本文關(guān)鍵詞： 滬深300指數(shù) R/S分析 BP神經(jīng)網(wǎng)絡(luò) 預(yù)測(cè)　出處：《大連理工大學(xué)》2013年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：眾所周知,股票市場(chǎng)在國(guó)民經(jīng)濟(jì)中的地位是極其重要的。中國(guó)股市到底遵循什么樣的規(guī)律呢?是線性的還是非線性的,是隨機(jī)游走模型抑或非高斯分布的無(wú)記憶模型還是一個(gè)分形結(jié)構(gòu)呢?只有把這些問(wèn)題徹底搞明白了,才能更好的分析和預(yù)測(cè)市場(chǎng)、才能抓住市場(chǎng)的規(guī)律,才能更好的利用我國(guó)的資本市場(chǎng)。 中國(guó)的股票市場(chǎng)包含了2000多家上市公司,以及各種板塊、指數(shù)等,對(duì)每一個(gè)都進(jìn)行分析是很難實(shí)現(xiàn)的。在實(shí)際的分析與研究中,一般會(huì)選擇上證指數(shù)和深證成指作為代表進(jìn)行分析,因?yàn)樯献C指數(shù)反映了上海證券交易所的整體走勢(shì)、深證成指反映了深圳證券交易所的整體走勢(shì)。本文將滬深300指數(shù)和上證指數(shù)、深證成指分別疊加分析,發(fā)現(xiàn)滬深300指數(shù)和上證指數(shù)、深證成指的走勢(shì)基本一致,可以說(shuō)滬深300指數(shù)的走勢(shì)反映了中國(guó)股票市場(chǎng)的整體走勢(shì),因此選用了滬深300指數(shù)來(lái)分析中國(guó)的股票市場(chǎng)。 在分析中國(guó)的股市是不是隨機(jī)游走時(shí)采用的是重標(biāo)極差的分析方法,根據(jù)滬深300指數(shù)的Hurst指數(shù)和0.5的大小比較來(lái)進(jìn)行確定。通過(guò)比較發(fā)現(xiàn)滬深300指數(shù)的日線、周線和月線的Hurst指數(shù)均大于0.6,從而說(shuō)明中國(guó)的股市不是隨機(jī)游走的,而是有記憶性的非線性結(jié)構(gòu),并且是一個(gè)分形結(jié)構(gòu)。從而也說(shuō)明中國(guó)股市的有效性不強(qiáng)。并根據(jù)重標(biāo)極差分析可確定論文第四章較為合理的預(yù)測(cè)區(qū)間。 在預(yù)測(cè)滬深300指數(shù)的收盤(pán)價(jià)時(shí),本文是結(jié)合技術(shù)分析,應(yīng)用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè),作為基本模型。在基本模型的基礎(chǔ)上,將預(yù)測(cè)結(jié)果和EMA均線進(jìn)行線性加權(quán)得到改進(jìn)模型。這兩個(gè)模型都可以跑贏大盤(pán),改進(jìn)模型精度更高。對(duì)411天數(shù)據(jù)的預(yù)測(cè)中,最終實(shí)現(xiàn)正數(shù)中最大偏離誤差為4.69%,負(fù)數(shù)中最大偏離誤差為-3.73%,預(yù)測(cè)值偏離實(shí)際值的偏差的絕對(duì)值的平均值為1.13%。將前20天的預(yù)測(cè)結(jié)果和后20天的預(yù)測(cè)結(jié)果進(jìn)行比較,發(fā)現(xiàn)對(duì)時(shí)間序列的學(xué)習(xí)樣本進(jìn)行及時(shí)的數(shù)據(jù)更新有利于提高預(yù)測(cè)精度。 本文最終得出三大結(jié)論：一,基于BP神經(jīng)網(wǎng)絡(luò)對(duì)滬深300指數(shù)的預(yù)測(cè)在一定程度上是可行的,可以跑贏大盤(pán)；二,選擇合適的預(yù)測(cè)區(qū)間是很重要的；三,中國(guó)的股票市場(chǎng)是一個(gè)分形結(jié)構(gòu),其有效性不強(qiáng)。事件驅(qū)動(dòng)型明顯,將新聞、消息具體量化并加入到影響因素中是很重要的,通過(guò)具體數(shù)據(jù)成功預(yù)測(cè)股市還有很長(zhǎng)的道路要走。
[Abstract]:As we all know, the position of the stock market in the national economy is extremely important. What kind of law does the Chinese stock market follow? Is it linear or nonlinear? is it a random walk model or a memoryless model of non-#china_person0# distribution or a fractal structure? Only by thoroughly understanding these problems can we better analyze and predict the market, grasp the laws of the market, and make better use of the capital market of our country. China's stock market contains more than 2000 listed companies, as well as a variety of plates, indices, and so on, each of which is difficult to achieve. In the actual analysis and research. Generally will choose the Shanghai Stock Exchange Index and Shenzhen Stock Exchange as the representative of the analysis, because the Shanghai index reflects the overall trend of the Shanghai Stock Exchange. The Shenzhen Composite Index reflects the overall trend of the Shenzhen Stock Exchange. This paper analyzes the CSI 300 index and the Shanghai Stock Exchange index respectively and finds the CSI 300 Index and the Shanghai Stock Exchange Index respectively. The Shenzhen Composite Index is basically in line with the trend. It can be said that the trend of the CSI 300 index reflects the overall trend of the Chinese stock market, so the CSI 300 Index has been chosen to analyze the Chinese stock market. In the analysis of whether the Chinese stock market is a random walk, the method of rescaling extreme difference is used. According to the Hurst index of Shanghai and Shenzhen 300 index and the size of 0. 5 to determine, through the comparison found that the daily line of the Shanghai and Shenzhen 300 index, the Hurst index of the week line and monthly line are all greater than 0. 6. It shows that China's stock market is not random walk, but a memory of the nonlinear structure. And it is a fractal structure, which also shows that the validity of Chinese stock market is not strong. According to the rescaled range analysis, we can determine the more reasonable prediction interval in Chapter 4th of this paper. In predicting the closing price of CSI 300 index, this paper uses BP neural network to predict the closing price of CSI 300 index, which is based on the basic model. The prediction results and the EMA mean line are weighted linearly to get the improved model. Both models can outperform the market, and the improved model has higher precision. The maximum deviation error of positive number and negative number is 4.69% and -3.73% respectively. The average absolute value of the deviation from the actual value is 1.13.The results of the first 20 days are compared with those of the next 20 days. It is found that timely updating of time series learning samples is helpful to improve prediction accuracy. This paper finally draws three conclusions: first, the prediction of Shanghai and Shenzhen 300 index based on BP neural network is feasible to some extent and can outperform the market; Secondly, it is very important to choose the appropriate prediction interval. Third, China's stock market is a fractal structure, its effectiveness is not strong, event-driven obviously, it is very important to quantify and add news and information to the influence factors. There is still a long way to go to successfully predict the stock market through specific data.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F832.51;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 楊成義;王大鵬;劉澄;;基于R/S分析的中國(guó)股市分形結(jié)構(gòu)的實(shí)證研究[J];北京科技大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2009年01期

2 吳微,陳維強(qiáng),劉波;用BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)股票市場(chǎng)漲跌[J];大連理工大學(xué)學(xué)報(bào);2001年01期

3 于寧莉;易東云;劉海波;周潤(rùn);;基于股票市場(chǎng)交易數(shù)據(jù)流的場(chǎng)景記憶模型[J];系統(tǒng)工程;2006年10期

4 王志剛;曾勇;李平;;中國(guó)股票市場(chǎng)技術(shù)分析非線性預(yù)測(cè)能力的實(shí)證檢驗(yàn)[J];管理工程學(xué)報(bào);2009年01期

5 魏希柱;任月明;沈毅;袁一星;;基于最大Lyapunov指數(shù)改進(jìn)算法的水量預(yù)測(cè)與控制[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2008年01期

6 梁夏;具有自糾錯(cuò)功能的人工神經(jīng)網(wǎng)絡(luò)在股票滾動(dòng)預(yù)測(cè)上的應(yīng)用[J];計(jì)算機(jī)應(yīng)用研究;1999年01期

7 尹紹飛;蘇勇;;BP神經(jīng)網(wǎng)絡(luò)在股指預(yù)測(cè)中的應(yīng)用[J];科學(xué)技術(shù)與工程;2009年24期

8 史永東;中國(guó)證券市場(chǎng)股票收益持久性的經(jīng)驗(yàn)分析[J];世界經(jīng)濟(jì);2000年11期

9 劉海s，

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