本文選題：金融預(yù)測(cè)　切入點(diǎn)：統(tǒng)計(jì)回歸　出處：《合肥工業(yè)大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：我國(guó)的股票市場(chǎng)還處在成長(zhǎng)階段,因此有著其固有的特殊性。由于股市的不規(guī)范所帶來(lái)的各種虛假信息,更是讓投資者對(duì)股票價(jià)格的走向難以進(jìn)行判斷,從而損失許多利益。因此在金融市場(chǎng)中,特別是股票市場(chǎng)中,通過(guò)數(shù)據(jù)的分析及處理來(lái)探討其內(nèi)在的運(yùn)行規(guī)律刻不容緩。而股票價(jià)格波動(dòng)的背后定然存在著一些潛在的必然規(guī)律,且這些規(guī)律來(lái)調(diào)配股票的價(jià)格。因此,問(wèn)題的焦點(diǎn)就集中在如何去尋找這些潛在的規(guī)律,這才是目前需要進(jìn)行進(jìn)一步去研究和探討的重點(diǎn)。近些年,基于數(shù)據(jù)處理和分析的預(yù)測(cè)理論在金融市場(chǎng)發(fā)揮的作用越發(fā)明顯,通過(guò)本文的具體研究,可以有效地運(yùn)用數(shù)學(xué)模型將信息進(jìn)行數(shù)學(xué)化的描述和分析,體現(xiàn)數(shù)據(jù)的變化趨勢(shì),挖掘潛在的固有規(guī)律等重要信息,從而為管理者以及投資者提供可靠的依據(jù)。 本文運(yùn)用數(shù)值分析理論、統(tǒng)計(jì)回歸理論、智能優(yōu)化理論等來(lái)解決金融領(lǐng)域的預(yù)測(cè)問(wèn)題。從形式上說(shuō)是給出了解決金融領(lǐng)域的問(wèn)題的三類方法體系,從本質(zhì)上理解,則是選取三個(gè)不同視角為切入點(diǎn)解決金融領(lǐng)域的預(yù)測(cè)。即數(shù)值分析理論主要解決預(yù)測(cè)過(guò)程中的計(jì)算過(guò)程的復(fù)雜性,統(tǒng)計(jì)回歸理論聚焦消除預(yù)測(cè)過(guò)程中的影響變量的多重性,智能優(yōu)化理論重點(diǎn)針對(duì)預(yù)測(cè)過(guò)程中的模型參數(shù)的優(yōu)化性。在不同的金融數(shù)據(jù)類型下,采用不同的模型體系,只有“因人而異”才可以起到“藥到病除”的效果。在具體的研究中,本文在簡(jiǎn)要地分析數(shù)據(jù)分析理論對(duì)促進(jìn)科學(xué)預(yù)測(cè)的重要性,闡述科學(xué)預(yù)測(cè)在金融業(yè),特別是股票市場(chǎng)中的重要性和必要性,以及論述金融預(yù)測(cè)模型的研究的國(guó)內(nèi)外現(xiàn)狀及其存在的問(wèn)題的基礎(chǔ)上,把灰色預(yù)測(cè)模型,偏最小二乘回歸預(yù)測(cè)模型,時(shí)間序列預(yù)測(cè)模型和智能優(yōu)化預(yù)測(cè)模型應(yīng)用到金融領(lǐng)域的實(shí)踐中去。 本文的具體研究?jī)?nèi)容和創(chuàng)新性工作如下： 一、在已有的傳統(tǒng)灰色模型基礎(chǔ)上,提出利用強(qiáng)化和弱化緩沖算子對(duì)原始數(shù)據(jù)序列進(jìn)行數(shù)據(jù)進(jìn)行預(yù)處理的策略,從而得到一組較為平緩的數(shù)據(jù)序列用于GM(1,1)預(yù)測(cè)模型的輸入,然后分別利用組合插值和三次樣條插值對(duì)傳統(tǒng)GM(1,1)模型的背景值進(jìn)行改進(jìn),以獲得新的預(yù)測(cè)模型。最后利用本章的預(yù)測(cè)方法對(duì)上證指數(shù)日收益率進(jìn)行仿真實(shí)驗(yàn),結(jié)果表明,本章方法克服了受沖擊擾動(dòng)數(shù)據(jù)影響的問(wèn)題,并且具有更高的模擬和預(yù)測(cè)精度。 二、在William Sharpe提出的資本資產(chǎn)定價(jià)模型(CAPM)基礎(chǔ)上,提出在多因子情況下遇到多重共線性問(wèn)題時(shí),一種新的解決這種困難的方法,即偏最小二乘的二次多項(xiàng)式回歸方法。該方法不僅考慮每個(gè)因素對(duì)收益的影響,還可以考慮到影響因素之間的相互作用對(duì)收益的影響,從而更加全面的分析影響資產(chǎn)回報(bào)率的因素。另外,我們還把偏最小二乘支持回歸理論與支持向量回歸理論結(jié)合,解決中國(guó)股票市場(chǎng)的多影響因子的優(yōu)化問(wèn)題,克服各因子間的多重共線性的干擾,從而篩選出影響股票收益回報(bào)率的重要因素變量,為股市的技術(shù)分析提供一個(gè)可信的工具。 三、考慮到SVR的算法過(guò)程中的由于不敏感損失函數(shù)中的ε、懲罰因子C和徑向基函數(shù)中的σ2這三個(gè)參數(shù)取值的不同,則會(huì)導(dǎo)致支持向量回歸模型不同的。故而在支持向量回歸的理論基礎(chǔ)上,結(jié)合我國(guó)經(jīng)濟(jì)運(yùn)行的基本特點(diǎn),汲取支持向量回歸和群智能算法的優(yōu)點(diǎn),分別提出通過(guò)控制誤差ε的取值,對(duì)偏最小二乘支持向量回歸模型中參數(shù)集(C,σ2)采用帶有RBF核的遺傳算法進(jìn)行近似尋優(yōu),之后采用偏最小二乘支持向量回歸對(duì)上證綜指收益率進(jìn)行預(yù)測(cè),算法對(duì)存在高度的非線性、耦合性的金融數(shù)據(jù),有著良好的適應(yīng)性,從而確保了預(yù)測(cè)的精度。 四、針對(duì)金融數(shù)據(jù)的非線性和不確定等特性,借助模糊邏輯系統(tǒng),提出一種新的金融市場(chǎng)波動(dòng)率的預(yù)測(cè)方法-模糊FEGARCH模型,用來(lái)更好的應(yīng)對(duì)具有非線性特性的收益率數(shù)據(jù)進(jìn)行預(yù)測(cè)。其次,為了判斷分布型模型和不對(duì)稱型模型對(duì)預(yù)測(cè)精度的影響程度,分別采用分布型和不對(duì)稱型與模糊FEGARCH)的波動(dòng)模型進(jìn)行預(yù)測(cè)比較。另外,綜合智能算法和時(shí)間序列的優(yōu)點(diǎn)對(duì)股票波動(dòng)率進(jìn)行預(yù)測(cè),利用加權(quán)最小二乘支持向量回歸模型進(jìn)行初步預(yù)測(cè),然后利用EGARCH模型對(duì)加權(quán)最小二乘支持向量回歸的預(yù)測(cè)誤差后進(jìn)行修正,通過(guò)EGARCH模型來(lái)估計(jì)預(yù)測(cè)模型的擬合誤差及其分布規(guī)律,得到最終的上證綜指波動(dòng)率預(yù)測(cè)值。最后,對(duì)上述兩種方法的預(yù)測(cè)結(jié)果的采用的SPA檢驗(yàn)方法,該方法的優(yōu)點(diǎn)在于可以遍歷對(duì)比每個(gè)模型的所有損失函數(shù)(預(yù)測(cè)誤差),從而全面的比較模型的預(yù)測(cè)精度。
[Abstract]:China's stock market is still in the growth stage, so it has its inherent particularity. Because of all kinds of false information about the stock market is not standardized, more is to allow investors to the stock price is difficult to judge, to lose a lot of benefits. Therefore in the financial market, especially the stock market, through the analysis of data processing and to explore the internal rules and delay. The fluctuation of the stock price behind there are certainly some potential inevitable, and these rules to adjust the stock price. Therefore, the focus of the problem is focused on how to find the potential rules, this is the need to focus on further research and discussion. In recent years, based on the forecast theory of data processing and analysis of the play in the financial markets are becoming more and more important, through the research of this thesis, can effectively use the mathematical model The information is mathematically described and analyzed, reflecting the trend of data change and mining potential inherent laws and other important information, so as to provide reliable basis for managers and investors.
This paper uses numerical analysis theory, statistical regression theory, intelligent optimization theory to solve the prediction problem in the financial field. From the form that is given three kinds of methods to solve the financial problems in the field of system, understanding from the essence, it is from three different perspectives to solve the financial sector forecast as the starting point. The complexity of calculation the numerical analysis theory mainly solves the problem in the process of prediction, statistical regression theory predicts multiple variable focus elimination effect in process optimization, intelligent optimization theory focuses on the model parameters in the process of prediction. In the financial data of different types, using various model system, only the "It differs from man to man. can play" the "charm" effect. In the study, based on the brief analysis of data analysis theory to promote the importance of scientific prediction, expounds the scientific prediction in Finance Industry, especially the importance and necessity of the stock market in the foundation as well as the present situation and the existing problems of the model research of financial prediction discussed at home and abroad on the grey prediction model, partial least squares regression model, time series prediction model and intelligent optimization prediction model is applied to practice in the financial field.
The specific research content and innovative work of this paper are as follows:
A, the traditional grey model on the basis of the strengthening and weakening buffer operator of original data sequence of data pretreatment method, to obtain a set of relatively flat data sequence for GM (1,1) prediction model of the input, and then use the combination of interpolation and three spline interpolation on the traditional GM (1,1) improved the value model of the background, in order to obtain a new prediction model. Finally the prediction method of this chapter carries on the simulation experiment, the results show that the Shanghai Composite Index daily return rate, this chapter method overcomes the disturbance data affected by the impact, and has higher precision of prediction and simulation.
Two, the capital asset pricing model proposed by William Sharpe (CAPM) on the basis of this encounter the problem of multicollinearity in multi factor conditions, a new method of solving the problems, two polynomial regression method and partial least squares. This method not only influence each factor on income consideration, can also be considering the influence of the interaction between the influencing factors of income, thus a more comprehensive analysis of the impact of asset return factors. In addition, we also put the partial least squares regression and support vector regression theory of support theory combined with solving multi factor optimization problem China stock market, overcome the multicollinearity of the factors in order to find out the influence of variable interference, important factors of stock returns, providing a reliable tool for the analysis of the stock market.
Three, taking into account the algorithm in the process of SVR due to the insensitive loss function epsilon, penalty factor C and radial basis function in sigma 2 these three parameters will lead to different support vector regression model. Therefore based on the theory of support vector regression, combined with the basic characteristics of China's economy run, learn the advantages of support vector regression and swarm intelligence algorithm, are proposed through the values of control error epsilon, the partial least squares support vector regression model parameter sets (C, sigma 2) using genetic algorithm with RBF kernel approximation optimization, using partial least squares support vector regression on the returns of Shanghai composite index forecast after the algorithm of highly nonlinear, financial data coupling, has good adaptability, so as to ensure the accuracy of prediction.
Four, the financial data for the nonlinear and uncertain characteristics, by means of fuzzy logic system, put forward a kind of new financial market volatility rate prediction method of fuzzy FEGARCH model to yield data to better cope with the nonlinear prediction. Secondly, in order to judge the influence of distribution model and model of asymmetry the prediction accuracy, respectively by the distribution pattern and asymmetric type and fuzzy FEGARCH) of the wave model is compared. In addition, the comprehensive advantages of intelligent algorithms and the time series of stock volatility forecast, using weighted least squares support vector regression model to preliminary forecasts, then the prediction error of weighted least squares support vector regression after correction using EGARCH model to estimate the fitting error and prediction of the distribution of the model through the EGARCH model, the Shanghai Composite Index Fluctuation Finally, we use the SPA test method to predict the prediction results of the above two methods. The advantage of this method is that it can traverse all the loss functions of each model (prediction error), so as to comprehensively compare the prediction accuracy of the model.

【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;F224

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