【摘要】：隨著我國(guó)證券市場(chǎng)的發(fā)展,上市公司面臨財(cái)務(wù)狀況異常或其他異常情況,資產(chǎn)重組便成為許多上市公司進(jìn)行改善績(jī)效的重要方法之一,它將作為證券市場(chǎng)里最能體現(xiàn)市場(chǎng)效率、最具創(chuàng)新活力的一環(huán)在我國(guó)上市公司中不斷上演。究其原因,主要是由于資產(chǎn)重組可以幫助ST公司調(diào)整產(chǎn)業(yè)結(jié)構(gòu)、優(yōu)化資源配置、改善企業(yè)經(jīng)營(yíng)結(jié)構(gòu),提高資產(chǎn)營(yíng)運(yùn)績(jī)效,從而實(shí)現(xiàn)成功摘帽。而資產(chǎn)重組績(jī)效預(yù)測(cè)則是作為對(duì)ST公司在重組前進(jìn)行判斷其能否成功摘帽的一種方式,在公司的經(jīng)營(yíng)管理中扮演著舉足輕重的角色。因此,如何對(duì)ST公司進(jìn)行資產(chǎn)重組績(jī)效預(yù)測(cè)以及如何提高其預(yù)測(cè)準(zhǔn)確率是刻不容緩的。 本文以上市公司資產(chǎn)重組作為研究對(duì)象,由于收集的數(shù)據(jù)集顯示重組成功的企業(yè)與重組失敗的企業(yè)的數(shù)目存在較大的非平衡性,雖然這是符合客觀實(shí)際,但是相對(duì)傳統(tǒng)的機(jī)器學(xué)習(xí)算法來(lái)說(shuō),通常會(huì)產(chǎn)生偏向多數(shù)類(lèi)的結(jié)果,因而對(duì)于具有重要作用的少數(shù)類(lèi)而言,預(yù)測(cè)的效果會(huì)相對(duì)較差。為了提高準(zhǔn)確率,本文對(duì)收集的數(shù)據(jù)進(jìn)行了平衡性處理。之后,利用單一預(yù)測(cè)模型來(lái)判斷企業(yè)資產(chǎn)重組是否取得好的效果,并且選用十種方法進(jìn)行性能比較,其中包括logit、probit、SVM、 MDA、CBR、BaggingLOGIT、BaggingSVM、BaggingPROBIT、BaggingCBR和BaggingMDA。結(jié)果顯示：SVM和CBR模型對(duì)上市公司資產(chǎn)重組績(jī)效的判斷準(zhǔn)確率要優(yōu)于其它八種預(yù)測(cè)模型,也就是說(shuō)這些模型可以判斷出80%以上的ST公司能否在資產(chǎn)重組后1年恢復(fù)上級(jí)狀態(tài)。通過(guò)此次研究,我們可以為ST公司能否通過(guò)資產(chǎn)重組來(lái)改善企業(yè)績(jī)效提供一定的理論依據(jù)。 其次,為了提高我國(guó)上市公司資產(chǎn)重組績(jī)效預(yù)測(cè)的準(zhǔn)確性,本文在單一預(yù)測(cè)模型的基礎(chǔ)上對(duì)預(yù)測(cè)模型進(jìn)行了改進(jìn),采用不同與前人對(duì)資產(chǎn)重組績(jī)效研究的方法,應(yīng)用聚類(lèi)混合分類(lèi)器與單一預(yù)測(cè)模型以及聚類(lèi)融合分類(lèi)器與單一預(yù)測(cè)模型相結(jié)合,建立了十種新的預(yù)測(cè)模型,即CLOGIT、CPROBIT、CMDA、CSVM、 CCBR、BaggingCLOGIT、BaggingCPROBIT、BaggingCMDA、BaggingCSVM和BaggingCCBR對(duì)資產(chǎn)重組績(jī)效進(jìn)行預(yù)測(cè)并且對(duì)這十種模型進(jìn)行性能的比較。結(jié)果顯示：聚類(lèi)融合算法與聚類(lèi)算法建立的模型無(wú)論是在準(zhǔn)確率還是在真正率和真負(fù)率方面都有積極的表現(xiàn)。其中,在準(zhǔn)確率和真負(fù)率方面,通過(guò)聚類(lèi)混合建立的模型的預(yù)測(cè)均值要高于聚類(lèi)融合分類(lèi)方法建立的模型；而對(duì)于真正率,聚類(lèi)融合分類(lèi)得到的均值要優(yōu)于聚類(lèi)混合方法建立的模型。 再次,為了對(duì)以上建立的二十種模型進(jìn)行驗(yàn)證,本文另外收集了2011-2012年的ST公司作為新樣本集進(jìn)行分析,檢驗(yàn)對(duì)上市公司資產(chǎn)重組績(jī)效預(yù)測(cè)研究的重要性以及本文所用各個(gè)資產(chǎn)重組預(yù)測(cè)模型的實(shí)用價(jià)值。實(shí)驗(yàn)結(jié)果表明傳統(tǒng)統(tǒng)計(jì)模型的預(yù)測(cè)結(jié)果次于人工智能方法建立的模型。其中,支持向量機(jī)及其集成、聚類(lèi)形成的模型的預(yù)測(cè)結(jié)果在非平衡數(shù)據(jù)還是平衡數(shù)據(jù)中均相對(duì)穩(wěn)定且效果較好,而案例推理及其集成、聚類(lèi)形成的模型的預(yù)測(cè)結(jié)果在非平衡數(shù)據(jù)中相對(duì)較好。 最后,本文對(duì)其研究進(jìn)行總結(jié),并指出了相關(guān)的管理啟示,在實(shí)際應(yīng)用中具有重要的作用,并為企業(yè)管理者進(jìn)行決策提供了一定的理論依據(jù)。
[Abstract]:With the development of China's securities market, listed companies are faced with abnormal financial conditions or other abnormal conditions. Asset reorganization has become one of the most important ways for many listed companies to improve their performance. It will be performed in the listed companies in our country as the most efficient and innovative part of the stock market. The main reason is that the asset reorganization can help ST company to adjust the industrial structure, optimize the allocation of resources, improve the enterprise management structure, improve the performance of the operation of the assets, and thus achieve the success of the cap. It plays an important role. Therefore, how to predict the performance of ST assets restructuring and how to improve its accuracy is urgent.
This paper takes the assets reorganization of the listed companies as the research object. As the collection of data sets shows that the number of successful restructuring enterprises and the number of enterprises that have failed to reorganize has a large non balance. Although this is in accordance with the objective reality, the result is usually biased toward the majority of the traditional machine learning algorithms. In order to improve the accuracy, this paper deals with the collected data in order to improve the accuracy. Then, the single prediction model is used to determine whether the enterprise asset reorganization has achieved good results, and the ten methods are selected for performance comparison, including logit, probit, SVM, MDA, CBR, Ba. The results of ggingLOGIT, BaggingSVM, BaggingPROBIT, BaggingCBR and BaggingMDA. show that the accuracy of SVM and CBR model is better than the other eight forecasting models for the performance of the listed companies' assets reorganization, that is to say, these models can judge whether more than 80% ST companies can recover their superior state in 1 years after the restructure. Through this study We can provide a theoretical basis for ST company to improve its performance through asset restructuring.
Secondly, in order to improve the accuracy of asset reorganization performance prediction in China's listed companies, this paper improves the prediction model on the basis of a single prediction model, adopts the methods of different and previous research on the performance of asset reorganization, and applies the mixed classifier and the single predictive model, the Clustering Fusion classifier and the single prediction model. Combined, ten new prediction models are established, that is, CLOGIT, CPROBIT, CMDA, CSVM, CCBR, BaggingCLOGIT, BaggingCPROBIT, BaggingCMDA, BaggingCSVM and BaggingCCBR are used to predict the asset reorganization performance and compare the performance of the ten models. The results show that the cluster fusion algorithm and the clustering algorithm are in the model The accuracy rate is also positive in both real and true negative rates. In terms of accuracy and true negative, the mean value of the model established by cluster mixing is higher than that of the clustering fusion classification method, while the mean value of the clustering fusion classification is better than the model established by the clustering method for the real rate.
Thirdly, in order to verify the twenty models established above, this paper also collected 2011-2012 years' ST company as a new sample set to analyze the importance of the performance prediction research on the assets reorganization of the listed companies as well as the practical value of the asset reorganization prediction model used in this paper. The experimental results show the traditional statistical model. The prediction results are second to the model established by artificial intelligence methods. Among them, the prediction results of the support vector machine and its integration, the prediction results of the clustering model are relatively stable in the unbalanced data or in the balanced data, and the case reasoning and its integration, the prediction results of the model formed by the cluster are relatively better in the non balanced data.
Finally, this paper summarizes the research, and points out the related management enlightenment, and plays an important role in the practical application, and provides a certain theoretical basis for the enterprise managers to make decisions.
【學(xué)位授予單位】：浙江師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F832.51;F275

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陸國(guó)慶;中國(guó)上市公司不同資產(chǎn)重組類(lèi)型的績(jī)效比較——對(duì)1999年度滬市的實(shí)證分析[J];財(cái)經(jīng)科學(xué);2000年06期

2 王躍堂;我國(guó)證券市場(chǎng)資產(chǎn)重組績(jī)效之比較分析[J];財(cái)經(jīng)研究;1999年07期

3 姚祿仕;李勝南;;上市公司資產(chǎn)重組績(jī)效的實(shí)證研究[J];財(cái)會(huì)月刊;2007年29期

4 楊柳;張俊芝;;淺談聚類(lèi)算法及其存在的問(wèn)題[J];產(chǎn)業(yè)與科技論壇;2012年02期

5 益智;;中國(guó)上市公司被動(dòng)式資產(chǎn)重組實(shí)證研究——基于價(jià)值效應(yīng)和績(jī)效的動(dòng)因模型構(gòu)建[J];管理世界;2005年01期

6 王千;王成;馮振元;葉金鳳;;K-means聚類(lèi)算法研究綜述[J];電子設(shè)計(jì)工程;2012年07期

7 包關(guān)云;;股權(quán)轉(zhuǎn)讓環(huán)節(jié)稅收政策分析[J];財(cái)會(huì)月刊;2012年16期

8 楊海軍;太雷;;基于模糊支持向量機(jī)的上市公司財(cái)務(wù)困境預(yù)測(cè)[J];管理科學(xué)學(xué)報(bào);2009年03期

9 黎祚;周步祥;林楠;;基于模糊聚類(lèi)與改進(jìn)BP算法的日負(fù)荷特性曲線分類(lèi)與短期負(fù)荷預(yù)測(cè)[J];電力系統(tǒng)保護(hù)與控制;2012年03期

10 邵希娟;曾海花;;我國(guó)上市公司財(cái)務(wù)困境的預(yù)警模型研究[J];經(jīng)濟(jì)管理;2009年09期

相關(guān)博士學(xué)位論文 前2條

1 孫潔;企業(yè)財(cái)務(wù)危機(jī)預(yù)警的智能決策方法研究[D];哈爾濱工業(yè)大學(xué);2007年

2 黃興孿;中國(guó)上市公司并購(gòu)動(dòng)因與績(jī)效研究[D];廈門(mén)大學(xué);2009年

，

本文編號(hào)：2138455