• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.2
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• pp.112-120
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• 2007

This paper reviews the use of parasites as 'biological tags' for studying stock analysis of salmonid fishes. Numerous definitions of stock concepts exist, but most of them essentially define a group of fish as having similar biological characteristics and being self-reproducing as stocks. It is important to manage fish stocks for human consumption and sustainable production and especially for salmonid fishes. Because these fry are considered as each country's property, it is necessary to identify and discriminate each fish stock in the open sea. Methods of separating fish stocks are very diverse. Artificial tags, parasites, otoliths scales and genetic characters have been used for stock analysis and each method has advantages and disadvantages. Of these parasites can be good biological tags because they are applied by nature at no cost. Parasites can be infected with susceptible host fishes when they enter into certain areas. Then if they move to the outside and are caught researchers can infer that the fish had been in the endemic area for a period of time during their life. Hence the host fish can be considered as naturally 'tagged' by parasites. However, if they do not pass the parasites-endemic. area, they will harbour no parasites. Therefore, researchers can discriminate each fish stocks and trace their migration routes with these biological tags. In this paper, several examples on the use of parasites as biological tags for studying salmonids, as well as other species, are listed. The advantages and limitations of parasites as biological tags are also discussed. Chum salmon (Oncorhynchus keta), the main salmonid species migrating to Korea, is distributed all around the North Pacific. Korean chum salmon are generally thought to move to the Sea of Okhotsk, the western North Pacific and the Bering Sea. However, there is no clear information on the distribution and migration pathways of Korean chum salmon, and no markers exist for separating them from others yet. Recent Korean chum salmon stock analysis including parasites information are mentioned.

• 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 Korean Society of Forest Science
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• v.34 no.1
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• pp.31-46
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• 1977

The study was made to give some helpful information for policy-making on ornamental tree cultivation by doing a survey on general situations, management analysis, and future prospects of the ornamental tree growing. The study was carried out through literature studies related to the subject, questionaire surveys, and on-the-spot investigation. The questionaire surveys could be divided into two parts: pre-questionaire survey and main-questionaire survey. In the pre-questionaire survey, the researchers intended to identify the total number of ornamental tree growers, cultivation areas in size and their locations. The questionaires were sent to each town and county administration authorities, forest cooperatives, and related organizations through-out the nation. The main-questionaires were prepared for detailed study and the questionaires were sent to 200 tree growers selected by option by taking considerations of the number of tree growers and the size of cultivating areas in regions. The main findings and some information obtained in the survey were as follows: 1. The total land for ornamental tree growing was amounted to 1,873.02 hectares and the number of cultivators was totaled to 2,717. 2. The main occupations of the ornamental tree growers were found in horticulture (41.9%), agronomy (25.9%), officialdom (11.3%), animal husbandry (6.5%), business circle(4.8%), and forestry (3.2%) in sequence. 3. The ornamental trees were cultivated mostly upperland (54.8), forest land (19.4%), rice paddy (11.3%) and others. 4. The educational training of the tree growers seemed quite high. The results of the survey indicated that a large number of tree growers was occupied by college graduates (38.7%), and then high school graduates (34.7%), middle school graduates (12.9%) in order. 5. The tree farming was undertaken as a side-job (41.9%) rather than main-job (23.4%), but a few of respondents rated as subsidiary-job (18.6%). 6. The management status classified by the rate of hired labors used was likely to belong to three categories: independant enterprise management (41.9%); half independant management (31.5%); and self-management (32.4%). 7. The majority of the tree growers sold their products to the consumers through middle-man channel (48.4%), or directly to the house-holder and detailers (13.7%), but a few of the respondents answered that they disposed of their products by bidding (11.2%) or by direct selling to the contractors (4.8%). 8. The channel cf marketing seemed somewhat complicated. The results of the survey were as: (1) producers ${\rightarrow}$consumers (22.6%) (2) producers ${\rightarrow}$field middle-men${\rightarrow}$consumers (33.1%) (3) producers ${\rightarrow}$field middle-men${\rightarrow}$first stage brokers${\rightarrow}$consumers (15.3%) (4) producers ${\rightarrow}$field middle-men${\rightarrow}$second stage middle-men${\rightarrow}$brokers${\rightarrow}$consumers (5.7%) (5) producers${\rightarrow}$field middle-men${\rightarrow}$third stage middle-men${\rightarrow}$second stage middlemen${\rightarrow}$brokers${\rightarrow}$consumers (4.8%) 9. It was responded that the margin for each stage of middle-men or brokers was assumed to be 30-50%(33.1%), 20-30%(32.3%), 50-100%(9.7%), and 100-200%(2.4%) in sequence. 10. The difference between the delivery price of consumers and field selling price of the producers seemed quite large. Majority of producers responded that they received half a price compared to the consumer's prices. 11. About two thirds of the respondents opposed to the measure of "Law on Preservation and Utilization of Agricultural Land" in which says that all the ornamental trees grown on flat agricultural lands less than 8 degrees in slope must be transplanted within three years to other places more than 8 degrees in slope. 12. The tree growers said that they have paid rather high land taxes than they ought to pay (38.7%), but come responded that land tax seemed to be appropriate (15.3%), and half of the respondents answered "not known". 13. The measures for the standardization of ornamental trees by size were backed up by a large number of respondents (57.3%), but one third of the respondents showed negative answer (29.8%). 14. About half of the respondents favored the systematic marketing through organization such as forest cooperatives (54%), but quite a few respondents opposed to organizing the systematic marketing channel (36.3%). 15. The necessary measures for permission in ornamental tree cultivation was rejected by a large number of respondents (49.2%) than those of favored (43.6%).