【摘要】：我國創(chuàng)業(yè)板和主板都在蓬勃發(fā)展。雖然兩市場交易規(guī)則、投資者特征以及上市公司特點各有不同，但是兩個市場都是符合中國國情的市場，所以兩者之間具有可比性和彼此借鑒性。因此，可以找出主板與創(chuàng)業(yè)板兩個市場企業(yè)的顯著差異特征，，再基于這些差異的特征分析創(chuàng)業(yè)板企業(yè)的成長性特點，以及該市場的現(xiàn)有制度的不足，提出一些相關(guān)的建議和研究的方向，也同時對我國創(chuàng)業(yè)型企業(yè)的發(fā)展提供了一定的參考。 本文主要通過數(shù)據(jù)挖掘中的分類回歸樹，隨機森林，以及Bagging算法在創(chuàng)業(yè)板和主板上市公司差異中的應(yīng)用。首先，本文介紹了數(shù)據(jù)挖掘技術(shù)的理論和其在本文應(yīng)用的優(yōu)勢；其次，介紹了三種挖掘算法的原理和三者之M的對比分析；同時構(gòu)建了模型的指標(biāo)體系，以實現(xiàn)對兩市的差異性分類和得到相應(yīng)的顯著差舁特征；最后選取了2011年和2012年創(chuàng)業(yè)板和滬深300的上市公司，分別作為訓(xùn)練集和測試集，運用軟件實現(xiàn)了上述所需的相關(guān)操作。 本文研究的結(jié)糶，對于創(chuàng)業(yè)板和主板來說，三個模型的對測試集的分類效果都比較理想，達到了80%以上的正確率；后續(xù)對測試集的預(yù)測正確率也很好。從而通過分類的方法實現(xiàn)了降維的目的，得到了三個重要分類指標(biāo)。然后以主板為基準(zhǔn)，基于這三個差異指標(biāo)分析了創(chuàng)業(yè)板上市企業(yè)成長性特點，得到了我國創(chuàng)業(yè)板目前出現(xiàn)的一些問題。最后通過本文的研究，為后續(xù)對我同創(chuàng)業(yè)板的相關(guān)制度的不足和未來這方面的研究方向做了展望。
[Abstract]:China's growth Enterprise Market and the main Board are booming. Although the trading rules of the two markets, the characteristics of investors and listed companies are different, but the two markets are in line with the national conditions of China, so the two markets are comparable and can be used for reference. Therefore, we can find out the characteristics of the significant differences between the main board and the gem, and then analyze the growth characteristics of the gem enterprises based on the characteristics of these differences, as well as the shortcomings of the existing system of the market. At the same time, it provides some references for the development of entrepreneurial enterprises in China. This paper mainly applies the classification regression tree, stochastic forest and Bagging algorithm to the difference between gem and main board listed companies in data mining. First of all, this paper introduces the theory of data mining technology and its advantages in this paper. Secondly, it introduces the principle of three mining algorithms and the comparative analysis of three kinds of M, and constructs the index system of the model. Finally, the listed companies of gem and CS300 in 2011 and 2012 are selected as training set and test set, and the relevant operations mentioned above are realized by software. For the gem and the main board, the classification effect of the three models on the test set is ideal, reaching the accuracy rate of more than 80%, and the prediction accuracy rate of the test set is also very good. The aim of dimensionality reduction is realized by the method of classification, and three important classification indexes are obtained. Then taking the main board as the benchmark, this paper analyzes the growth characteristics of the gem listed enterprises based on these three indicators, and obtains some problems in the gem at present. Finally, through the research of this paper, the future research direction and the deficiency of the related system with gem are prospected.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51;TP311.13;F275

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