本文選題：灰理論 + 支持向量機(jī)��； 參考：《武漢理工大學(xué)》2012年碩士論文

【摘要】：隨著社會經(jīng)濟(jì)的發(fā)展和人民收入水平的提高,股票已經(jīng)成為人們投資理財?shù)囊环N重要工具。我國的證券市場目前還處于發(fā)展的初始階段,其波動性和風(fēng)險性都遠(yuǎn)遠(yuǎn)高于國外的成熟市場,因此準(zhǔn)確地預(yù)測股價對于投資決策具有十分重要的指導(dǎo)意義。本文在灰色預(yù)測模型研究的基礎(chǔ)上,將結(jié)合支持向量機(jī)的理論與方法構(gòu)建組合模型對股票價格進(jìn)行短期預(yù)測建模研究。主要內(nèi)容如下： 第一章主要介紹股票預(yù)測方法,概述了證券投資分析方法、數(shù)理統(tǒng)計方法、現(xiàn)代技術(shù)分析方法以及灰色系統(tǒng)理論等方法。 第二章首先論述了中國股市不符合隨機(jī)游走模型,股票價格的波動存在規(guī)律性,然后說明中國股市并沒有達(dá)到弱式有效,從而說明中國股市在一定程度上是可以預(yù)測的。 第三章首先介紹了灰色系統(tǒng)理論,接著針對GM(1,1)模型的模擬序列未能較好的反映出原始數(shù)據(jù)序列的光滑比和級比動態(tài)變化的問題,提出了基于光滑比和級比序列的GM(1,1)組合預(yù)測模型,并通過實證表明模型的有效性。然后針對股市存在漲跌停盤或短期節(jié)假日的情況,嘗試將非等間距GM(1,1)模型運(yùn)用到預(yù)測中,提出了通過累加法將灰導(dǎo)數(shù)優(yōu)化和背景值優(yōu)化進(jìn)行組合,再采用逐步迭代來估計模型參數(shù)的新方法,實證表明該方法得到的模擬和預(yù)測值具有較高的精度。最后分析了經(jīng)典GM(1,N)模型的建模機(jī)理,并闡述了經(jīng)典GM(1,N)模型存在三個方面不足,并針對各個不足進(jìn)行了相應(yīng)改進(jìn),并提出了改進(jìn)后的GM(1,N)模型,通過實證分析表明模型的有效性,能夠運(yùn)用到股價的短期預(yù)測中去。 第四章將支持向量機(jī)解決小樣本、非線性及高維模式識別的優(yōu)勢與灰色預(yù)測模型“小樣本、貧信息”的特點(diǎn)相結(jié)合進(jìn)行組合建模以及通過累加生成挖掘原始數(shù)據(jù)序列中潛藏的內(nèi)在規(guī)律的特征相結(jié)合,提出了基于SVM的GM(1,1)模型的股價預(yù)測方法和基于SVM的GM(1,N)模型的股價預(yù)測方法,并且根據(jù)灰色理論中光滑比和級比的定義,提出了基于SVM的級比非線性灰色模型和基于SVM的灰色光滑比和級比預(yù)測模型,通過實證表明兩者結(jié)合能夠很好的運(yùn)用到股價預(yù)測中,并且精度較高。 第五章介紹了本文的主要研究內(nèi)容、研究成果和創(chuàng)新點(diǎn),并對未來的研究工作進(jìn)行了展望。
[Abstract]:With the development of social economy and the improvement of people's income level, stock has become an important tool for people's investment and financial management. The stock market of our country is still in the initial stage of development, its volatility and risk are far higher than the mature markets abroad, so it is very important to predict the stock price accurately for the investment decision. In this paper, based on the research of grey forecasting model, combined with the theory and method of support vector machine (SVM), a combination model is constructed to study the short-term forecasting model of stock price. The main contents are as follows: The first chapter mainly introduces the stock forecasting method, summarizes the stock investment analysis method, the mathematical statistics method, the modern technical analysis method and the grey system theory and so on. The second chapter first discusses that the Chinese stock market does not conform to the random walk model, and the fluctuation of stock price is regular, and then shows that the Chinese stock market does not achieve weak efficiency, which shows that the Chinese stock market can be predicted to a certain extent. In the third chapter, the grey system theory is introduced first, and then the simulation sequence of the GM-1) model can not reflect the smooth ratio and the dynamic change of the order ratio of the original data sequence. A combined prediction model based on smooth ratio and order ratio sequence is proposed, and the validity of the model is demonstrated by empirical results. Then, aiming at the situation of stock market with fluctuation limit or short-term holiday, this paper tries to apply the non-equal-spacing GMM1Q1) model to the prediction, and puts forward the combination of grey derivative optimization and background value optimization by the accumulative method. A new method of estimating the model parameters by step-by-step iteration is adopted. It is proved that the simulation and prediction values obtained by this method have high accuracy. At last, the paper analyzes the modeling mechanism of the classical GM1N) model, and expounds the shortcomings of the classical GM1N) model in three aspects, and makes corresponding improvements to each deficiency, and puts forward the improved GM1N) model. The validity of the model is proved by the empirical analysis. Can be applied to the stock price forecast in the short term. In chapter 4, support vector machine is used to solve the advantages of small sample, nonlinear and high dimensional pattern recognition and grey prediction model. The characteristics of "poor information" are combined to combine the characteristics of the combination modeling and the cumulative generation of the inherent laws hidden in the mining original data sequence, In this paper, the stock price forecasting method based on SVM model and SVM model is put forward, and the definition of smooth ratio and grade ratio in grey theory is given. The nonlinear grey model based on SVM and the prediction model of grey smooth ratio and grade ratio based on SVM are proposed. The empirical results show that the combination of the two models can be applied to the stock price forecasting well, and the accuracy is high. The fifth chapter introduces the main research contents, research results and innovation points, and prospects for future research work.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F832.51

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