FTL is to be defined as the XAST(extensively-Associative Sector Translation). The XAST manages the offset mapping table with efficiency based on the spatial locality and temporal locality.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• KDI Journal of Economic Policy
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• v.32 no.4
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• pp.1-34
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• 2010

We conduct a comprehensive risk analysis of household debt in Korea for the first time using the whole sample credit bureau (CB) data of 2.2 million individual debtors. After analysing debt service capacity profiles of debtor groups classified by the borrower characteristics such as income, age, occupation, credit scoring, and the type of creditor business companies, we investigate the impact of interest rate and income changes on debt service-to-income ratios (DTIs) and default rates of respective debtor groups. Empirical results indicate that debt service burdens are relatively high for low income wage earners, high income self-employed, low income capital and card loan holders, and high income mutual savings loan holders. We also find that debtors from multiple financial companies are particularly weak in their debt service capacity. The scenario analysis indicates that financial companies, with the current level of capital buffers, may be able to absorb negative consequences arising from the increase in DTIs and loan default rates if the interest rate and income changes remain modest. However, the negative consequences may fall disproportionately on non-bank financial companies such as capital, credit card, and mutual savings banks, whose debtors' DTIs are already high. We also find that the refinancing risk of household debt is relatively high in Korea as more than half of household mortgage debts are bullet loans. As the DTIs of mortgage loan holders are already high, under the current DTI regulation, mortgage loans may not be readily refinanced especially when the interest rate rises. Disruptions in mortgage loan refinancing may put downward pressure on housing prices, which may in turn magnify refinancing risk under the current loan-to-value (LTV) regulation. Overall our analysis suggests that, for more effective monitoring of household debt risk, it is necessary to combine existing surveillance schemes based on macro aggregate indicators with more comprehensive and detailed risk analyses based on micro individual data.

• Environmental and Resource Economics Review
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• v.24 no.3
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• pp.573-605
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• 2015

This study proposed scenarios of tax reform regarding taxation on bituminous coal for power generation since July 2015 and July 2014, estimated its impact on SMP, settlement price, tax revenue from year 2015 to year 2029. These scenarios are compared with those of the standard scenario. To estimate them, the power system simulation was performed based on the government plan, such as demand supply program and the customized model to fit Korea's power system and operation. Imposing a tax on bituminous coal for power generation while maintaining tax neutrality reducing tax rate on LNG, the short-term SMP is lowered than the one of the standard scenario. Because the cost of nuclear power generation is still smaller than costs of other power generation, and the nuclear power generation rarely determines SMPs, the taxation impact on SMP is almost nonexistent. Thus it is difficult to slow down the electrification of energy consumption due to taxation of power plant bituminous coal in the short term, if SMP and settlement price is closely related. However, in the mid or long term, if the capacity of coal power plant is to be big enough, the taxation of power plant bituminous coal will increase SMP. Therefore, if the tax reform is made to impose on power plant bituminous coal in the short term, and if the tax rate on LNG is to be revised after implementing big enough new power plants using bituminous coal, the energy demand would be reduced by increasing electric charges through energy tax reform. Both imposing a tax on power plant bituminous coal and reducing tax rate on LNG increase settlement price, higher than the one of the standard scenario. In the mid or long term, the utilization of LNG complex power plants would be lower due to an expansion of generating plants, and thus, the tax rate on LNG would not affect on settlement price. Unlike to the impact on SMP, the taxation on nuclear power plants has increased settlement price due to the impact of settlement adjustment factor. The net impact of energy taxation will depend upon the level of offset between settlement price decrease by the expansion of energy supply and settlement price increase by imposing a tax on energy. Among taxable items, the tax on nuclear power plants will increase the most of additional tax revenue. Considering tax revenues in accordance with energy tax scenarios, the higher the tax rate on bituminous coal and nuclear power, the bigger the tax revenues.

• Journal of Distribution Research
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• v.15 no.5
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• pp.237-255
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• 2010

The Korean Pharmacist Law has limited the sales of medicine to pharmacies. This has caused difficulty in purchasing medicine late at night or on holidays, which has limited the range of customers' selections and accelerated customers' discomfort, accordingly. Also, the rapid progress of aging has quickly boosted medical expenses for seniors, and has served as a factor that aggravates the budget of national medical insurance. Meanwhile, advanced countries, including the USA and Japan, have allowed the sales of general medicine, of which the safety and efficacy have been tested, in general retail stores such as convenience stores or super markets from the perspective of supporting self-medication. In particular, Japan, which has a strong tendency of pursuing safety in the world, diversified sales channels for general medicine in order to control quickly rising medical expenses. As a result, Japan has achieved the effect of easing various regulations as follows in the economic and social fields. First, the increasing distribution channels of general medicine from pharmacies to general retail stores provoked a potential demand, which also expanded related markets. Second, the competition between sales channels resulted in the reduction of the price of medicine. Third, the growing sales channels of medicine have extended the options of consumers and, subsequently, the convenience in the use of consumers has increased. Fourth, the creation of a competitive environment owing to the diversification of sales channels has accelerated an effort to enhance corporate competitiveness. Fifth, the foundation of enhancing the financial soundness of medical expenses has been prepared through the formation of a self-medication environment. In 2000, the Korean population aged 65 or over exceeded 7%, and it is anticipated to be over 14% by 2018; thus, the increase of national medical expenses will be sped up. As a way of being prepared for the era of aging, we, just as other advanced countries, need to create a self-treatment environment by diversifying the sellers of general medicine, and, thus, reduce spending on personal medical expenses, enhance the financial soundness of national medical insurance, and, further, promote the welfare of consumers.

• Journal of Mushroom
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• v.17 no.4
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• pp.224-229
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• 2019

The two most common mushroom species grown in Korea are pearl oyster mushroom (Pleurotus ostreatus) and king oyster mushroom (P. eryngii). In recent years, the production of king oyster mushroom greatly increased due to the automation of the cultivation facilities, and it became a major export mushroom owing to its excellent shelf life. However, the increase in the production of king oyster mushroom led to a decline in its market price; thus, necessitating the development of new mushroom species that could replace king oyster mushroom, to diversify the mushroom market for the benefit of both, the producers and the consumers. The Mushroom division at the National Institute of Horticultural & Herbal Science (NIHHS) reported the development of a new interspecific hybrid between P. ferulae and P. tuoliensis, referred to as 'Creamy.' Two parental strains KMCC00430 (Bisan2ho, P. ferulae) and KMCC00461 (P. tuoliensis) were selected based on the results of genetic resource analysis, and their monokaryons were collected. About 1,000 Mon-Mon crosses were performed and 73 of them were selected. Following repeated cultivation tests and strain analyses, we selected strain 7773, which had a bright creamy pileus and a thick straight stipe, and named it 'Creamy.' Optimum temperature for mycelial growth of Creamy was 25-30℃, and that for fruiting body growth was 16℃. The pileus, which had a brighter creamy color, was small in size with a diameter of 61.2 mm. Although it was cultivated in suboptimal conditions, such as low temperature and high CO₂ concentration, Creamy was characterized by its straight and smooth stipe. Field production tests and further analyses indicated that the yield of Creamy was 5% higher than that of Baekhwang. It is expected that Creamy, the new interspecific hybrid with a bright creamy pileus and a pleasant flavor, will help create new opportunities for mushroom farmers and diversify the mushroom market.

• Korean Journal of Food Science and Technology
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• v.42 no.1
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• pp.69-74
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• 2010

Black garlic has recently received significant attention due to its various health functional properties, and there has been an increase in demand for its use as a functional food. This study was performed to determine the optimum concentration for the fermentation of black garlic yakju. In addition, the antioxidant activity of the fermented black garlic yakju was examined. The alcohol content in the black garlic yakju significantly increased for 6 days and the pH gradually increased as the concentration of black garlic increased. The reducing sugar content at each black garlic concentration was maximal when it was fermented for 24 hours, and then rapidly decreased at longer fermentation periods. The main organic acids were lactic, citric, malic and oxalic acid. Also, the lactic acid content increased as the concentration of the black garlic increased where as the content of other organic acids decreased. The total polyphenol content, ferric ion reducing antioxidant power (FRAP) activity and DPPH (1,1-diphenyl-2-picryl-hydrazyl) free radical scavenging activity of black garlic yakju increased as the concentration of black garlic increased. The sensory characteristics of fermented black garlic yakju were evaluated in terms of color, flavor, taste and overall acceptability, and the highest overall acceptability value was obtained for yakju containing a black garlic concentration of 1-3%. Therefore, the optimum concentration of black garlic was determined to be 1% for the production of high quality black garlic yakju.

• Journal of the Korean Geographical Society
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• v.31 no.4
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• pp.662-671
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• 1996

Since the world's economic and political structures have changed, the term 'globlization' has shown up as a dominant power and as a necessity for regional and national development. Each nation is responding to the globalization process economically and politically in various ways. In general, however, the economic response to the globalization is economic restructuring from the Fordist industries to 'flexible specialization'. And the political response to the globalization is 'global localization' as a new type of local politics(i.e., local policy activism or growth-enhancing local development policies). The crisis of Fordism shifted the role of local governments towards more involovement with local economic development. Local governments are mobilizing for loca economic development, they are taken into a process of institutional change that tends to redefine their responsibilities inside the state. Local governments thus tend to act as an entrepreneur in order to restructure theiir local economies and to compete with other national and international regions. State restructuring towards enerepreneurialism and efficient regional policy pursuing a pro-growth coalition trategy is chosen as a new mode of regulation for the post-Fordism at the local level. The flexible specialization as the post-Fordist economy and the local government as an entrepreneur are the global choice for globalization and a post-Fordist society. The paper focuses on the regulation theory which comprises the political economic perspective on resturcturing. Economic restructuring and state restructuring will be discussed in detail. And the paper tries to combine the economic globalization and the global localization as economic and political responses to globalization.