• 한국가금학회지
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• 제39권3호
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• pp.233-239
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• 2012

농업 관측에서 제공하고 있는 육계 가격 전망치들은 관련산업에 종사하는 생산자나 기업들에게 매우 중요함에도 불구하고, 예측 자료에 대한 객관적인 평가에 대한 연구가 전무한 실정이다. 본 연구에서는 농업 관측에서 제공하고 있는 육계 가격 전망치의 예측 성과에 대해 정확성에 기초한 검정뿐만 아니라 분류 기반 검정 등 포괄적인 평가를 실시하였다. 분석 결과, 농업 관측 육계 가격의 전망치는 가능한 정보의 활용이나 예측 오류 측면에서 효율적이며, 가격 변화의 방향도 월간에 59%, 연도 간에는 80% 이상 정확하게 예측하는 등 전반적으로 가격 예측을 성공적으로 수행하고 있음을 알 수 있다. 그러나 예측치들이 일관되게 과소 추정되는 경향이 있고, 예측 구간이 32% 이상 실제 가격을 포함하고 있음에도 불구하고, 단순 모형에 대한 상대적인 성과가 통계적으로 유의하지 않은 점 등은 개선되어야 할 필요가 있을 것이다. 이상의 결과를 토대로 볼 때 농업 관측 센터에서 현재 활용하고 있는 예측 기법을 자체적으로 평가해 보고, 다양한 대안적 예측 기법과의 비교 평가를 통해 육계 가격 전망치를 개선할 여지가 있는지를 검토 해보는 것은 의미가 있다고 할 것이다.

• 수산경영론집
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• 제52권4호
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• 전기학회논문지
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• 제57권8호
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• pp.1342-1348
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• 2008

One important objective of the electricity market is to decrease the price by ensuring stability in the market operation. Interconnected to this is another objective; namely, to realize sustainable consumption of electricity by equitably distributing the effects and benefits of participating in the market among all participants of the industry. One method that can help achieve these objectives is the ^{(R)}$demand-response program, - which allows for active adjustment of the loadage from the demand side in response to the price. The demand-response program requires a customer baseline load (CBL), a criterion of calculating the success of decreases in demand. This study was conducted in order to calculate undistorted CBL by analyzing the correlations between such external or seasonal factors as temperature, humidity, and discomfort indices and the amounts of electricity consumed. The method and findings of this study are accordingly explicated.

• The Journal of Asian Finance, Economics and Business
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• 제7권1호
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• pp.29-36
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• 2020

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• 무역학회지
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• 제47권5호
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• pp.21-37
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• 2022

Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.

• 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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 추계 학술논문 발표대회
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• pp.191-192
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• 2017

In response to the economic depression, the demand for fixed rent income has increased according to the easing construction regulations. it caused indiscriminated investment to stakeholders. This leads to oversupply in the multi-family Housing market and increases unsold housing and vacancy rates except specific area such as Gangnam-gu.In order to solve this issue, although studies on the optimization price of apartment houses has been conducted, the study is insufficient regarding on residential officetel. Therefore, the objective is to suggest a basic study on optimal price estimation model development by using probabilistic forecasting method in planning phase. To achieve the objective, first, variables are defined such as expenses, financial costs, income, etc. Second, causal loop diagram is suggested. Third, basic optimization prices estimation model is developed. In the future, this study can be used as one of decision making tools in planning phase of officetel development projects.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.389-391
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• 2002

This paper presents the System Marginal Price(SMp) forecasting implementation using backpropagation Neural Networks in Competitive Electricity Market. SMP is very important term to seek the maximum profit to bidding participants. Demand and SMP that necessary data for training Neural Networks, supplied from Korea Power Exchange(KPX). Statistic analysis about predicted SMP presents a part of consideration in end of this paper.

• 산업경영시스템학회지
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• 제46권1호
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.