证券时间

最后在此基础上，还开发了一个证券时间序列分析的原型系统。

Finally , A prototype system in stock field based the framework has developed .

本文提出的研究方法亦可应用于其他金融证券时间序列预测中。

The study methods this paper proposes can also be applied in others financial securities time series forecasting .

本文深入分析了传统的时间序列模型和神经网络模型，并对一定时期内的证券时间序列的拟合效果进行了比较。

This paper fits a period of security time series with both neural network and time series model .

所以对奇异点方向进行判断，可以更直观的反映出信息对证券时间序列的影响。

So the judgment of the direction of isolated singularity can show the influence of information for stock time series .

本文根据证券时间序列奇异点的特点，采用滑动窗口的对时间序列分割，实现了有效的局部离群点检测。

This paper accordance with the characteristics of anomalous diffraction spots , using slip window segmentation of time-series data detects local isolated singularity .

在这些拟合方法中，尽管可以得到趋势明朗后模型与实际数据的较好拟合效果，但是对于引起证券时间序列趋势改变的奇异点及其邻域内的时序点拟合误差较大。

The results indicate the methods are effective in clear tendency , but singularities changing the trend of time series can not be fitted well .

许多数据挖掘算法试图使奇异点的影响最小化，甚至排除它们。但在证券时间序列中，奇异点可能影响时间序列的趋势，也能反映出许多重要特征，同时携带着重要的投资信息。

Some data mining algorithms try to mitigate the effect of anomalous diffraction spots , but the tread for anomalous diffraction spots has an effect on time series show some characteristics and have certain information for investment .

基于证券价格时间序列的协整优化指数跟踪方法研究

Research on the Cointegration Optimization Index Tracking Approach based on the Time Series of Securities Prices

基金经理的证券从业时间，管理基金的基金经理人数均对基金业绩没有显著的影响。

The fund manager 's time of obtaining employment of securities , the manager 's number of fund of managing fund does not have remarkable influence on the achievement of the fund .

本文在探讨了证券投资风险时间序列非线性特征的基础上，研究了包括AR（P）模型，ARCH方差补偿型预测模型和神经网络预测模型在内的各种非线性风险预测方法以及组合预测方法。

After discussing the non-linear characteristic of the investment risk series , the thesis investigates several forecasting methods for non-linear risk series including AR ( P ) model , ARCH model and neural network model .

针对股票对象的特点，提出了适应股票规律的GEP-STOCK模型，包括n时段-STOCK-GENE，STOCK-fitness以及STOCK-GEP算法，并以上海证券交易指数时间序列数据为对象做了实验.进行了误差和指数涨跌分析。

Based on the features of stock objects , presents the GEP-STOCK model including the STOCK-GENE and the STOCK-fitness that appropriated to the special rules of stocks , and STOCK-GEP algorithm , gives experiments and analysis on the real stock-price index of Shanghai Stock Exchange .

我国证券市场多时间跨度的分形特征研究

Research on the Fractal Property of Multi Time Span of Chinese Securities Market

同时，由于我国证券市场发展时间不长、数据少，因此，利用公开披露的信息使用传统投资决策分析方法存在着明显缺陷。

Hence , the obvious defect exists that the investors utilize the traditional investment decision-making analytical method by information revealed publicly .

虽然国外的研究模型相对成熟，但是由于国内外会计准则以及证券市场存在时间方面的差异，我们并不能照搬国外的模型。

Although the study abroad model is relatively mature , but in the domestic and international accounting standards and securities market timing differences exist , we can not copy foreign models .

您从事有价证券交易的时间有多久，或者如果是个实体，授权直接交易这个帐户的合伙人、主管、受托人有交易有价证券的经验吗？

How many years experience do you , or , if an entity , does the partner , officer , or trustee thereof authorized to direct transactions in the account , have trading securities ?

但是，由于我国开放证券市场的时间不长，缺乏经验，有些配套措施没有跟上，体制也没有完全理顺，证券市场还存在许许多多的问题。

It also makes the monetary system more comPlete and co tent . However , there are a lot of problems In the bond market due to the short opening period and the Insufflcency of experience .

试验表明，用此方法分析中国证券市场的股票时间序列非常有效。

Experiments have shown that this method can efficiently analyze the time series of Chinese Stock Market .

因此，如何把握证券市场开放的时间和节奏，尽快完善市场体系以适应竞争之需是当务之急。

Therefore , it 's urgent for us to seize the right time and rhythm to improve and perfect our securities market .

短期内，证券收益呈现出时间序列正自相关；而长期内，则呈现出时间序列负自相关。

The predictability stands out to be that security return takes on positively auto-correlated in short term while negatively auto-correlated in long term .

由于我国证券市场发展的时间较短，历史数据较少，分形分布的应用需要进一步的验证。

Due to short period of the development of Chinese securities market and lack of historical data , the application of Fractal Distribution needs further verification .

这项安排有助缩短证券交易的结算时间，提高结算效率，最终目标是达到即时货银两讫。

This would enable settlement time for securities transactions to be shortened and therefore made more efficient , with a view in the end of achieving real time dvp .

同时，由于我国证券市场形成的时间不长，法律法规仍不完善，在证券公司破产清算中投资者保护方面还显得比较薄弱。

At the same time , bond market builds before long in our country , laws and statutes does not still improved and perfect , investor protection aspect appears comparatively weak in security company liquidation .

在证券价格服从离散时间算术布朗运动的假设下，得到资产流动性风险最优控制策略，并对该策略进行有关参数的敏感性分析。

Under the assumption that the security ′ s price follows the discrete-time arithmetic Brown motion process , the optimal control strategy of the asset ′ s liquidity risk is proposed and the parameters sensitivity of the strategy is analyzed .

虽然我国证券市场的发展时间较短，机构投资者的发展时间更短，但是机构投资者参与公司治理的事件时有发生，表现出参与公司治理的强烈愿望，这是直接证据。

Although the time for the development of Chinese security market is comparatively short , and the development of institutional investors even shorter , institutional investors now and then participate in corporate governance with their strong desire , as can be seen as direct evidence .

因为信息是证券市场上引起价格变动的决定性因素，所以证券时间序列中的奇异点反应了某种信息对证券时间序列的影响。

Because information is a decisive factor in stock market for price change , anomalous diffraction spots of stock time series marked certain information have affection for stock time series .

通过模型，对证券价格变动趋势进行预测研究，建立灰色GM模型，并针对上海证券交易所某个时间段进行实证模型建立、分析及精度检验，取得了满意的结果。

We take the advantage of GM matrix-setting to analyze , make accuracy test on and set a redistic matrix for some periods of Shanghai Stock Exchange .

实证结果表明，我国证券市场交易过程中的集群性是由于以私人信息为基础的信息交易所引起的，私人信息的进入导致了证券市场在时间方向表现出更大的波动性。

The research results indicate that the transaction clustering is caused by the informed trades and the volatility with respect to time is magnified due to the private information introduction in stock market of China .