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开发市场无效性:通过偏差建模预测短期价格移动(一)

作者:浮云 整理:本网站论文网 录入时间:2011-12-13 23:49:54
开发市场无效性:通过偏差建模预测短期价格移动

[摘 要]  近几年,投资行为主要集中于以长期获利机会为代价的短期获利上。据说,成功的短
期操作,在运行的基础上,需要开发市场无效性,这一概念已加剧了市场有效假说( EMH) 的赞同
者和反对者之间的辩论。依据强形式有效性,EMH 坚持安全价格总是完全反映所有的信息。弱
形式有效性坚持以前数据的随机建模是无用的,因为随机行走过程产生短期价格移动。就反对
EMH而言,作者运用偏差建模———其结构会随时间推移而改变的随机时间系列等式———揭示了
金融和体育赌博市场中的市场无效性并由此得出可行的预测。建模的前提是震动是模型结构改
变的催化剂,还有价格移动在可预测的时期内外波动。这些时期受制于现存的所选图形价格P体
积形状和它们与滞后震动之间的相互作用。预测程序以递增的数学难度水平和递减的投资风险
水平来显示。在更基础的水平上,预测方法使用简单的图表结构使我们接近更复杂建模的预测。转载于 无忧论文网 http://www.wypaper.com
[中图分类号]F 71315    [文献标识码]  A   [文章编号]  100520310 (2002) S120025207
Exploitating Market Inefficiencies :Forecasting Short Term Price
Movements Through Drift Modeling
William S. Mallios
(Craig School of Business ,California State University , Fresno USA)
Abstract : In recent years , investment behavior has been preoccupied with short2term profit at the expense of
longer2term profit opportunities. It is said that successful , short2term performance , on an ongoing basis ,
requires the exploitation of market inefficiencies , a notion that has intensified the debate between opponents
and proponents of the efficient market hypotheses (EMH) . In terms of strong form efficiency , the EMH as2
serts that security prices always fully reflect all available information. Under weak form efficiency the asser2
tion is that stochastic modeling of past data is useless since random walk processes generate short2term price
movements. In rejecting the EMH , the writer shows that applications of drift modeling —stochastic time series
equations whose structure changes over time —expose market inefficiencies in both financial and sports gam2
bling markets and lead to viable forecasts. The modeling premise is that shocks are catalysts for changes in
model structure and that price movements fluctuate in and out identifiable periods of predictability. Such pe2
riods are dominated by the existence of selected geometrical pricePvolume configurations and their interactions
with lagged shocks. Forecasting procedures are presented at increasing levels of mathematical complexity and
decreasing levels of investment risk. At more basic levels , the forecasting methods utilize simple charting
configurations to approximate to forecasts of more complex modeling.
Author information :William S.Mallios is a Professor of Decision Sciences and Director of Economic Devel2
opment Programs in the Former Soviet Union.
2002 年9 月
第16 卷增1 期总增6 期
北京联合大学学报
Journal of Beijing Union University
Sep. 2002
Vol. 16 No. S1 Sum No. S6
1  Setting the Stage :Modeling Scenarios
We precede the general discussion

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