本文關(guān)鍵詞：基于多技術(shù)指標(biāo)模型的滬深300指數(shù)走勢(shì)預(yù)測(cè)　出處：《江西財(cái)經(jīng)大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 滬深300指數(shù) 技術(shù)指標(biāo) 短期預(yù)測(cè)

【摘要】：滬深300指數(shù)反映了中國(guó)證券市場(chǎng)股票價(jià)格變動(dòng)的概貌和運(yùn)行狀況,能夠作為投資業(yè)績(jī)的評(píng)價(jià)標(biāo)準(zhǔn),越來(lái)越受到投資者的青睞。在技術(shù)分析中具有重要代表性的技術(shù)指標(biāo)能不能對(duì)股市進(jìn)行預(yù)測(cè),存在著股市是否達(dá)到弱式有效性的問(wèn)題。本文綜述了研究我國(guó)股市有效性問(wèn)題的相關(guān)文獻(xiàn),利用ADF單位根檢驗(yàn)得出我國(guó)股市未達(dá)到弱式有效性,在這一前提下,基于多技術(shù)指標(biāo)構(gòu)建模型短期預(yù)測(cè)滬深300指數(shù)。 股票中的技術(shù)指標(biāo),是評(píng)價(jià)股票某一特性而構(gòu)造出的數(shù)學(xué)公式,用來(lái)計(jì)算股票相關(guān)數(shù)據(jù)。技術(shù)指標(biāo)分析法,根據(jù)統(tǒng)計(jì)學(xué)中分析方法,考察技術(shù)指標(biāo)間的統(tǒng)計(jì)性質(zhì),構(gòu)建模型預(yù)測(cè)股票未來(lái)走勢(shì)的分析方法。本文根據(jù)技術(shù)指標(biāo)選取的綜合性與系統(tǒng)性原則、科學(xué)性原則、可操作性原則和組合使用原則,挑選出能概括超買(mǎi)超賣(mài)型、成交量型、能量型、趨勢(shì)型和停損型的14個(gè)常用技術(shù)指標(biāo),對(duì)14個(gè)技術(shù)指標(biāo)提示的買(mǎi)賣(mài)點(diǎn)進(jìn)行數(shù)據(jù)預(yù)處理,以便后文分析。 由于單一技術(shù)指標(biāo)在提示股票指數(shù)買(mǎi)賣(mài)點(diǎn)上存在著片面性,利用多個(gè)技術(shù)指標(biāo)提高預(yù)測(cè)準(zhǔn)確率就顯得勢(shì)在必行,而且選取技術(shù)指標(biāo)的方式對(duì)于成功構(gòu)建預(yù)測(cè)模型預(yù)測(cè)滬深300指數(shù)走勢(shì)至關(guān)重要。本文運(yùn)用統(tǒng)計(jì)學(xué)中的泊松相關(guān)系數(shù)矩陣考察技術(shù)指標(biāo)兩兩之間在提示買(mǎi)賣(mài)點(diǎn)上的相似性,再進(jìn)一步利用聚類分析對(duì)技術(shù)指標(biāo)提示買(mǎi)賣(mài)點(diǎn)進(jìn)行分類,最后利用灰色關(guān)聯(lián)度分析對(duì)常用的14個(gè)技術(shù)指標(biāo)與滬深300指數(shù)之間的關(guān)聯(lián)程度數(shù)量化,進(jìn)行排名并結(jié)合樣本數(shù)據(jù)的實(shí)際狀況,最終選取與滬深300指數(shù)關(guān)聯(lián)度比較大的OBV、RSI、PSY、DMI、SAR五個(gè)技術(shù)指標(biāo),基于這五個(gè)技術(shù)指標(biāo)構(gòu)建預(yù)測(cè)模型短期預(yù)測(cè)滬深300指數(shù),在最大精準(zhǔn)率下應(yīng)用最少的技術(shù)指標(biāo)是本文選取技術(shù)指標(biāo)的一個(gè)基本原則。 預(yù)測(cè)方法按統(tǒng)計(jì)性質(zhì)可分為定性預(yù)測(cè)和定量預(yù)測(cè),本文主要是對(duì)滬深300指數(shù)運(yùn)用定量分析手段預(yù)測(cè)其短期走勢(shì)。定量預(yù)測(cè)方法的發(fā)展根據(jù)出現(xiàn)時(shí)間的先后大體上可分為三個(gè)階段：結(jié)構(gòu)計(jì)量模型階段、時(shí)間序列分析階段和智能預(yù)測(cè)階段。由于股票市場(chǎng)無(wú)時(shí)無(wú)刻都受到各種確定或不確定性因素的影響,并且時(shí)間的不可逆性導(dǎo)致了股票市場(chǎng)具有非線性的特征,繼續(xù)使用以前的線性分析或近似分析已無(wú)法準(zhǔn)確分析研究出股票市場(chǎng)的特征和趨勢(shì)。從定量預(yù)測(cè)發(fā)展階段來(lái)說(shuō),目前主要研究集中在非線性、非參數(shù)的智能預(yù)測(cè),把新的預(yù)測(cè)方法應(yīng)用于實(shí)際是否能提高預(yù)測(cè)效果和精度就顯得異常重要。 股市是一個(gè)復(fù)雜的非線性動(dòng)態(tài)系統(tǒng),具有非線性和時(shí)變性等特征,本文在對(duì)股價(jià)主要預(yù)測(cè)方法介紹及評(píng)論后,最終確定決策樹(shù)分析和RBF神經(jīng)網(wǎng)絡(luò)分析預(yù)測(cè)滬深300指數(shù)。決策樹(shù)分析不僅能對(duì)滬深300指數(shù)走勢(shì)方向進(jìn)行預(yù)測(cè),而且能夠驗(yàn)證技術(shù)指標(biāo)用于預(yù)測(cè)的有效性。最后運(yùn)用RBF神經(jīng)網(wǎng)絡(luò)分析對(duì)滬深300指數(shù)短期具體點(diǎn)位進(jìn)行預(yù)測(cè)。實(shí)證分析表明決策樹(shù)分析和RBF網(wǎng)絡(luò)分析能夠準(zhǔn)確地進(jìn)行短期預(yù)測(cè),為投資者短期預(yù)測(cè)提供思路及方法參考。 最后本文分別給予證券監(jiān)管機(jī)構(gòu)和投資者相關(guān)建議,對(duì)證券監(jiān)管機(jī)構(gòu)來(lái)說(shuō)提高我國(guó)證券市場(chǎng)的有效性,關(guān)鍵在于建立信息披露制度,保護(hù)投資者利益,促進(jìn)上市公司的資源優(yōu)化配置。對(duì)投資者來(lái)說(shuō),要將本文分析的思路、方法和結(jié)果應(yīng)用于實(shí)際操作中,投資者應(yīng)關(guān)注以下幾個(gè)方面：(1)基本分析與技術(shù)分析結(jié)合運(yùn)用；(2)順勢(shì)而為；(3)量?jī)r(jià)配合；(4)多種技術(shù)指標(biāo)結(jié)合使用；(5)利用非線性方法預(yù)測(cè)。為保證我國(guó)股市能夠持續(xù)穩(wěn)定的發(fā)展,不斷提高股市有效性、甄別篩選技術(shù)指標(biāo)思路和應(yīng)用恰當(dāng)?shù)念A(yù)測(cè)方法提高預(yù)測(cè)精度,具有較強(qiáng)的現(xiàn)實(shí)意義和一定的實(shí)用價(jià)值。
[Abstract]:Shanghai and Shenzhen 300 index reflects the Chinese stock market stock price situation and operating conditions, can be used as the evaluation standard for investment performance, more and more investors. Technical indicators representative in technical analysis can predict the stock market, the stock market is to reach weak efficiency. This paper a review of the relevant literature on the effectiveness of China's stock market, using the ADF unit root test that China's stock market has not reached the weak efficiency, in this premise, the construction of Shanghai Shenzhen 300 index forecast model based on multi technology index.
Technical indexes in stock, construct a mathematical formula for the evaluation of certain features of the stock, used to calculate stock data. Technical index analysis method, based on the analysis of statistical methods, statistical analysis on nature of technical indicators. The analysis method to construct a model to forecast future stock trend. Based on the comprehensive and systematic principle and technology index selection, scientific principles, operational principles and the combination principle, the selection can be summarized OBOS type, volume type, power type, 14 commonly used technical indexes and stop the trend, to suggest that the 14 technical indexes of the trading point for data preprocessing for later analysis.
Due to the single technical indicators to forecast the stock index trading point on one sidedness, the use of a number of technical indicators improve the accuracy of prediction is imperative, and the selection of technical indicators for the success of the model to predict the trend of the CSI 300 index is very important. This paper use Poisson correlation coefficient matrix in statistics on technical indicators 22 between the tips of similarity trading points, then use clustering analysis to classify technical indicators suggested that the point of sale, finally using grey relational analysis to analyze the degree of correlation between the number of 14 technical indicators and the Shanghai and Shenzhen 300 commonly used index, ranking and combined with the actual situation of the sample data, and finally selected the Shanghai and Shenzhen 300 index correlation of high OBV, RSI, PSY, DMI, SAR five technical indicators, build prediction model for short-term forecasting of Shanghai and Shenzhen 300 index based on the five technology The application of the least technical index to the maximum precision is a basic principle of selecting technical indicators in this paper.
According to the statistical properties of prediction methods can be divided into qualitative forecast and quantitative forecast, this paper is mainly on the Shanghai and Shenzhen 300 index by means of quantitative analysis to predict the short-term trend. Quantitative prediction method of development according to the times can be divided into three stages: the stage of structural econometric model, time series analysis stage and intelligent prediction stage. Because the stock market is influenced by various effects or determined every hour and moment of uncertainty, and the irreversibility of time led to the stock market has nonlinear characteristics, continue to use the previous linear analysis or similar analysis has been unable to accurately analyze the characteristics and trends of the stock market. From the quantitative prediction of the development stage, the main research focus on nonlinear, non parametric intelligent prediction, the application of the new forecasting method to the actual effect and can improve the prediction accuracy is abnormal It's important.
The stock market is a complex nonlinear dynamic system with nonlinear and time-varying characteristics, based on the introduction and comment on the main stock price prediction method, final decision tree analysis and RBF neural network prediction and analysis of the CSI 300 index. The decision tree analysis can not only predict the trend of the CSI 300 index, effective and can be used to prediction verification technology index. Finally using RBF neural network analysis on the Shanghai and Shenzhen 300 index short-term specific point prediction. The empirical analysis shows that the decision tree analysis and RBF network analysis can accurately predict and provide reference ideas and methods to forecast short-term investors.
At the end of this paper were given the securities regulators and investors related suggestions, improve the effectiveness of China's securities market of securities regulators, the key lies in the establishment of information disclosure system, to protect the interests of investors, promoting the optimal allocation of resources of listed companies. For investors, to the analysis of the ideas, methods and results are applied to the actual operation, investors should focus on the following aspects: (1) the fundamental analysis and technical analysis combined with the use of the flow; (2); (3) with volume price; (4) use a variety of technical indicators; (5) using nonlinear prediction method. For the development of China's stock market to ensure sustained and stable, continuously improve the stock market efficiency to improve the prediction accuracy of screening, screening technology index methods and application of appropriate forecasting methods, it has a strong practical significance and practical value.

【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王遠(yuǎn)林;;有效市場(chǎng)假說(shuō)及其檢驗(yàn)的新進(jìn)展[J];東北財(cái)經(jīng)大學(xué)學(xué)報(bào);2008年03期

2 劉付芬;;人工神經(jīng)網(wǎng)絡(luò)的研究與應(yīng)用[J];福建電腦;2009年08期

3 陸蓉,徐龍炳;“牛市”和“熊市”對(duì)信息的不平衡性反應(yīng)研究[J];經(jīng)濟(jì)研究;2004年03期

4 王美今,孫建軍;中國(guó)股市收益、收益波動(dòng)與投資者情緒[J];經(jīng)濟(jì)研究;2004年10期

5 李虹來(lái);勒中堅(jiān);;灰色關(guān)聯(lián)分析在農(nóng)業(yè)現(xiàn)代化評(píng)價(jià)體系中的應(yīng)用[J];江西財(cái)經(jīng)大學(xué)學(xué)報(bào);2007年01期

6 劉葉玲;高玲;;利用技術(shù)指標(biāo)及多元回歸模型預(yù)測(cè)股票價(jià)格[J];技術(shù)與創(chuàng)新管理;2010年02期

7 史代敏;上海股票市場(chǎng)波動(dòng)的周內(nèi)效應(yīng)[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2003年06期

8 方匡南;紀(jì)宏;路遜;;股票技術(shù)指標(biāo)相似性與有效性研究[J];統(tǒng)計(jì)與信息論壇;2009年09期

9 曹欣榮;樓章華;孫宏巍;;基于RBF神經(jīng)網(wǎng)絡(luò)的水閘垂直位移時(shí)間序列預(yù)測(cè)模型[J];三峽大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年05期

10 羅爭(zhēng);張e，

本文編號(hào)：1398269