|本期目录/Table of Contents|

[1]潘文超.以广义回归神经网络预测共同基金报酬[J].长安大学学报(社科版),2007,9(04):55-58.
　PAN Wen-chao.Forecast for mutual fund returns with gerenal regression neural network[J].Journal of Chang＇an University(Social Science Edition),2007,9(04):55-58.
点击复制

以广义回归神经网络预测共同基金报酬 (PDF)
分享到：

《长安大学学报（社科版）》[ISSN:1671-6248/CN:61-1391/C]

卷:
第9卷
期数:
2007年04期
页码:
55-58
栏目:
应用经济学研究
出版日期:
2007-12-20

文章信息/Info

Title:
Forecast for mutual fund returns with gerenal regression neural network
作者:
潘文超
兰阳技术学院信息管理系,台湾台北　104
Author(s):
PAN Wen-chao
Department of Information Management, Lanyang Institute of Technology, Taibei 104, Taiwan, China
关键词:
灰关联分析; 灰预测; 广义回归神经网络; 多元回归模型; 遗传算法
Keywords:
grey relational analysis;  grey prediction;  general regression neural network;  multiple regession;  genetic algorithm
分类号:
F7830.91
DOI:
-
文献标志码:
A
摘要:
鉴于近年来许多相关文献成功地运用广义回归神经网络进行财经方面的预测,以及国内共 同基金净值之预测与报酬率评估。通过搜集国内基金资料,以灰关联分析法进行各基金投资绩效 分析,挑选投资绩效良好的共同基金作为投资标的;再以广义回归神经网络建立预测模型,与灰预 测模型、多元回归模型进行预测能力及报酬率的比较分析。5种预测绩效评价指标、5组数据交互 验证散布图及报酬率分析表明:广义回归神经网络在预测能力及预测报酬率上均有很好的表现。
Abstract:
In recent years, there are many relevant documents that are successfully in general regression neural network for the financial sector forecasts. This paper adoptes the general regression neural network for the prediction of the net value of the domestic mutual fund and for evaluation of the return of the investment. The author picks out a lot of fund information at home, analysizes its investment performance with grey relational analysis, and select some good investment performances of mutual funds as investment targets. Through general regression neural network model, he sets up the prediction model and with grey prediction and multiple regression model, he conducts the comparative analysis on the accuracy of the prediction and the return rate. It is found that it is better to predict the return rate with general regression neural network than with grey prediction and multiple regression model. On the basis of the evaluation of the 5 indexes of the performance management,and 5 group interactive data validation map, the gereral regression neural network can perform well in prediction and the prediction of return rates.

参考文献/References:

[1]　李沃墙.共同基金的绩效评估及净值预测-遗传算法的 运用[J].台湾经济金融月刊,1997,33(12):45-62.
[2]　余尚武,许意铃.共同基金净值之预测———灰色理论、 类神经网络及适应性类神经模糊推论系统之应用[J]. 中华管理评论,2005,8(3):1-33.
[3]　Bowden G J,Nixon J B,Dandy G C,et al.Forecasting chlorine residuals in a water distribution system using a general regression neural network[J].Mathematical and Computer Modelling,2006,44(5/6):469-484.
[4]　Jeyamkondan S,Jayas D S, Holley R A.Microbial growth modelling with artificial neural networks[J]. International Journal of Food Microbiology,2004,64 (3):343-354.
[5]　杜　壮,陈勇雄.智能型服务复原诊断系统———以快餐 业为例[M].台北:儒林书局,2001.
[6]　Specht D F.Probabilistic neural networks and the poly- nomial adaline as complementary techniques for classi- fication[J].IEEE Trans on Neural Networks,1990,1 (1):111-121.
[7]　叶怡成.应用类神经网络[M].台北:儒林书局,1998.
[8]　Specht D F.A general regression neural network[J]. IEEE Tras Neural Networks,1991,2(6):568-576.
[9]　DENG Ju-long.The control problems of grey system [J].System& Control Letters,1982,3(5):288-294.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期:2007-08-13
作者简介:潘文超(1966-),男,江苏南京人,副教授。
更新日期/Last Update: 2007-12-20