• Management Science and Financial Engineering
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• 제19권1호
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• pp.1-10
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• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• 융합경영연구
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• 제9권1호
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• pp.25-40
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• 2021

Purpose: The forecasting is the likelihood scientifically proved judgment about the prospects, the possible conditions of this or that phenomenon in the future and (or) about the alternative ways and the means of their realization. To adapt the instruments of budgeting for the analysis cash flow of company. Research design, data and methodology: The creates the budget of cash flow were carried out on the basis of data of the report for the 2017 of corporations POSCO and in the first half of the 2018 Daewoo Shipbuilding & Marine Engineering of South Korea. Results: The simultaneous use of budgeting techniques and the simple financial analysis allows to systematize the transactions, to identify the main problem areas in the movement cash flows. Therefore, working capital analysis is to determine the limits of their fluctuations in view of the changes in the business processes. Conclusions: In the pedagogical context solved the features of budgeting in the part evaluation current assets, its financing, its elements: the cash, the debtor. In the process of budgeting of cash flow, in credit budget, in financial budget we can see the main indicators: the current assets, the functioning capital, the optimum number of debtors, the optimum amount of cash and another.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1305-1325
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• 2008

As index option markets grow recently, many analysts and investors become interested in forecasting the volatility of KOSPI 200 Index to achieve portfolio's goal from the point of financial risk management and asset evaluation. To serve this purpose, we introduce NN and SVM integrated with other financial series models such as GARCH, EGARCH, and EWMA. Moreover, according to the empirical test, Integrating NN with GARCH or EWMA models improves prediction power in terms of the precision and the direction of the volatility of KOSPI 200 index. However, integrating SVM with financial series models doesn't improve greatly the prediction power. In summary, SVM-EGARCH was the best in terms of predicting the direction of the volatility and NN-GARCH was the best in terms of the prediction precision. We conclude with advantages of the integration process and the need for integrating models to enhance the prediction power.

• 보건행정학회지
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• 제10권2호
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• pp.1-21
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• 2000

This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

• 경영과정보연구
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• 제3권
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• pp.179-199
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. In case-based approach the problem solving experience of the domain expert is encoded in the form of cases. CBR has successfully been applied to many kinds of problems such as design, planning, diagnosis and forecasting. In this paper, CBR was applied for forecasting default risk. The applied result was successful in spite of the small casebase. Generally, CBR requires large casebase. So, if the number of data was large, the result was better. But in this paper, what financial variable was more forecastable was not tested. Next, this should be tested.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제19권1호
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• pp.7-21
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• 2012

The fuzzy time series is introduced by Song and Chissom([8]) to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is discussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully reflects the fluctuation of historical data.

• 응용통계연구
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• 제28권3호
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• pp.429-442
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• 2015

변동성(volatility)은 투자위험을 의미하며 자산의 가격결정이나 포트폴리오 관리 및 투자전략에서 아주 중요한 역할을 한다. 이러한 변동성을 모형화하기 위한 조건부 이분산 모형으로서 전통적인 GARCH(generalized autoregressive conditional heteroskedastic) 모형 및 확장된 형태들이 널리 사용되어지고 있으나, 금융위기와 재정위기와 같은 구조적 변화를 변동성 예측에 반영할 수 없다는 단점을 가지고 있다. 본 논문에서는 이를 극복하기 위한 모형으로서 국면전환 GARCH(Markov regime switching GARCH) 모형을 소개하고, 한국의 일별 KOSPI 수익률에 적용하여 변동성 분석 및 예측을 실시하고, 기존의 GARCH 모형들과 비교하여 그 성능을 평가한다. 그 결과 표본 내(in-sample)의 변동성 적합도 측면에서 국면전환 GARCH 모형이 가장 우수한 성능을 보였으며, 표본 외(out-of-sample) 예측력 측면에서는 국면전환 GARCH 모형이 단기적 예측에서 좋지 않은 성능을 보였으나 장기적 예측에서 우수함을 보였다.

• 응용통계연구
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• 제36권2호
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• pp.129-139
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• 2023

변동성의 예측은 자산의 리스크에 대비하는 데에 중요한 역할을 하기때문에 필수적이다. 인공지능을 통하여 이러한 복잡한 특성을 지닌 변동성 예측을 시도하였는데 기존 시계열 예측에 적합하다 알려진 LSTM (1997)과 GRU (2014)은 기울기 소실로 인한 문제, 방대한 연산량의 문제, 그로 인한 메모리양의 문제 등이 존재하였다. 변동성 데이터는 비정상성(non-stationarity)과 정상성(stationarity)을 모두 가지고 있는 특성이 있으며, 자산 가격 하방 쇼크에 더 큰 폭으로 상승하는 비대칭성과 상당한 장기 기억성, 시장에 큰 사건이 발생할 때 기존의 값들에 비해 이상치라 할 수 있을 정도의 예측할 수 없는 큰 값이 발생하는 특성들이 존재한다. 이렇게 여러 가지 복잡한 특성들은 하나의 모형으로 구조화되기 어려워서 전통적인 방식의 모형으로는 변동성에 대한 예측력을 높이기 어려운 면이 있다. 이러한 문제를 해결하기 위해 1D CNN의 발전된 형태인 causal TCN (causal temporal convolutional network) 모형을 변동성 예측에 적용하고, 예측력을 최대화 할 수 있는 TCN 구조를 설계하고자 하였다. S&P 500, DJIA, Nasdaq 지수에 해당하는 변동성 지수 VIX, VXD, and VXN, 에 대하여 예측력 비교를 하였으며, TCN 모형이 RNN 계열의 모형보다도 전반적으로 예측력이 높음을 확인하였다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권3호
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• pp.167-172
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• 2015

기계 학습은 컴퓨터를 학습시켜 분류나 예측에 사용되는 기술이다. 그 중 SVM은 빠르고 신뢰할 만한 기계 학습 방법으로 분류나 예측에 널리 사용되고 있다. 본 논문에서는 재무 정보를 기반으로 SVM을 이용하여 주식 가격의 예측력을 검증한다. 이를 통해 회사의 내재 가치를 나타내는 재무정보가 주식 가격 예측에 얼마나 효과적인지를 평가할 수 있다. 회사 재무 정보를 SVM의 입력으로 하여 주가의 상승이나 하락 여부를 예측한다. 다른 기법과의 비교를 위해 전문가 점수와 기계 학습방법인 인공신경망, 결정트리, 적응형부스팅을 통한 예측 결과와 비교하였다. 비교 결과 SVM의 성능이 실행 시간이나 예측력면에서 모두 우수하였다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.