本文關鍵詞： 股票 函數(shù)性數(shù)據(jù) 數(shù)據(jù)擬合 聚類　出處：《哈爾濱工業(yè)大學》2012年碩士論文　論文類型：學位論文

【摘要】：隨著中國股票市場的迅速發(fā)展，市場的規(guī)范化程度不斷提高，股票品種也有了向多元多層次化發(fā)展的趨勢，吸引了越來越多投資者的目光。為減少投資風險，獲得豐厚的利潤回報，理智的股票投資者將會更加重視投資對象的選擇。表達股票數(shù)據(jù)的真實意義對投資者來說是關鍵，而股票交易數(shù)據(jù)包含了大量的信息，對股票交易數(shù)據(jù)的分析就顯得特別重要。 股票交易數(shù)據(jù)的表現(xiàn)受很多因素的影響，包含信息量較大，總體上體現(xiàn)出函數(shù)性特征，采用傳統(tǒng)的時間序列數(shù)據(jù)分析方法受到很多局限。為此，根據(jù)股票交易數(shù)據(jù)的函數(shù)性特征，借助函數(shù)性數(shù)據(jù)分析方法，對股票交易數(shù)據(jù)進行了有針對性的分析。主要內容是基于股票交易數(shù)據(jù)的函數(shù)性特征，對股票交易數(shù)據(jù)進行預處理和曲線擬合，，使得原始數(shù)據(jù)“抽象化”，進而得到統(tǒng)一的函數(shù)系數(shù)矩陣，再借助系數(shù)矩陣對反映股票特性的函數(shù)進行聚類，得出相應的股票聚類結果，并對結果進行了合理解釋。 將函數(shù)性數(shù)據(jù)分析方法應用于分析研究函數(shù)性數(shù)據(jù)中，改善了傳統(tǒng)分析方法對數(shù)據(jù)要求的約束，這樣不僅增加了可分析數(shù)據(jù)的范圍，而且擴大了函數(shù)性數(shù)據(jù)分析方法的應用領域。將該方法實際應用于現(xiàn)實股票交易數(shù)據(jù)的擬合和聚類分析中，得到了非常理想的結果，表明了方法的有效性，該方法也能夠為投資者提供很好的決策依據(jù)。
[Abstract]:With the rapid development of China's stock market, the standardization of the market is constantly improving, and the stock varieties have the trend of multi-level development, attracting the attention of more and more investors in order to reduce the investment risk. In order to get a good profit return, rational stock investors will pay more attention to the choice of investment object. Expressing the true meaning of stock data is key to investors, and stock trading data contains a lot of information. The analysis of stock trading data is particularly important. The performance of stock trading data is affected by many factors, including a large amount of information, which generally reflects the functional characteristics, and the traditional time series data analysis method is limited. According to the functional characteristics of the stock trading data and with the help of the functional data analysis method, the paper makes a targeted analysis of the stock trading data. The main content is based on the functional characteristics of the stock trading data. The preprocessing and curve fitting of the stock trading data make the original data "abstract", and then get the unified function coefficient matrix, and then cluster the functions reflecting the stock characteristics with the help of the coefficient matrix. The corresponding stock clustering results are obtained, and the results are explained reasonably. The functional data analysis method is applied to analyze and study the functional data, which improves the constraint of the traditional analysis method on the data requirement, which not only increases the scope of analytical data. Moreover, the application field of the functional data analysis method is expanded. The method is applied to the real stock trading data fitting and clustering analysis, and the very ideal result is obtained, which shows the validity of the method. This method can also provide investors with a good basis for decision-making.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.51;F224

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