The research of the identification and evaluation of entrepreneurial opportunity is a hotspot in entrepreneurial management research. On the basis of literature review, this paper establishes a theoretical model for the process mechanism of identification and evaluation of entrepreneurial opportunity. Moreover, the paper verifies the theoretical model by some cases so that it will be useful for the subsequent research in the related area.
{"title":"The Identification and Evaluation Mechanism of Entrepreneurial Opportunity","authors":"H. Lai, Yuyong Liu","doi":"10.1109/CSO.2011.270","DOIUrl":"https://doi.org/10.1109/CSO.2011.270","url":null,"abstract":"The research of the identification and evaluation of entrepreneurial opportunity is a hotspot in entrepreneurial management research. On the basis of literature review, this paper establishes a theoretical model for the process mechanism of identification and evaluation of entrepreneurial opportunity. Moreover, the paper verifies the theoretical model by some cases so that it will be useful for the subsequent research in the related area.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115145135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the smoothness of curve fitted by the interest rate term structure model of polynomial spline functions, the adaptive semi parametric regression with a penalized item is introduced to estimate the unknown parameters. The generalized cross-validation method is discussed to select the smoothing parameter, and genetic algorithm is applied to search the optimal smoothing parameter. Then, the empirical results show that this model with penalty function is relatively effective in China. However, the curve fitting smoothness is improved to some extend at the expense of fitting accuracy.
{"title":"The Semiparametric Model of Interest Rate Term Structure Based on GCV Method and Its Empirical Comparison","authors":"Shuyi Ren, Fengmei Yang, Rongxi Zhou","doi":"10.1109/CSO.2011.284","DOIUrl":"https://doi.org/10.1109/CSO.2011.284","url":null,"abstract":"In order to improve the smoothness of curve fitted by the interest rate term structure model of polynomial spline functions, the adaptive semi parametric regression with a penalized item is introduced to estimate the unknown parameters. The generalized cross-validation method is discussed to select the smoothing parameter, and genetic algorithm is applied to search the optimal smoothing parameter. Then, the empirical results show that this model with penalty function is relatively effective in China. However, the curve fitting smoothness is improved to some extend at the expense of fitting accuracy.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123328551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a group update algorithm based on truncated trust region strategy for large-scale sparse unconstrained optimization. In large sparse optimization computing the whole Hessian matrix and solving exactly the Newton-like equations at each iteration can be considerably expensive. By the method the elements of the Hessian matrix are updated successively and periodically via groups during iterations and an inaccurate solution to the Newton-like equations is obtained by truncating the inner iteration under certain control rule. Besides, we allow that the current direction exceeds the trust region bound if it is a good descent direction satisfying some descent conditions. Some good convergence properties are kept and we contrast the computational behavior of our method with that of other algorithms. Our numerical tests show that the algorithm is promising and quite effective, and that its performance is comparable to or better than that of other algorithms available.
{"title":"A Group Update Sparse Method Using Truncated Trust Region Strategy","authors":"Junxiang Li, Tao Dai, Feng Cheng, Jia-zhen Huo","doi":"10.1109/CSO.2011.31","DOIUrl":"https://doi.org/10.1109/CSO.2011.31","url":null,"abstract":"We present a group update algorithm based on truncated trust region strategy for large-scale sparse unconstrained optimization. In large sparse optimization computing the whole Hessian matrix and solving exactly the Newton-like equations at each iteration can be considerably expensive. By the method the elements of the Hessian matrix are updated successively and periodically via groups during iterations and an inaccurate solution to the Newton-like equations is obtained by truncating the inner iteration under certain control rule. Besides, we allow that the current direction exceeds the trust region bound if it is a good descent direction satisfying some descent conditions. Some good convergence properties are kept and we contrast the computational behavior of our method with that of other algorithms. Our numerical tests show that the algorithm is promising and quite effective, and that its performance is comparable to or better than that of other algorithms available.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123350065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a multi-objective programming model for the asset portfolio selection problem. Use the idea of fuzzy sets to set up the membership function of the objective function for providing the satisfaction degree to the return and risk of the portfolio. Then the approach is used to achieve the high test degree of each of the membership functions and abtain the satisfactary solution for the decision maker. Numerical examples are given to demonstrate the proposed approach.
{"title":"Multi-objective Programming Model for Asset Portfolio Selection","authors":"Wenguang Tang, Fenxia Zhao","doi":"10.1109/CSO.2011.171","DOIUrl":"https://doi.org/10.1109/CSO.2011.171","url":null,"abstract":"This paper presents a multi-objective programming model for the asset portfolio selection problem. Use the idea of fuzzy sets to set up the membership function of the objective function for providing the satisfaction degree to the return and risk of the portfolio. Then the approach is used to achieve the high test degree of each of the membership functions and abtain the satisfactary solution for the decision maker. Numerical examples are given to demonstrate the proposed approach.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More and more family holding companies become the public companies in recent years. The problem of executives' agency risk is an important factor which influences their rapid development., This article tries to do the exploratory research of evaluating the agency risk of executives. At first this article makes an in-deep analysis on the indices of executive agency risk in family holding public companies based on executives' devotion and encroachment. Then model is established with the operation of logistic regression. In the end the article made recommendations about preventing the agency risk of executives.
{"title":"An Empirical Study on the Agency Risk of Executives in the Family Holding Public Company","authors":"Jinguo Xin, Tingting Wei","doi":"10.1109/CSO.2011.74","DOIUrl":"https://doi.org/10.1109/CSO.2011.74","url":null,"abstract":"More and more family holding companies become the public companies in recent years. The problem of executives' agency risk is an important factor which influences their rapid development., This article tries to do the exploratory research of evaluating the agency risk of executives. At first this article makes an in-deep analysis on the indices of executive agency risk in family holding public companies based on executives' devotion and encroachment. Then model is established with the operation of logistic regression. In the end the article made recommendations about preventing the agency risk of executives.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121409562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional road supply models assume full knowledge of the inverse demand function, such that the supply-demand equilibrium point can be easily obtained. However, in practice, it is often difficult to completely characterize the inverse demand function, especially for a congested road. In this paper, we study the traffic volume estimating problem for a congested road with partial information about the inverse demand function, i.e., range or total willing to pay for travel. In particular, we first propose a minimax regret model for minimizing the planner's maximum opportunity cost of not acting optimally, and then obtain some analytical solutions by transforming it into a moment problem equivalently with some simplified assumptions. The model and results in this paper are both instructive and can be extended to investigate more realistic scenarios for practical application.
{"title":"Regret Approach in Estimating Traffic Volume for a Congested Road with Unknown Inverse Demand Function","authors":"Tianliang Liu, Yan Wang","doi":"10.1109/CSO.2011.298","DOIUrl":"https://doi.org/10.1109/CSO.2011.298","url":null,"abstract":"Traditional road supply models assume full knowledge of the inverse demand function, such that the supply-demand equilibrium point can be easily obtained. However, in practice, it is often difficult to completely characterize the inverse demand function, especially for a congested road. In this paper, we study the traffic volume estimating problem for a congested road with partial information about the inverse demand function, i.e., range or total willing to pay for travel. In particular, we first propose a minimax regret model for minimizing the planner's maximum opportunity cost of not acting optimally, and then obtain some analytical solutions by transforming it into a moment problem equivalently with some simplified assumptions. The model and results in this paper are both instructive and can be extended to investigate more realistic scenarios for practical application.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123962346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate prediction on crude oil price in a long time horizon has been appealing both for academia and practitioners. Recursive strategy and direct strategy are two mainstream modeling schemas widely used for multi-step-ahead prediction in the context of time series modeling. In this paper, a comparative study has been conducted to justify these two strategies in multi-step-ahead prediction for crude oil price with Support Vector Regression (SVR). The experimental results show the direct strategy has more consistent performance than recursive one in the various experimental setting.
{"title":"A Comparative Study of Multi-step-ahead Prediction for Crude Oil Price with Support Vector Regression","authors":"Yukun Bao, Yunfei Yang, T. Xiong, Jinlong Zhang","doi":"10.1109/CSO.2011.70","DOIUrl":"https://doi.org/10.1109/CSO.2011.70","url":null,"abstract":"Accurate prediction on crude oil price in a long time horizon has been appealing both for academia and practitioners. Recursive strategy and direct strategy are two mainstream modeling schemas widely used for multi-step-ahead prediction in the context of time series modeling. In this paper, a comparative study has been conducted to justify these two strategies in multi-step-ahead prediction for crude oil price with Support Vector Regression (SVR). The experimental results show the direct strategy has more consistent performance than recursive one in the various experimental setting.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129369042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a novel modular-type Support Vector Machine (SVM) is presented to simulate rainfall prediction. First of all, a bagging sampling technique is used to generate different training sets. Secondly, different kernel function of SVM with different parameters, i.e., base models, are then trained to formulate different regression based on the different training sets. Thirdly, the Partial Least Square (PLS) technology is used to select choose the appropriate number of SVR combination members. Finally, a $nu$-SVM can be produced by learning from all base models. The technique will be implemented to forecast monthly rainfall in the Guangxi, China. Empirical results show that the prediction by using the SVM combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.
{"title":"A Novel Nonlinear Combination Model Based on Support Vector Machine for Rainfall Prediction","authors":"Kesheng Lu, Lingzhi Wang","doi":"10.1109/CSO.2011.50","DOIUrl":"https://doi.org/10.1109/CSO.2011.50","url":null,"abstract":"In this study, a novel modular-type Support Vector Machine (SVM) is presented to simulate rainfall prediction. First of all, a bagging sampling technique is used to generate different training sets. Secondly, different kernel function of SVM with different parameters, i.e., base models, are then trained to formulate different regression based on the different training sets. Thirdly, the Partial Least Square (PLS) technology is used to select choose the appropriate number of SVR combination members. Finally, a $nu$-SVM can be produced by learning from all base models. The technique will be implemented to forecast monthly rainfall in the Guangxi, China. Empirical results show that the prediction by using the SVM combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130947836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve the problem that the iterated closest contour point(ICCP) algorithm diverges easily when the initial INS error is large, the terrain contour matching (TERCOM) algorithm is firstly used to reduce the initial INS error, then ICCP algorithm is used to obtain the best matching position. Two matching difference is used as the measurement of Kalman filter, INS error is corrected and the optimal estimate is obtained. The correlative analysis MSD is only introduced in the coarse matching stage, and the sliding window is used in the precise matching stage to improve the algorithm efficiency. Simulations are performed and the results show that the proposed combinational algorithm matching process is more stable and the precision is higher than traditional algorithm.
{"title":"A Combinational Underwater Aided Navigation Algorithm Based on TERCOM/ICCP and Kalman Filter","authors":"G. Yuan, Hongwei Zhang, Kefei Yuan, Chunyan Tao","doi":"10.1109/CSO.2011.23","DOIUrl":"https://doi.org/10.1109/CSO.2011.23","url":null,"abstract":"In order to solve the problem that the iterated closest contour point(ICCP) algorithm diverges easily when the initial INS error is large, the terrain contour matching (TERCOM) algorithm is firstly used to reduce the initial INS error, then ICCP algorithm is used to obtain the best matching position. Two matching difference is used as the measurement of Kalman filter, INS error is corrected and the optimal estimate is obtained. The correlative analysis MSD is only introduced in the coarse matching stage, and the sliding window is used in the precise matching stage to improve the algorithm efficiency. Simulations are performed and the results show that the proposed combinational algorithm matching process is more stable and the precision is higher than traditional algorithm.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130054817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many problems in enterprise running faced by managers can be resolved by simulation of the corresponding system dynamics model. It is often desirable to forecasting enterprise running in the next period. And managers hope to find the reason quickly if there are some exceptions in the running. The main purpose of this paper is to design a simulation and analysis system, which can help managers solve the above problems. In this article, we design a framework, and give the details of main modules. We also discuss the characteristics and future use of the system.
{"title":"Design of an Enterprise Dynamic Performance Simulation and Analysis System","authors":"Zheng Li, Y. Chai, Yi Liu","doi":"10.1109/CSO.2011.117","DOIUrl":"https://doi.org/10.1109/CSO.2011.117","url":null,"abstract":"Many problems in enterprise running faced by managers can be resolved by simulation of the corresponding system dynamics model. It is often desirable to forecasting enterprise running in the next period. And managers hope to find the reason quickly if there are some exceptions in the running. The main purpose of this paper is to design a simulation and analysis system, which can help managers solve the above problems. In this article, we design a framework, and give the details of main modules. We also discuss the characteristics and future use of the system.","PeriodicalId":210815,"journal":{"name":"2011 Fourth International Joint Conference on Computational Sciences and Optimization","volume":"81 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120879268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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