摘要
多维球面模型是从资源监测发展起来的 ,基于向量的多元分析方法 .向量是既有量值 ,又有方向的变量 .多维球面模型把传统的向量分析从三维扩展到任意多维 ,可以同时处理任意多个变量 .多维球面模型定义了向量除法 ,可以处理指数增长 ,因此特别适用于生物、金融、信息、经济等领域的变量 .应用于基金市场时 ,模型以基金为坐标轴建立多维基金空间 ,用多元向量表示基金市场 :以向量长度做指数表示市场的量值 ,以向量的方向表示市场状态 ;并用后前余弦的商表示市场的动态 ,称多维即时趋势 ;进而根据趋势值将基金排序 ,根据基金的序位决定售购 .在 7个月 ( 1 998.6.1 9～ 1 999.1 .2 9)的投资实验中 ,多维球面模型指导的个人退休基金的增率是 2 5.82 % .这个增率不仅超过了 2 0个基金市场的平均增率 ( 2 .64% ) ,而且超过了市场中最好的基金的增率 ( 1 8.98% ) .目前 ,尚不能用传统的风险或投机的概念来解释 MDSM的高增率 .多维球面模型假定变量无关 。
The MultiDimensional Sphere Model is a new multivariate data analysis method based on extended vector analysis derived from natural resource monitoring. A vector is a quantity stating both a magnitude and direction. MDSM extended the classic vector analysis from 3 dimension to multidimension, so it can handle more than three variables simultaneously. MDSM defined vector division, so it can manipulate the exponential growth. Thus, it is suitable for variables in sciences of biology, financing, information, and economics. When applied to mutual fund data, MDSM builds a multidimensional space with funds, expresses the market with a multicomponent vector. It expresses the quantity of the market with vector length, and expresses the state with direction. It uses the quotient of cosine values, present over previous, to express the changing trend of the funds, and trade them based on their trend values. In a seven month test, an account directed by MDSM returned 25.82%, not only higher than the average of the 20 involved funds (2.64%), but also better than the best individual fund performance (18.98%).
出处
《内蒙古大学学报（自然科学版）》
CAS
CSCD
北大核心
2001年第2期130-141,共12页
Journal of Inner Mongolia University：Natural Science Edition
