本文選題：模糊聚類 + 灰色聚類��； 參考：《湖南工業(yè)大學(xué)》2012年碩士論文

【摘要】：我國(guó)證券市場(chǎng)經(jīng)過(guò)20多年的發(fā)展，不僅對(duì)市場(chǎng)經(jīng)濟(jì)建設(shè)起到重要的作用，而且對(duì)人們生活的影響也更加明顯。由于證券市場(chǎng)數(shù)據(jù)的龐大和信息的模糊性，依據(jù)基本分析和技術(shù)分析制定投資決策帶有很強(qiáng)的主觀性，影響投資判斷效果。模糊理論和灰色系統(tǒng)理論都是處理這種不精確、不確定信息的有效工具，因此，本文選取這兩種理論對(duì)研究股票市場(chǎng)中的模糊不確定性信息具有重要的實(shí)踐意義。 本文首先在借鑒國(guó)內(nèi)外已有研究成果的基礎(chǔ)上，重點(diǎn)介紹了相關(guān)理論知識(shí)。第三章首先融合夾角余弦法和歐氏距離法構(gòu)建“夾角余弦歐氏距離法”作為模糊聚類的相似度函數(shù)，并構(gòu)建相關(guān)模型，，然后選取證券期貨行業(yè)12家上市公司進(jìn)行模糊聚類實(shí)證分析。最后采用F統(tǒng)計(jì)量確定最佳分類，適用于不同投資風(fēng)格的投資者，使其根據(jù)自己投資特點(diǎn)科學(xué)選票，以提高投資效率。 灰色系統(tǒng)理論與模糊理論在描述和處理不確定性或者模糊性信息方面表現(xiàn)出一定的相似性。第四章首先在借鑒相關(guān)文獻(xiàn)資料的基礎(chǔ)上研究模糊聚類和灰色聚類的可結(jié)合性，并在兩種理論的基礎(chǔ)上提出區(qū)間灰數(shù)的灰色動(dòng)態(tài)聚類模型。該方法融合模糊聚類方法的思想，將指標(biāo)數(shù)由清晰值拓展到區(qū)間灰數(shù)，避免主觀賦值指標(biāo)權(quán)重，增加算法的客觀性，簡(jiǎn)化計(jì)算程序。本章采用區(qū)間灰數(shù)對(duì)12家證券上市公司進(jìn)行灰色聚類分析，使其更符合客觀現(xiàn)實(shí)，并提高了分類的精確度，為投資者科學(xué)選股獲取滿意的投資收益提供一定的指導(dǎo)和借鑒。
[Abstract]:After more than 20 years' development, China's securities market not only plays an important role in the construction of market economy, but also has a more obvious impact on people's life. Because of the huge data of securities market and the ambiguity of information, it is very subjective to make investment decision based on basic analysis and technical analysis, which affects the effect of investment judgment. Both fuzzy theory and grey system theory are effective tools to deal with this kind of imprecise and uncertain information. Therefore, the selection of these two theories is of great practical significance to the study of fuzzy uncertain information in stock market. First of all, based on the existing research results at home and abroad, this paper focuses on the relevant theoretical knowledge. In chapter 3, the method of angle cosine and Euclidean distance is used to construct the similarity function of fuzzy clustering, and the correlation model is constructed. Then 12 listed companies in the securities and futures industry are selected to carry out fuzzy clustering empirical analysis. Finally, F statistic is used to determine the best classification, which can be applied to investors with different investment styles, and make them vote scientifically according to their own investment characteristics in order to improve the investment efficiency. Grey system theory and fuzzy theory show certain similarity in describing and dealing with uncertain or fuzzy information. In the fourth chapter, the combination of fuzzy clustering and grey clustering is studied on the basis of relevant literature, and the grey dynamic clustering model of interval grey number is put forward on the basis of the two theories. This method integrates the idea of fuzzy clustering method, extends the index number from clear value to interval grey number, avoids subjective assignment of index weight, increases the objectivity of the algorithm and simplifies the calculation program. In this chapter, the grey clustering analysis of 12 listed securities companies is carried out by using interval grey number, which makes it more in line with the objective reality, and improves the accuracy of classification, and provides certain guidance and reference for investors to select stocks scientifically to obtain satisfactory investment returns.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.51;F224

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