發(fā)布時(shí)間：2018-02-06 00:01

  本文關(guān)鍵詞： 股票板塊 股票分類(lèi) 聚類(lèi)分析 蟻群算法　出處：《復(fù)旦大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著中國(guó)股票市場(chǎng)不斷發(fā)展,正確對(duì)股票進(jìn)行分類(lèi),以構(gòu)建投資組合降低投資風(fēng)險(xiǎn)的重要性也不斷提高。根據(jù)現(xiàn)代投資組合理論,通過(guò)構(gòu)建投資組合,可以起到分散非系統(tǒng)性風(fēng)險(xiǎn)的作用。投資組合的風(fēng)險(xiǎn)程度與組合內(nèi)各股票之間的相關(guān)性有關(guān),各股票之間的相關(guān)性越小,組合起到的風(fēng)險(xiǎn)分散效應(yīng)越明顯。現(xiàn)階段投資者常按照行業(yè)對(duì)股票進(jìn)行分類(lèi)。因此,如果同一行業(yè)內(nèi)的股票收益率之間的相關(guān)性高于不同行業(yè)的股票收益率間的相關(guān)性,不同行業(yè)間資產(chǎn)的搭配也應(yīng)該能起到更好的效果。 但是,本文通過(guò)實(shí)證研究證明了中國(guó)股市行業(yè)之間股票價(jià)格波動(dòng)具有很高的相關(guān)性,按行業(yè)分類(lèi)構(gòu)建投資組合以降低風(fēng)險(xiǎn)的效果較差。因此,提出一種行業(yè)之外的有效分類(lèi)股票的方法就顯得非常重要。 本文提出使用優(yōu)化的蟻群聚類(lèi)算法,對(duì)中國(guó)A股市場(chǎng)上所有的兩千多支股票進(jìn)行聚類(lèi)分析。分別采用財(cái)務(wù)指標(biāo)和個(gè)股收益率波動(dòng)對(duì)股票進(jìn)行聚類(lèi),通過(guò)對(duì)聚類(lèi)結(jié)果的分析驗(yàn)證了使用蟻群聚類(lèi)算法對(duì)大樣本量數(shù)據(jù)進(jìn)行聚類(lèi)分析的可行性和良好效果。為在中國(guó)市場(chǎng)進(jìn)行股票分類(lèi)提供了新的思路,為投資決策和風(fēng)險(xiǎn)控制提供了理論和數(shù)據(jù)基礎(chǔ)。 本文共分為五章：第一章為研究背景、文獻(xiàn)綜述及論文框架介紹；第二章介紹了中國(guó)股票市場(chǎng)行業(yè)分類(lèi)及其存在問(wèn)題：第三章提出了基于優(yōu)化的蟻群聚類(lèi)算法的股票分類(lèi)方法；第四章就第三章提出的方法進(jìn)行了實(shí)證研究；最后提出了結(jié)論和展望。
[Abstract]:With the continuous development of China's stock market, the importance of correctly classifying stocks in order to build a portfolio to reduce investment risk is also increasing. According to modern portfolio theory, through the construction of portfolio. The degree of risk in the portfolio is related to the correlation between the stocks in the portfolio, and the smaller the correlation among the stocks. Portfolio plays a more obvious risk dispersion effect. At this stage, investors often classify stocks according to the industry. Therefore. If the correlation between stock returns in the same industry is higher than the correlation between stock returns in different industries, asset matching among different industries should also play a better role. However, through empirical research, this paper proves that stock price volatility among Chinese stock market industries has a high correlation, according to the industry classification of investment portfolio to reduce the effect of risk is poor. It is very important to propose an effective method of classifying stocks outside the industry. In this paper, we use the optimized ant colony clustering algorithm to cluster all the more than 2,000 stocks in the A-share market of China. We use the financial index and the volatility of individual stock return to cluster the stocks. The analysis of clustering results verifies the feasibility and good effect of using ant colony clustering algorithm to cluster large sample data, which provides a new idea for stock classification in Chinese market. It provides theoretical and data basis for investment decision and risk control. This paper is divided into five chapters: the first chapter is the research background, literature review and the introduction of the paper framework; The second chapter introduces the industry classification of Chinese stock market and its existing problems. In chapter 3, we propose a stock classification method based on ant colony clustering algorithm. Chapter 4th makes an empirical study on the methods proposed in the third chapter. Finally, the conclusion and prospect are put forward.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F832.51;F224

【參考文獻(xiàn)】

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本文編號(hào)：1493097