• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.311-324
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• 2021

Investors have increasingly become interested in macroeconomic antecedents in order to better understand the investment environment and estimate the scope of profitable investment in equity markets. This study endeavors to examine the interdependency between the macroeconomic antecedents (international oil price (COP), Domestic gold price (GP), Rupee-dollar exchange rates (ER), Real interest rates (RIR), consumer price indices (CPI)), and the BSE Sensex and Nifty 50 index return. The data is converted into a natural logarithm for keeping it normal as well as for reducing the problem of heteroscedasticity. Monthly time series data from January 1992 to July 2019 is extracted from the Reserve Bank of India database with the application of financial Econometrics. Breusch-Godfrey serial correlation LM test for removal of autocorrelation, Breusch-Pagan-Godfrey test for removal of heteroscedasticity, Cointegration test and VECM test for testing cointegration between macroeconomic factors and market returns,] are employed to fit regression model. The Indian market returns are stable and positive but show intense volatility. When the series is stationary after the first difference, heteroskedasticity and serial correlation are not present. Different forecast accuracy measures point out macroeconomics can forecast future market returns of the Indian stock market. The step-by-step econometric tests show the long-run affiliation among macroeconomic antecedents.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.1-13
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• 2016

This study attempts to establish a precise forecast model for the container inventory demand of shipping companies through forecasts based on equipment type/size, ports, and weekly system dynamics. The forecast subjects were Shanghai and Yantian Ports. Only dry containers (20, 40) and high cubes (40) were used as the subject container inventory in this study due to their large demand and valid data computation. The simulation period was from 2011 to 2017 and weekly data were used, applying the actual data frequency among shipping companies. The results of the model accuracy test obtained through an application of Mean Absolute Percentage Error (MAPE) verified that the forecast model for dry 40' demand, dry 40' high cube demand, dry 20' supply, dry 40' supply, and dry 40' high cube supply in Shanghai Port provided an accurate prediction, with $0%{\leq}MAPE{\leq}10%$. The forecast model for supply and demand in Shanghai Port was otherwise verified to have relatively high prediction power, with $10%{\leq}MAPE{\leq}20%$. The forecast model for dry 40' high cube demand and dry 20' supply in Yantian Port was accurate, with $0%{\leq}MAPE{\leq}10%$. The forecast model for supply and demand in Yantian Port was generally verified to have relatively high prediction power, with $10%{\leq}MAPE{\leq}20%$. The forecast model in this study also had relatively high accuracy when compared with the actueal data managed in shipping companies.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.93-124
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• 2003

This paper studies the financial analyst's forecasting activities on the firm's operating performance during the period from 1999 to 2003. In this study, financial analyst's forecasting activities are focused on the sales, operating income and net income and financial analyst's forecasting accuracy, forecasting revising patterns and forecasting activities to the unexpected firm's operating performance are studied. Some empirical findings in this study are as follows. First, standard estimate error on the sales, operating income and net income are all significantly negative value and so financial analyst's forecast on the firm's operating performance are upwardly biased. Second, domestic financial analyst's forecasting activities is relatively more accuracy than foreign financial analyst's forecasting activities. Third, forecasting time is more close to the end of the operating performance announcement day, forecasting activities are more accuracy. Fourth, comparing with individual financial analyst's forecast, consensus forecast is more accuracy. Fifth, in the comparative forecasting activities study according to the prior firm's operating performance, financial analyst's forecasting revision activities are found to be upward or downward. Sixth, financial analysts overreact in the sales forecast and underreact in the operating income and net income forecast. Seventh, in the empirical analysis on the Easterwood-Nutt's test model(1999) which the firm's performance change are divided into the expected performance change and the unexpected performance change, it is found that financial analyst's forecasting activities on the firm's operating performance are systematically optimistic.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.84.2-84.2
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• 2015

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model (Lee et al. 2012) which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 1996 to 2014 using this model. Overall frequencies are 61.08% (C), 22.83% (M), and 5.44% (X). The maximum probabilities computed by the model are 99.9% (C), 89.39% (M), and 25.45% (X), respectively. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. For the critical success index widely used, the probability threshold values for contingency tables are 25% (C), 20% (M), and 4% (X). We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.15-0.83(C), 0.11-0.51(M), and 0.04-0.17(X), also depending on a lifetime of satellite. We expect that this study would provide a guideline to determine the probability threshold for space weather forecast.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.718-723
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• 2016

The temperature changes are very important in improving the accuracy of the load forecasting during the summer. It is because the cooling load in summer contribute to the increasing of the load. This paper proposes a weekend load forecasting algorithm using the temperature change characteristic in a summer of Jeju. The days before and after weekends in Jeju, when the load curves are quite different from those of normal weekdays. The temperature change characteristic are obtained by using weekends peak load and high temperature data. And load forecasted based on the sensitivity between unit temperature changes and load variations. Load forecast data with better accuracy are obtained by using the proposed temperature changes than by using the ordinary daily peak load forecasting. The method can be used to reduce the error rate of load forecast.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.91-93
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• 2004

Load forecasting is essential in order to supply electrical energy stably and economically in power systems. ANNs have flexibility to predict a nonlinear feature of load profiles. In this paper, we selected just the necessary input variables used in the paper(2) which is based on the phase-space embedding of a load time-series and reviewing others. So only 5 input variables were selected to forecast for spring, fall and winter season and another input considering temperature sensitivity is added during the summer season. The training cases are also selected from all previous data composed training cases of a 7-day, 14-day and 30-day period. Finally, we selected the training case of a 7-day period because it can be used in STLF without sacrificing the accuracy of the forecast. This allows more compact ANNs, smaller training cases. Consequently, test results show that compact neural networks can be forecasted without sacrificing the accuracy.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.382-395
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• 2013

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the $2{\times}2$ contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group's satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.