• Korean journal of applied entomology
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• v.47 no.4
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• pp.401-405
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• 2008

This study was conducted to estimate the control thresholds (CTs) of imported cabbage worm, Artogeia rapae L., injuring Chinese cabbage. The second instar larvae of A. rapae were inoculated with five density levels on each Chinese cabbages transplanted three weeks earlier under greenhouse condition, and checked injury rates after allowing their feeding for one week and two weeks, respectively. The average leaf area consumed by single larvae was 657.7 $mm^2$ in plots inoculated at three weeks after transplanting (WAT) and 2495.8 $mm^2$ in plots at 6-WAT, respectively. In the field experiment, different numbers of A. rapae ranged from one to seven larvae were inoculated on 20 plants. The percent yield reduction (Y) of Chinese cabbage infested by different densities of A. rapae (X) for a three-week period was estimated by the following equation; (1) Y=1.764X-0.3049 ($R^2$=0.9901) in plots inoculated at 3-WAT; and (2) Y=1.0305X-0.2976 ($R^2$=0.9398) in plots inoculated at 6-WAT. Based on the relationships between the densities of A. rapae larvae and the yield index of Chinese cabbage, the number of second instar larvae which caused 5% loss of yield (gain threshold proposed by Japan), was estimated as 3.0 per 20 plants for the 3-WAT and 5.1 for the 6-WAT.

• The Korean Journal of Applied Statistics
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• v.28 no.4
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• pp.603-620
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• 2015

The National Pension of Korea is a public social security system designed to alleviate social risks and poverty that has had a major impact on the quality of life for the aging population. However, a rapidly aging population and low fertility threaten the sustainability of national pension in Korea. The National Pension Research Institute publishes a nancial projection every ve years; consequently, the government has lowered the entitlements for the sustainability of national pension based on the projection results. The current reform of the pension system that arbitrarily reduces the entitlements might detract from the income security role of the national pension for pensioners without accounting for the highest elderly poverty rate in the OECD countries. We first discuss methods for the financial projection of the national pension in terms of population, subscribers, and pensioner projections in order to estimate the pension reserve fund and the financial depletion year. We also conduct a sensitivity analysis for population variables, institutional variables, and economic variables based on pension reserves and the financial depletion year. We evaluate intergenerational fairness between the income hierarchy by conducting a money's worth analysis. Finally, we investigate the possibility of the sustainability of national pension by adjusting pension contributions and entitlements (income replacement rate). A new dependency ratio shows that a simple reform of the national pension does not secure the sustainability of the national pension without adapting a pay-as-you-go system.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Korean journal of applied entomology
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• v.53 no.2
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• pp.93-96
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• 2014

This study was conducted to determine the economic injury levels and control thresholds for the striped cabbage flea beetle, Phyllotreta striolata (Coleoptera: Chrysomelidae), on Chinese cabbage at two different planting times. The number of inoculated adults per 10 cabbages was 0, 2, 4, 8, and 16 at the early developmental stage of the cabbage5 days after planting) and 0, 10, 20, 30, and 40 at the middle developmental stage (30 days after planting). Damages to the leaves at the first inoculation were 2.5-21.1% and at the second inoculation were 1.8-26.3% after harvesting. The linear relationships between population density and yield reduction were as follows: Y = 1.3475X + 2.135 ($R^2$ = 0.8699) at the early developmental stage and Y = 0.703X - 1.78 ($R^2$ = 0.966) at the middle developmental stage. On the basis of these results, the economic injury levels caused 5% loss of yield; there were 2.1 adults per 10 Chinese cabbage at the early developmental stage and 9.6 adults per 10 Chinese cabbage at the middle developmental stage.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• The Korean Journal of Financial Management
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• v.8 no.2
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• pp.209-255
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• 1991

This paper develops a simple model to explain the reasons why foreign acquirers pay significantly higher premiums for U.S. target firms than do U.S. buyers. We also provide empirical work on the valuation effect of foreign takeovers and the determinants of the wealth gains of U.S. target shareholders involved in foreign takeovers. The results indicate that target wealth gains are significantly higher in foreign takeovers than in domestic takeovers, after controlling for the wealth effects of payment method, acquisition type, tax status, size and time period of bids. This confirms the valuation effect of foreign takeovers. Furthermore, the results of cross-sectional regression analysis show that the variation in U.S. target wealth gains is explained by extra tax benefits stemming from double tax deductions for acquisition-related interest expenses incurred by foreign acquirers. These findings imply that differential taxation across tax jurisdictions is the main source of the valuation effect of foreign takeovers. In addition, we find that there exists a valuation effect of the nationality of the foreign acquirers. Japanese companies pay significantly higher premiums than do non-Japanese acquirers. The finding also indicates that competition among bidders increases the abnormal returns to U.S. target shareholders in foreign takeovers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.