The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip...The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.展开更多
基于时变参数向量自回归(time-varying parameter-vector auto regression,TVPVAR)模型,考察了经济政策不确定性、投资者情绪与股价同步性之间的时变关联性.模型估计结果表明,经济政策不确定性对股价同步性主要表现为中短期的正向影响,...基于时变参数向量自回归(time-varying parameter-vector auto regression,TVPVAR)模型,考察了经济政策不确定性、投资者情绪与股价同步性之间的时变关联性.模型估计结果表明,经济政策不确定性对股价同步性主要表现为中短期的正向影响,且波动比较明显,长期影响则相对较弱;投资者情绪对股价同步性表现为负向影响,且短期影响最为明显,长期影响则较弱.时点脉冲函数结果显示,在不同时间点上,股价同步性对经济政策不确定性的冲击具有正向响应,对投资者情绪的冲击具有负向响应,且不同时间点的响应程度和响应时间均存在差异.这些结论为进一步完善政策调控体系,规范和引导投资者行为,促进市场理性化提供了思路.展开更多
To reveal the quantitative relationship between research and development (R&D) investment and gross domestic product (GDP) in China, we have demonstrated and analyzed the relationship between R&D investment an...To reveal the quantitative relationship between research and development (R&D) investment and gross domestic product (GDP) in China, we have demonstrated and analyzed the relationship between R&D investment and science and technology (S&T) progress, and based on a mount of S&T statistical data, have proceeded demonstration research of the relationship between R&D investment and GDP in China with Solow and vector auto regression (VAR) models. Cubic curve fitting and cross-correlation analysis of them with SPSS have shown that there is a strong synchronic relationship between R&D investment and GDP.展开更多
Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction mod...Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.展开更多
Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are ene...Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.展开更多
In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incom...In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incomplete historical QoS data,traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments.In this paper,we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices.By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD)with the classical ARIMA model,we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently.Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.展开更多
文摘The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.
文摘基于时变参数向量自回归(time-varying parameter-vector auto regression,TVPVAR)模型,考察了经济政策不确定性、投资者情绪与股价同步性之间的时变关联性.模型估计结果表明,经济政策不确定性对股价同步性主要表现为中短期的正向影响,且波动比较明显,长期影响则相对较弱;投资者情绪对股价同步性表现为负向影响,且短期影响最为明显,长期影响则较弱.时点脉冲函数结果显示,在不同时间点上,股价同步性对经济政策不确定性的冲击具有正向响应,对投资者情绪的冲击具有负向响应,且不同时间点的响应程度和响应时间均存在差异.这些结论为进一步完善政策调控体系,规范和引导投资者行为,促进市场理性化提供了思路.
文摘To reveal the quantitative relationship between research and development (R&D) investment and gross domestic product (GDP) in China, we have demonstrated and analyzed the relationship between R&D investment and science and technology (S&T) progress, and based on a mount of S&T statistical data, have proceeded demonstration research of the relationship between R&D investment and GDP in China with Solow and vector auto regression (VAR) models. Cubic curve fitting and cross-correlation analysis of them with SPSS have shown that there is a strong synchronic relationship between R&D investment and GDP.
文摘Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.
文摘Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.
文摘In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incomplete historical QoS data,traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments.In this paper,we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices.By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD)with the classical ARIMA model,we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently.Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.