scholarly journals Enterprise Valuation Analysis Based on Grey Prediction Model and Index Selection—A Case Study of Huayi Brothers Media Group

2016 ◽  
Vol 8 (8) ◽  
pp. 11
Author(s):  
Na Luo ◽  
Jiangrui Chen ◽  
Lingyi Kong ◽  
Yuanfeng Zhu
Keyword(s):  
Prediction Method ◽  
Share Price ◽  
Grey Prediction ◽  
Average Value ◽  
Valuation Model ◽  
Enterprise Valuation ◽  
Reasonable Range ◽  
Significant Area ◽  
Relative Valuation ◽  
Media Group

<p>Research on the investment value of enterprises has been a significant area, which the market investors and corporate decision-makers always pay much attention to. In this paper, Huayi Brothers Media Group, the leading enterprise of the film industry, is chosen as the research subject. The paper firstly targeted the difficulties of evaluating Huayi Brothers through analyzing its financial data. Then we used the improved grey prediction method as an absolute valuation model to estimate the cash flow, with relative valuation models, including PE, PB, PS and PEG, as supplements. From the results, we reached a conclusion that these two kinds of valuation models have a similar market value for Huayi Brothers at about 40 billion, which should be reliable when compared with the current average value, about 39 billion, evaluated by 13 official valuation mechanisms. What’s more, the share price of Huayi Brothers in the bull market in 2015 is far higher than the reasonable range of value, and thus we advised that short-term investors have better not make an investment on Huayi Brothers until its share price is in a reasonable range.</p>

Trigonometric Grey Prediction Method for Turkey's Electricity Consumption Prediction

2021 ◽  
pp. 136-154
Author(s):  
Adem Tuzemen
Keyword(s):  
Evaluation Criteria ◽  
Electricity Consumption ◽  
Prediction Method ◽  
Electricity Demand ◽  
Grey Prediction ◽  
Data Set ◽  
Determining Factors ◽  
The Future ◽  
Forecasting Analysis ◽  
Short Term Forecasting

Industry and technology continue to develop rapidly in today's world. The indisputable most important source of this development, energy is among the indispensables of daily life. Since it is one of the determining factors for the country's economy, the future forecast of electricity demand means calculating the future steps. Based on this, to forecast Turkey's electricity demand, it was benefited from grey model (GM) and trigonometric GM (TGM) techniques. The data set includes annual electricity consumption for the period 1970-2018. The performances of the methods determined were compared based on the forecast evaluation criteria (MSE, MAD, MAPE, and RMSE). Short-term forecasting analysis was carried out by determining the method that gives these values to a minimum. In the future forecast, it has been determined that electricity consumption will increase continuously.

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

2020 ◽  
Vol 42 (11) ◽  
pp. 1946-1959
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Jian Cao ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Gaussian Kernel ◽  
Filter Method ◽  
Grey Prediction ◽  
Sea Trial ◽  
False Diagnosis

The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

scholarly journals A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yun Zhang ◽  
Xueming Li ◽  
Jianli Zhang ◽  
Derui Song
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Prediction Method ◽  
Image Data ◽  
Chromatic Aberration ◽  
Grey Prediction ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Matching Algorithm ◽  
Edge Detection Method

In this paper, data mining theory is applied to carry out the field of the pretreatment of remote sensing images. These results show that it is an effective method for carrying out the pretreatment of low-precision remote sensing images by multisource image matching algorithm with SIFT operator, geometric correction on satellite images at scarce control points, and other techniques; the result of the coastline extracted by the edge detection method based on a chromatic aberration Canny operator has a height coincident with the actual measured result; we found that the coastline length of China is predicted to increase in the future by using the grey prediction method, with the total length reaching up to 19,471,983 m by 2015.

scholarly journals Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

2019 ◽  
Vol 11 (21) ◽  
pp. 5921 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Nonlinear Coefficient ◽  
Prediction Method ◽  
Decision Makers ◽  
Grey Model ◽  
Correct Prediction ◽  
Grey Prediction ◽  
Real World Datasets ◽  
Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

scholarly journals Bank Valuation Models – A Comparative Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 116
Author(s):  
Chitra Gunshekhar Gounder ◽  
M. Venkateshwarlu
Keyword(s):  
Public Sector ◽  
Private Sector ◽  
Value Creation ◽  
Shareholder Value ◽  
Market Price ◽  
Residual Income ◽  
Valuation Model ◽  
Residual Income Model ◽  
Income Model ◽  
Relative Valuation

The Bank valuation model was designed based on objective to fit  the most  applicable  valuation model for banks to help in forecasting bank specific decision and also forecast the market value of share. First study the accuracy and explanatory value of the value estimates from the residual income model compared to the estimates from the Relative valuation model for banks. Empirical evidence suggests that the residual income model is superior to the relative valuation model when it comes to measuring bank shareholder value. The results of the comparison suggest that value estimates from the residual income model are even more reliable for banks. On this basis, we conclude that residual income is an appropriate value estimate for the shareholder value of banks. There was positive significant relationship identified between the intrinsic value of bank share determined by RIV model and Market price of share in all the cases by performing correlation and Regression study. This study will be useful for forecasting the possible changes in market price. It was identified that determinants vary as per the working and regulatory condition as determinants impacting private, public and Indian banks were not similar so panel regression model will vary for each cases. It was also identified that Public Sector Bank in India shows more positive progressive trend as compared to private Sector Bank even after the fact that public Sector Bank has higher regulatory restriction as compared to Private Sector banks. This research will serve very useful for the banker to plan and take decision regarding shareholder value creation by implementing proper valuation model for getting appropriate value estimate and also adopting proper internal performance measure for having accurate and regular check on the process of value creation. 

Gas Pipeline Corrosion Prediction Based on Modified Support Vector Machine and Unequal Interval Model

2013 ◽  
Vol 373-375 ◽  
pp. 1987-1994 ◽  
Author(s):  
Wei Dong Zhang ◽  
Bin Shen ◽  
Yi Bo Ai ◽  
Bin Yang
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Prediction Method ◽  
Original Data ◽  
Gas Pipeline ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Grey Prediction ◽  
Interval Model ◽  
Pipeline Corrosion

The corrosion is an important problem for the service safety of oil and gas pipeline. This research focuses. This paper proposed a new prediction algorithm on corrosion prediction of gathering gas pipeline, which combined modified Support Vector Machine (SVM) with unequal interval model. Firstly, grey prediction method with unequal interval model was used to pretreatment original data because there is unequal interval problem in actual collected data of pipeline. Secondly, improved Support Vector Regression (SVR) based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) has been proposed to resolve parameters selection problem for SVR. Finally, the corrosion prediction model of gas pipeline has been proposed which combined improved SVR and unequal interval grey prediction method. The experiment results show this algorithm could increase precision of the pipeline corrosion prediction compared with the traditional SVM. This research provides reliable basis for in-service pipeline life prediction and confirming inspecting cycle.

Research on Data Prediction Methods of Structural Health Monitoring System

2012 ◽  
Vol 256-259 ◽  
pp. 1022-1028
Author(s):  
Chun Xiao ◽  
Xue Ping Hao ◽  
Li Qiao Li ◽  
Wei Li ◽  
Xun Gang Liu
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Model Prediction ◽  
Prediction Method ◽  
Grey Model ◽  
Prediction Methods ◽  
Grey Prediction ◽  
Health Monitoring System ◽  
Term Data

Trend prediction is virtually modeling process for dynamic data. The key to prediction is to establish a model in accordance with actual status, then use the model to predict the trend of object, and infer its behavior in future. Two prediction methods are researched to predict the trend on the observed points of the structure in this paper, which are regression prediction method and grey prediction method. The continuous time strain value of a measured point on Tianxingzhou Yangtze River Bridge is used as data sample for researching. The method of regression analysis is applied for predicting the trend of short-term data, and the method of grey model prediction for predicting long-term data. Regression prediction can assess the health status of the structure and obtain the alarm information effectively by comparing the actual monitoring data with the range of forecast interval. Grey prediction method has great advantages when dealing with poor information. By engineering example this study shows the pros and cons of these two methods, and proves that the method of grey model prediction is more suitable of predicting the trend of object in the structural health monitoring system.

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