• The Journal of Industrial Distribution & Business
• /
• v.9 no.10
• /
• pp.83-89
• /
• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.237-245
• /
• 2017

Our research is aimed at predicting recent trend and leading technology for the future and providing optimal Nano technology trend information by analyzing Nano technology trend. Under recent global market situation, Users' needs and the technology to meet these needs are changing in real time. At this point, Nano technology also needs measures to reduce cost and enhance efficiency in order not to fall behind the times. Therefore, research like trend analysis which uses search data to satisfy both aspects is required. This research consists of four steps. We collect data and select keywords in step 1, detect trends based on frequency and create visualization in step 2, and perform analysis using data mining in step 3. This research can be used to look for changes of trend from three perspectives. This research conducted analysis on changes of trend in terms of major classification, Nano technology of 30's, and key words which consist of relevant Nano technology. Second, it is possible to provide real-time information. Trend analysis using search data can provide information depending on the continuously changing market situation due to the real-time information which search data includes. Third, through comparative analysis it is possible to establish a useful corporate policy and strategy by apprehending the trend of the United States which has relatively advanced Nano technology. Therefore, trend analysis using search data like this research can suggest proper direction of policy which respond to market change in a real time, can be used as reference material, and can help reduce cost.

• Convergence Security Journal
• /
• v.8 no.3
• /
• pp.33-39
• /
• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss exponentially weighted moving average chart, in measuring failure time. In control, exponentially weighted moving average chart's uses are efficiency case of analysis with knowing information, Using real software failure time, we are proposed to use exponentially weighted moving average chart and comparative analysis of software failure time.

• Industrial Engineering and Management Systems
• /
• v.8 no.4
• /
• pp.257-263
• /
• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• Nuclear Engineering and Technology
• /
• v.52 no.1
• /
• pp.211-221
• /
• 2020

One fundamental element of probabilistic safety assessment (PSA) is the initiating event (IE) analysis. Since IE frequencies can change over time, time-trend analysis is required to obtain optimized IE frequencies. Accordingly, such time-trend analyses have been employed to estimate industry-average IE frequencies for use in the PSAs of U.S. nuclear power plants (NPPs); existing PSAs of Korean NPPs, however, neglect such analysis in the estimation of IE frequencies. This article therefore provides the method for and results of estimating Korean industry-average IE frequencies using time-trend analysis. It also examines the effects of the IE frequencies obtained from this study on risk insights by applying them to recently updated internal events Level 1 PSA models (at-power and shutdown) for an OPR-1000 plant. As a result, at-power core damage frequency decreased while shutdown core damage frequency increased, with the related contributions from each IE category changing accordingly. These results imply that the incorporation of time-trend analysis leads to different IE frequencies and resulting risk insights. The IE frequency distributions presented in this study can be used in future PSA updates for Korean NPPs, and should be further updated themselves by adding more recent data.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.2
• /
• pp.65-73
• /
• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Communications for Statistical Applications and Methods
• /
• v.9 no.3
• /
• pp.703-713
• /
• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• The Korean Journal of Applied Statistics
• /
• v.36 no.6
• /
• pp.573-589
• /
• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• Convergence Security Journal
• /
• v.11 no.3
• /
• pp.19-24
• /
• 2011

Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.

• Convergence Security Journal
• /
• v.8 no.2
• /
• pp.35-40
• /
• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.