本文關(guān)鍵詞： 上證指數(shù) 時(shí)間序列模型 狀態(tài)空間 神經(jīng)網(wǎng)絡(luò) 實(shí)證分析　出處：《上海交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：對(duì)于股票價(jià)格時(shí)間序列預(yù)測(cè)研究的必要性已經(jīng)成為實(shí)務(wù)界和學(xué)術(shù)界的普遍共識(shí)，但是股票價(jià)格時(shí)間序列本身具有復(fù)雜性、多樣性和善變性，并且有許多因素在在影響著股票的變化，由于這些影響股市的因素，有些可以度量，，有些卻難以度量，因此很難將所有的因素都通過計(jì)算量化評(píng)價(jià)從而進(jìn)行科學(xué)研究的計(jì)算和評(píng)價(jià)�，F(xiàn)代統(tǒng)計(jì)學(xué)對(duì)于股票市場(chǎng)的研究存在的問題是往往看重樣本內(nèi)的擬合效果，研究?jī)?yōu)化各種模型從而達(dá)到對(duì)樣本內(nèi)數(shù)據(jù)的完美的回歸，而對(duì)樣本內(nèi)擬合精度的日益嚴(yán)格要求往往使得人們忽略了研究模型的魯棒性，無法將樣本內(nèi)優(yōu)異的擬合度推廣到樣本外，尤其是面對(duì)股票市場(chǎng)長(zhǎng)期走勢(shì)不斷出現(xiàn)的突變，不具有快速的反應(yīng)能力，樣本內(nèi)外的擬合度相差過大反而使預(yù)測(cè)不具有實(shí)用性和前瞻性。而本文試圖在對(duì)傳統(tǒng)以及現(xiàn)代一些流行的統(tǒng)計(jì)模型進(jìn)行比較分析的基礎(chǔ)上選擇出樣本內(nèi)樣本外擬合度誤差最小，且長(zhǎng)期表現(xiàn)更為穩(wěn)定的模型，使預(yù)測(cè)更具實(shí)用性。 目前對(duì)股票價(jià)格的分析和預(yù)測(cè)盡管有大量的分析工具和模型，但總的大類可以分為基本面分析，技術(shù)分析和統(tǒng)計(jì)類分析，而本文將要討論的就是其中的經(jīng)濟(jì)統(tǒng)計(jì)類分析。事實(shí)上，60年前就已經(jīng)出現(xiàn)有關(guān)股票價(jià)格的時(shí)間序列預(yù)測(cè)研究，但因?yàn)橛绊懝善眱r(jià)格的因素多變復(fù)雜且難以定義，每個(gè)人的理解和運(yùn)用都不同，因此對(duì)應(yīng)而使用的時(shí)間序列預(yù)測(cè)方法也多種多樣，但總體來看可以分成兩大類：第一類是較為傳統(tǒng)的統(tǒng)計(jì)學(xué)方法，包括AR,MA,ARMA,ARIMA,ARCH和GARCH等，以及其后在其基礎(chǔ)上衍生的統(tǒng)計(jì)方法包括本文將會(huì)采用的狀態(tài)空間模型，而另一類是近幾年來普遍流行的以人工神經(jīng)網(wǎng)絡(luò)模型為代表的計(jì)算智能方法。本文通過分別回顧并總結(jié)迄今為止現(xiàn)有的關(guān)于股票價(jià)格時(shí)間序列預(yù)測(cè)的兩大類方法，并進(jìn)而基于現(xiàn)有的關(guān)于股票價(jià)格時(shí)間序列預(yù)測(cè)的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行現(xiàn)有研究的評(píng)述，指出當(dāng)前研究存在的問題，并對(duì)1998年以來的股票數(shù)據(jù)進(jìn)行實(shí)證分析，對(duì)，，狀態(tài)空間模型和人工神經(jīng)網(wǎng)絡(luò)模型分別進(jìn)行固定模型下的滾動(dòng)預(yù)測(cè)和滾動(dòng)更新模型下的滾動(dòng)預(yù)測(cè)兩種實(shí)證分析，論文在對(duì)四種模型進(jìn)行擬合度的比較后得出簡(jiǎn)單模型的擬合情況雖然在短期內(nèi)較差，但在長(zhǎng)期，尤其是面對(duì)市場(chǎng)發(fā)生突變的情況下，擬合度相比復(fù)雜模型更好，因此更具有長(zhǎng)期穩(wěn)定性。復(fù)雜模型由于樣本內(nèi)過擬合，短期預(yù)測(cè)時(shí)波動(dòng)性較大，長(zhǎng)期預(yù)測(cè)在遇到市場(chǎng)波動(dòng)時(shí)往往會(huì)出現(xiàn)不穩(wěn)定的情況，預(yù)測(cè)準(zhǔn)確度不如簡(jiǎn)單模型。此結(jié)論為未來實(shí)證模型的選擇提供一定的參考價(jià)值。
[Abstract]:The necessity of stock price time series prediction has become a common understanding in the field of practice and academia, but the stock price time series itself has complexity, diversity and variability. And there are many factors in the impact of stock changes, because of these factors affecting the stock market, some can be measured, some difficult to measure. Therefore, it is difficult to calculate and evaluate all the factors through quantitative evaluation. The problem of modern statistics for stock market research is that the fitting effect in samples is often valued. Research and optimization of various models to achieve the perfect regression of the data in the sample, and the increasingly stringent requirements for the precision of fitting in the sample often make people ignore the robustness of the model. It is impossible to extend the excellent fitting degree in the sample to outside the sample, especially in the face of the sudden changes in the long-term trend of the stock market, so it does not have the ability to react quickly. The difference between the fitting degree inside and outside the sample makes the prediction not practical and prospective. However, this paper tries to select the sample outside the sample on the basis of comparing and analyzing some traditional and modern popular statistical models. Minimum coincidence error. And long-term performance more stable model, make the prediction more practical. Although there are a lot of tools and models for stock price analysis and prediction, the general categories can be divided into fundamental analysis, technical analysis and statistical analysis. What this paper will discuss is the economic statistical analysis. In fact, 60 years ago, there has been time series prediction of stock prices. However, because the factors affecting stock prices are complex and difficult to define, each person's understanding and application are different, so the corresponding time series prediction methods are also varied. But in general, it can be divided into two categories: the first is the more traditional statistical methods, including ARMAMAA ARIMAARCH and GARCH. The statistical methods derived from them include the state-space model which will be adopted in this paper. The other is the computational intelligence method which is popular in recent years, which is represented by artificial neural network model. In this paper, we review and summarize two kinds of methods about stock price time series prediction. . And then based on the existing research status quo of stock price time series prediction at home and abroad, pointed out the existing problems. And the empirical analysis of stock data since 1998, yes, The state space model and the artificial neural network model respectively carry on the rolling forecast under the fixed model and the rolling forecast under the rolling update model two kinds of empirical analysis. After comparing the fitting degree of the four models, it is concluded that the fitting condition of the simple model is worse in the short term, but in the long run, especially in the face of the market mutation, the fitting degree is better than the complex model. Because of the over-fitting in the sample, the volatility of the short-term prediction is large, and the long-term prediction will often be unstable in the event of market volatility. The prediction accuracy is not as good as the simple model. This conclusion provides a certain reference value for the choice of future empirical model.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 何永沛;;ARMA模型參數(shù)估計(jì)算法改進(jìn)及在股票預(yù)測(cè)中的應(yīng)用[J];重慶工學(xué)院學(xué)報(bào)(自然科學(xué)版);2009年02期

2 陳必焰;戴吾蛟;蔡昌盛;匡翠林;;時(shí)間序列與神經(jīng)網(wǎng)絡(luò)組合方法在電離層TEC預(yù)報(bào)中的應(yīng)用[J];工程勘察;2011年04期

3 朱大奇;人工神經(jīng)網(wǎng)絡(luò)研究現(xiàn)狀及其展望[J];江南大學(xué)學(xué)報(bào);2004年01期

4 王新武;;股票價(jià)格預(yù)測(cè)模型[J];隴東學(xué)院學(xué)報(bào);2012年03期

5 孫曉瑩;李曉靜;;數(shù)據(jù)挖掘在股票價(jià)格組合預(yù)測(cè)中的應(yīng)用[J];計(jì)算機(jī)仿真;2012年07期

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