• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1263-1270
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• 2012

Livestock disease that decreases the farm productivity and income leads to not only financial loss but also national loss from the spread of contagious disease. The purpose of this paper is to propose a livestock disease forecasting system that can diagnose disease of livestock at an early stage based on the livestock activity and body temperature. The proposed livestock disease forecasting system collect data on livestock activity and body temperature using a acceleration sensor and thermal imaging camera and comparing the data with control according to disease. It is expected that, this system can be accurately identify and prevent spread of livestock disease beforehand to minimize damages caused by disease to improve the productivity and the rate of return of livestock farms.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.31-42
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• 2022

Purpose - The main research goals by macroeconomic analysis is to assess the effectiveness of state regulation, the sustainability of development, and the financial stability of the state. Research design, Data, and methodology - The research were analyzed using the methods of multivariate statistics and application of the software package Stat graphics. The volume of data from the 1995 to the 2021 was analyzed by Russian Federation. The scale of research on Belarus: to be analyzed the amount of data from the 2015 by 2021, on Kazakhstan - from the 19941, on Kyrgyzstan - from the 2002, on Tajikistan - from the 2008, on Armenia - from the 2021, on Japan - since the 1970, on China - since the 1950, on South Korea - since the 1953. Result - The methods of multivariate statistics was demonstrated exact of result in forecasting of macroeconomic indicators. The most of tendency with the accurate results of are described using the second-degree polynomials. In the most research of country there are the macroeconomic proportion are broken. Conclusion - In the countries studied, the monetary aggregates have a significant growth rate. The shares with a substantial monetary stock and the speed of its growth are divided in the two groups: having placements in the real sectors of the economy and not having received the same result of development from the growth of the monetary stock.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.93-104
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• 2022

In forecasting realized volatility of the major US stock price indexes (S&P 500, Russell 2000, DJIA, Nasdaq 100), internet search volume reflecting investor's interests and implied volatility are used to improve forecast via a deep learning method of the LSTM. The LSTM method combined with search volume index produces better forecasts than existing standard methods of the vector autoregressive (VAR) and the vector error correction (VEC) models. It also beats the recently proposed vector error correction heterogeneous autoregressive (VECHAR) model which takes advantage of the cointegration relation between realized volatility and implied volatility.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.741-744
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• 1996

Artifical Neural Network(ANN) models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct ANN models which can maximize the performance of prediction, regarding Corporate Bond Yield (CBY) as interest rate. Synergistic Market Analysis (SMA) was applied to the construction of models [Freedman et al.]. In this aspect, while the models which consist of only time series data for corporate bond yield were devloped, the other models generated through conjunction and reorganization of fundamental variables and market variables were developed. Every model was constructed to predict 1,6, and 12 months after and we obtained 9 ANN models for interest rate forecasting. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 and 6 months after.

• KDI Journal of Economic Policy
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• v.42 no.4
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• pp.117-138
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• 2020

Few studies have specifically focused on the uncertainty of demand forecasting despite the fact that uncertainty is the one of greatest risks for governments and private partners in PPP projects. This study presents a methodology for finding robust contract conditions considering uncertainty in travel demand forecasting in a PPP project. Through a case study of an urban railway PPP project in Korea, this study uncovered the risk of excessive government payments to private partners due to the uncertainty in contracted forecast ridership levels. The results allow the suggestion that robust contract conditions could reduce the expected total level of government payments and lower user fees while maintaining profitability of the project. This study offers a framework that assists contract negotiators and gives them more information regarding financial risks and vulnerabilities and helps them to quantify the likelihood of these vulnerabilities coming into play during PPP projects.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.589-594
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• 2011

Selecting an appropriate railway system in urban railway project is an important step for an efficient public transport policy. This paper attempts to solve the railway system selection problems in the (pre)feasibility study or preliminary research of urban railway project, by the rough transportation demand forecasting and financial analysis. There are two stages in this paper: in stage one, we review the worthwhile and various criteria which presented in precedent studies; whereas in stage two, an structured selection criteria is proposed for determining the appropriate railway system in urban railway project. The utilization of the proposed criteria is demonstrated with the case of a newtown in the metropolitan area. The results show that proposed criteria can be used to make the rational decision for governmental financial condition and social benefit.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1015-1022
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• 2009

Currently, Korean real estate market has experienced cooling down of the business because of the global economic crisis which resulted from the subprime mortgage lending practice. In response, the Korean government has enforced various policies at the base of deregulating real estate speculation, such as increasing Loan to value ratio (LTV) in order to stimulate housing demand and supply. However, these policies seemed to result in deep confusion in the Korean housing market. Furthermore, analysis for housing market forecasting, especially international financial crisis on Korean real estate market, has been partial and fragmentary, therefore comprehensive solution and systematical approach is required to analyze the real estate and real estate financial market including causal nexus between market determining factors. In an integrated point of view, applying the system dynamics modeling, the paper aims at proposing Korean Real Estate and Mortgage market dynamics models based on fundamental principles of housing market determined by supply and demand. We also find the impact of deregulation policies focusing on mortgage loan which is the main factors of policies.