本文選題：股票板塊　切入點(diǎn)：相關(guān)性　出處：《重慶大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：目前，人們的金融意識(shí)的日益增強(qiáng)，引起越來(lái)越多的投資者對(duì)股票投資的青睞。因此，對(duì)股票市場(chǎng)的分析和預(yù)測(cè)的研究就越有其必要性，研究者們也一直致力于利用各種方法對(duì)某支股票、某股票指數(shù)或不同板塊股票的走勢(shì)的預(yù)測(cè)研究。雖然股票的波動(dòng)可能為投資者們帶來(lái)不確定的收益，可由于其影響的因素較多，使得投資者們難以并且也不可能完全掌握股票確切的漲跌規(guī)律。但若能估計(jì)股票的基本漲跌情況，一定程度上也可以給投資者一些建議。對(duì)板塊內(nèi)的所有股票數(shù)據(jù)間的研究，有利于在了解板塊整體的漲跌情況以及板塊內(nèi)是否存有漲勢(shì)情況異于其他的股票后，為投資者對(duì)該板塊的后期發(fā)展的分析提供一些參考信息。因此，有關(guān)估計(jì)板塊股票漲跌趨勢(shì)的研究具有一定的價(jià)值。 投資者一直都希望能夠掌握變化莫測(cè)的股市的漲跌規(guī)律，從而出現(xiàn)了很多技術(shù)分析方法以及股票預(yù)測(cè)方法，其中技術(shù)分析方法包含主觀成分較多，不同的研究者的結(jié)論可能會(huì)有一定的出入。股票預(yù)測(cè)方法主要有數(shù)學(xué)模型和無(wú)模型兩類。傳統(tǒng)的關(guān)于股票數(shù)據(jù)分析預(yù)測(cè)的研究，主要是側(cè)重對(duì)單支股票某些股票的研究，通過(guò)指定的模型對(duì)數(shù)據(jù)做實(shí)證分析。在建立數(shù)學(xué)模型時(shí)，主要選取適當(dāng)?shù)淖兞�，以保證做實(shí)證分析預(yù)測(cè)時(shí)有一定的可靠性和準(zhǔn)確性，但是建立數(shù)學(xué)模型時(shí)要檢驗(yàn)各個(gè)變量的顯著性以及考慮變量的合適性，從而計(jì)算量比較大，并不具有直觀性。 本文的研究?jī)?nèi)容是結(jié)合圖像處理方法對(duì)股票數(shù)據(jù)進(jìn)行分析。首先將收集到的某板塊股票數(shù)據(jù)做歸一化處理，，再形成灰度圖，圖像的豎直方向表示板塊中的不同股票，而水平方向表示處理后的股票不同日期的收盤價(jià)格數(shù)據(jù)。由于各支股票間的相關(guān)性強(qiáng)弱將影響圖像在豎直方向上的連貫性和光滑性，因此需對(duì)該板塊內(nèi)的股票重新排列。投資者主要關(guān)注股票的大致走勢(shì)，忽略股票間那些小的波動(dòng)，這些小的波動(dòng)可視為噪聲。在分析由股票板塊數(shù)據(jù)形成的灰度圖時(shí)，噪聲將影響圖像的清晰度。文章中結(jié)合股票數(shù)據(jù)圖像的特點(diǎn)，將均值濾波法、中值濾波法和自適應(yīng)維納（Wiener）濾波法分別對(duì)圖像進(jìn)行豎直方向上去噪。將實(shí)驗(yàn)結(jié)果進(jìn)行對(duì)比，同時(shí)結(jié)合處理后的股票數(shù)據(jù)圖像對(duì)該板塊的漲勢(shì)進(jìn)行了分析，進(jìn)而能得出該板塊是否具有明顯的板塊效應(yīng)。最后，對(duì)去噪后的圖像分別進(jìn)行水平方向和豎直方向的邊緣提取。提取的豎直方向邊緣主要是反映了該股票板塊在某時(shí)刻的漲跌幅度的極大值或極小值；而水平方向的邊緣主要反映了該板塊內(nèi)某支股票漲勢(shì)的奇異性。提取邊緣所用的方法主要是利用小波變換，找到數(shù)據(jù)中的極值，從而找到對(duì)應(yīng)方向上的邊緣。結(jié)合這些邊緣所處的位置間隔分析該板塊的大致漲跌周期。與既往的方法相比，本文從二維的角度來(lái)分析股票數(shù)據(jù)且能夠較直觀的反映該板塊股票的整體漲跌趨勢(shì)。
[Abstract]:At present, with the increasing of people's financial consciousness, more and more investors prefer to invest in stocks. Therefore, it is necessary to study the analysis and prediction of stock market. Researchers have also been using a variety of methods to predict the movements of a particular stock, a stock index, or a different sector, although volatility in stocks can bring uncertain returns to investors. However, because of its many factors, it makes it difficult and impossible for investors to fully grasp the exact rise and fall rules of the stock. But if we can estimate the basic rise and fall of the stock, To a certain extent, some suggestions can also be given to investors. The study of all the stock data in the plate is helpful in understanding the overall rise and fall of the plate and whether there is a rise in the plate that is different from that of other stocks. This paper provides some reference information for investors to analyze the late development of the plate. Therefore, the research on estimating the trend of stock price rise and fall of the plate is of certain value. Investors have always hoped to be able to master the fluctuating rules of the stock market. As a result, there have been many technical analysis methods and stock forecasting methods, among which the technical analysis methods contain more subjective elements. The conclusions of different researchers may be different. There are two main methods of stock prediction: mathematical model and no model. The traditional research on stock data analysis and prediction mainly focuses on the research of some stocks in a single stock. When establishing the mathematical model, the appropriate variables are selected to ensure the reliability and accuracy of the empirical analysis and prediction. But it is necessary to test the significance of each variable and the appropriateness of considering the variable when establishing the mathematical model so that the calculation is large and not intuitive. The research content of this paper is to analyze the stock data with image processing method. Firstly, the stock data collected from a certain plate is normalized, and then a gray map is formed, and the vertical direction of the image represents different stocks in the block. The horizontal direction represents the closing price data of the processed stocks on different dates. Because the correlation between the stocks will affect the coherence and smoothness of the image in the vertical direction, Investors focus on the general trend of stocks, ignoring the small fluctuations between stocks, which can be regarded as noise. When analyzing grayscale maps formed by stock plate data, The noise will affect the sharpness of the image. In this paper, mean filter, median filter and adaptive Wiener filter are used to de-noise the image in vertical direction according to the characteristics of stock data image, and the experimental results are compared. At the same time, combined with the processed stock data image, we analyze the rise of the plate, and then we can find out whether the plate has obvious plate effect. Finally, The edge of the image after denoising is extracted in horizontal direction and vertical direction respectively. The extracted vertical edge mainly reflects the maximum or minimum value of the stock plate's rise and fall at a certain time. The edge of the horizontal direction mainly reflects the singularity of a stock rise in the plate. The method to extract the edge is to use wavelet transform to find the extreme value in the data. In order to find the corresponding edge in the direction. Combined with the position interval of these edges to analyze the roughly rising and falling cycles of the plate. Compared with the previous methods, This paper analyzes the stock data from a two-dimensional perspective and can directly reflect the overall trend of the stock market.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F830.91

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