本文關(guān)鍵詞： 小波分析 奇異點(diǎn) 周期性 移動(dòng)平均法 小波方差及相關(guān)系數(shù)　出處：《西南財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：股市是一個(gè)“高風(fēng)險(xiǎn),高收益”的市場(chǎng)。如何對(duì)股市進(jìn)行分析是股市存在以來眾多學(xué)者力圖解決的一大難題。傳統(tǒng)的股市分析方法有移動(dòng)平均線法、灰色系統(tǒng)法、神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)法等等,它們?cè)诠墒蟹治鰬?yīng)用中起到了非常重要的作用。但隨著股市數(shù)據(jù)量日益龐大,噪聲更加繁多,變化越為頻繁,這些傳統(tǒng)分析方法對(duì)股市這樣的非平穩(wěn)數(shù)據(jù)進(jìn)行處理有些“力不從心”。尤其是我國(guó)股市,作為一個(gè)典型的龐大但不成熟的市場(chǎng),劇烈頻繁的波動(dòng)幾乎是它的常態(tài)。 傅里葉分析和統(tǒng)計(jì)相結(jié)合是股市研究的重要方法之一,但因?yàn)楦道锶~不具備“空間局部性”,因此也束縛了它在股市中的應(yīng)用。在傅里葉基礎(chǔ)上,“小波變換”被提出�；谛〔己玫摹白赃m應(yīng)性”和“變焦性”等特性,小波變換非常適合對(duì)股市這樣的“非平穩(wěn)數(shù)據(jù)”進(jìn)行分析。本文從四個(gè)方面對(duì)小波變換在股市中的應(yīng)用進(jìn)行研究,并以2005年之后上海證券交易所上證綜指和深圳證券交易所深證成指的每日收盤價(jià)為對(duì)象,進(jìn)行實(shí)證研究,主要包括： (1)首先我們對(duì)上證綜指數(shù)據(jù)進(jìn)行預(yù)處理,然后利用MATLAB軟件的小波工具箱,對(duì)其進(jìn)行小波分解。眾所周知,股市的波動(dòng)一定原因是由噪聲(即突變因素)造成的,因此對(duì)每層的高頻分解圖進(jìn)行觀察分析,可得出股市的奇異點(diǎn),并結(jié)合李氏指數(shù),對(duì)奇異點(diǎn)的突變程度進(jìn)行分析。 (2)小波變換利用其分形的特性,可以將股市中的噪聲信號(hào)進(jìn)行剔除,從而使得股市的大趨勢(shì)更加突出。通過對(duì)深證成指的每日收盤價(jià)進(jìn)行小波分解和重構(gòu),得出去噪后的股市圖。通過周期性分析,我們力圖找出股市變動(dòng)的規(guī)律性。 (3)由上知,移動(dòng)平均法是主要的線性分析方法之一。但由于它具有時(shí)滯的缺陷,使得其得出的股市分析結(jié)果有些誤差�；诖�,我們通過研究發(fā)現(xiàn),用小波變換后的低頻數(shù)據(jù),取代短期移動(dòng)平均線,可有效地解決“時(shí)滯”的問題,因此本文以2005年之后招商銀行的每日收盤價(jià)為研究對(duì)象,采用改進(jìn)的移動(dòng)平均法,進(jìn)行了實(shí)證研究。 (4)上證綜指和深證成指作為我國(guó)股市的兩大重要指標(biāo),對(duì)它們之間的關(guān)聯(lián)性進(jìn)行研究,是近幾年股市分析的主要方向之一。方差和相關(guān)系數(shù)是相關(guān)性研究的兩個(gè)主要指標(biāo)。本文結(jié)合小波變換,分別計(jì)算上證和深證數(shù)據(jù)的小波方差,及兩者之間的相關(guān)系數(shù),并對(duì)這兩個(gè)指標(biāo)進(jìn)行分析,從而得出兩市之間的關(guān)聯(lián)性。
[Abstract]:The stock market is a "high risk, high yield" market. How to analyze the stock market is a big problem that many scholars have tried to solve since the stock market existed. The traditional stock market analysis method has the moving average method. Grey system method, neural network prediction method and so on, they play a very important role in the application of stock market analysis. These traditional analysis methods to deal with the non-stationary data such as the stock market is somewhat "beyond our means", especially the stock market of our country, as a typical large but immature market. Violent and frequent fluctuations are almost the norm. The combination of Fourier analysis and statistics is one of the important methods of stock market research, but because Fourier does not have "spatial localization", it also restricts its application in stock market. "Wavelet transform" is proposed, based on the good "adaptive" and "zoom" characteristics of wavelet. Wavelet transform is very suitable for the analysis of "non-stationary data" such as stock market. This paper studies the application of wavelet transform in stock market from four aspects. Taking the daily closing price of Shanghai Composite Index and Shenzhen Stock Exchange Composite Index after 2005 as the object, the empirical research is carried out, including: First, we preprocess the data of Shanghai Composite Index, then use the wavelet toolbox of MATLAB software to decompose it. The fluctuation of stock market is caused by noise (that is, sudden change factor), so the singularity of stock market can be obtained by observing and analyzing the high-frequency decomposition diagram of each layer, and combining with Li's index. The mutation degree of singularity is analyzed. Wavelet transform can eliminate the noise signal in stock market by using its fractal characteristic. In order to make the general trend of the stock market more prominent. Through wavelet decomposition and reconstruction of the daily closing price of Shenzhen Stock Exchange Index, the stock market map after noise is obtained, and the periodic analysis is carried out. We tried to find out the regularity of stock market movements. The moving average method is one of the main linear analysis methods, but because of its time delay, there are some errors in the results of stock market analysis. Based on this, we find out through the research. The problem of "delay" can be effectively solved by replacing the short-term moving average with the low-frequency data after wavelet transform. Therefore, this paper takes the daily closing price of China Merchants Bank after 2005 as the research object. An empirical study was carried out by using the improved moving average method. As two important indexes of China's stock market, the Shanghai Composite Index and the Shenzhen Composite Index are studied on the relationship between them. Variance and correlation coefficient are two main indicators of correlation research. In this paper, wavelet variance of Shanghai Stock Exchange and Shenzhen Stock Exchange data are calculated with wavelet transform. And the correlation coefficient between the two, and the analysis of the two indicators, so as to obtain the correlation between the two cities.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張潛,高立群;基于小波分析理論的證券投資市場(chǎng)預(yù)測(cè)[J];東北大學(xué)學(xué)報(bào);2002年06期

2 侯木舟,袁修貴;基于MATLAB的小波分析在股市技術(shù)分析中的應(yīng)用[J];系統(tǒng)工程;2001年05期

3 譚屹然;石柱鮮;趙紅強(qiáng);;小波分析模型在經(jīng)濟(jì)領(lǐng)域中的應(yīng)用[J];工業(yè)技術(shù)經(jīng)濟(jì);2010年12期

4 姚洪興,盛昭瀚;股市預(yù)測(cè)中的小波神經(jīng)網(wǎng)絡(luò)方法的研究[J];管理工程學(xué)報(bào);2002年02期

5 薛全會(huì);程秀芳;姚桂艷;孫麗媛;;小波分析的應(yīng)用現(xiàn)狀與前景[J];河北理工學(xué)院學(xué)報(bào);2006年01期

6 袁修貴,李英;小波分析在經(jīng)濟(jì)預(yù)測(cè)模型中的應(yīng)用[J];經(jīng)濟(jì)數(shù)學(xué);2004年03期

7 孟衛(wèi)東,嚴(yán)太華,丁首營(yíng);上證指數(shù)奇異信號(hào)統(tǒng)計(jì)檢測(cè)的小波分析[J];經(jīng)濟(jì)問題;2001年02期

8 周明磊;非參數(shù)估計(jì)與小波分析在股市趨勢(shì)線中的應(yīng)用[J];數(shù)理統(tǒng)計(jì)與管理;2005年04期

9 薛超;李星野;雷蕾;;滬深港股市相關(guān)性的小波分析[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2008年16期

10 楊凌;;上海股票市場(chǎng)的基于小波去噪的混沌性檢驗(yàn)[J];統(tǒng)計(jì)與信息論壇;2006年03期

，

本文編號(hào)：1446440