本文關(guān)鍵詞：基于集成學(xué)習(xí)模型的上市公司財(cái)務(wù)困境判別研究　出處：《深圳大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 財(cái)務(wù)困境判別 集成學(xué)習(xí) 隨機(jī)森林算法

【摘要】：隨著我國資本市場的迅速發(fā)展,企業(yè)面臨的競爭日益激烈,上市企業(yè)的財(cái)務(wù)狀況受到了嚴(yán)峻的考驗(yàn)。上市公司因財(cái)務(wù)狀況異常而陷入困境的情況屢見不鮮,因此建立準(zhǔn)確的財(cái)務(wù)困境判別模型顯得尤為重要。從2006年到2015年10年的時(shí)間里,已經(jīng)有321家公司被“ST”。上市公司被“ST”后,不但影響自己正常的生產(chǎn)經(jīng)營,還讓相關(guān)投資者和債權(quán)人蒙受損失。因此,利用上市公司發(fā)布的財(cái)務(wù)數(shù)據(jù),建立上市公司財(cái)務(wù)困境判別模型以揭示風(fēng)險(xiǎn),己成為上市公司管理者、投資者和債權(quán)人等相關(guān)利益方共同關(guān)注的問題。上市公司作為我國多層次資本市場建設(shè)的基石,其經(jīng)營業(yè)績、財(cái)務(wù)狀況是資本市場健康發(fā)展的重要保障,建立上市公司的財(cái)務(wù)困境判別模型,也有利于資本市場實(shí)現(xiàn)其價(jià)格發(fā)現(xiàn)、風(fēng)險(xiǎn)轉(zhuǎn)移、資源配置的功能。在介紹了選題背景及研究意義的基礎(chǔ)上,本文明確了研究方法、主要內(nèi)容和研究框架。隨后系統(tǒng)梳理了國內(nèi)外學(xué)者在財(cái)務(wù)困境領(lǐng)域的相關(guān)研究文獻(xiàn),對財(cái)務(wù)困境的概念界定、特征和判別的相關(guān)理論等方面進(jìn)行探討,為實(shí)證分析部分的輸入指標(biāo)的選擇和建模方法應(yīng)用提供思路。本文運(yùn)用集成學(xué)習(xí)模型來提前判別上市公司會否陷入財(cái)務(wù)困境,首先對上市公司出現(xiàn)財(cái)務(wù)困境的相關(guān)特征進(jìn)行總結(jié)歸納,本文選取了在2006到2015年期間被“ST”的A股上市公司作為研究對象,并以同行業(yè)、同規(guī)模、同類型的原則選擇配對的正常企業(yè),通過對比,來研究上市公司在陷入財(cái)務(wù)困境前三年的主要特征�？疾斓奶卣骱w上市公司的報(bào)表項(xiàng)目特征,如資產(chǎn)、負(fù)債和所有者權(quán)益等項(xiàng)目,也涵蓋上市公司的相關(guān)財(cái)務(wù)指標(biāo),如償債能力、營運(yùn)能力、盈利能力、現(xiàn)金流動(dòng)能力、潛在發(fā)展能力等指標(biāo),此外還涵蓋了上市公司的治理特征,如股權(quán)結(jié)構(gòu)、董事會規(guī)模、大股東持股比例和外部審計(jì)等內(nèi)容。在對上市公司陷入財(cái)務(wù)困境前的特征有一定了解的基礎(chǔ)上,本文建立了相關(guān)指標(biāo)體系,并且通過數(shù)據(jù)處理與嚴(yán)格分析,選擇了11個(gè)指標(biāo)作為主要判別變量,這些判別變量在一定程度上可以區(qū)分正常企業(yè)與陷入財(cái)務(wù)困境的企業(yè)。接著本文構(gòu)建了隨機(jī)森林財(cái)務(wù)困境判別模型,模型中包含1000棵隨機(jī)樹,并且在每一次運(yùn)算時(shí)隨機(jī)抽取6個(gè)判別變量,使用2006到2012的樣本作為學(xué)習(xí)樣本,利用2013年到2015年的樣本作為檢驗(yàn)樣本,準(zhǔn)確率高達(dá)83%,比傳統(tǒng)分類方法高4到20個(gè)百分點(diǎn)。另外在模型中,對隨機(jī)樹木依次取0到500,隨機(jī)抽取1到10個(gè)判別變量,隨機(jī)森林算法的準(zhǔn)確率依然保持高位。當(dāng)隨機(jī)樹木個(gè)數(shù)大于100以后,準(zhǔn)確率逐步穩(wěn)定在80%以上;而抽取4到7個(gè)判別變量時(shí),模型的準(zhǔn)確率較高,這都體現(xiàn)了集成學(xué)習(xí)方法的穩(wěn)健性。最后,從隨機(jī)森林模型的結(jié)果中可以得到,每股收益與凈資產(chǎn)收益率這兩個(gè)指標(biāo)對判別上市公司財(cái)務(wù)困境有著十分重要的作用,在指標(biāo)體系中的重要性高達(dá)35%和20%。由于各種主客觀因素,本文還有些許不足,例如樣本量有限,未能覆蓋到全部的上市公司,同時(shí)選取的指標(biāo)也未能反映出導(dǎo)致上市公司陷入財(cái)務(wù)困境的所有因素。根據(jù)上述的理論分析與實(shí)證結(jié)果,本文認(rèn)為基于集成學(xué)習(xí)方法對上市公司的財(cái)務(wù)困境判別模型對于企業(yè)管理者、投資者和債權(quán)人而言,都有較高的實(shí)用價(jià)值。
[Abstract]:With the rapid development of China's capital market, enterprises are facing increasingly fierce competition in the market, the financial situation of enterprises have faced severe challenge. It is often seen. listed companies because of abnormal financial condition and the plight of the situation, so it is particularly important to establish the model of financial distress accurately. From 2006 to 2015 10 years, there have been 321 companies were "ST". The listed company is "ST", not only affect their normal production and operation, also let investors and creditors suffer losses. Therefore, the financial data released by listed companies, the establishment of corporate financial distress discriminant model to reveal the risks, has become the common concern of managers of listed companies, investors and the creditors and other stakeholders. Listed companies as the cornerstone of the construction of China's multi-level capital market, its operating results, financial condition is the capital market. An important guarantee of healthy development, the establishment of discriminant models of financial distress of listed companies, but also conducive to the capital market to achieve its price discovery, risk transfer, the function of resource allocation. Based on introducing the background and significance of research, this paper introduced research methods, main contents and research framework. Then the system reviews foreign scholars in the field of financial distress related research literature, the concept of financial distress definition, characteristics and discrimination theory so as to provide ideas for the application of empirical analysis and modeling method of input selection part of the index. In this paper, using the integrated learning model to determine in advance of listed companies will fall into financial difficulties, first of all the relevant features the listed company's financial difficulties are summarized, the paper selected during the period from 2006 to 2015 by the "ST" of the A shares of listed companies as the research object, and the The same industry, the same size, the same type of normal business, the principle of selecting the pairing by comparison, to study the main characteristics of Listed Companies in financial distress three years ago. The study covers characteristics of listed companies report project, such as assets, liabilities and owners' equity project also covers listed companies related financial indicators such as, solvency, operating capacity, profitability, cash flow ability, potential development capacity, in addition to covering corporate governance features, such as ownership structure, board size, the proportion of large shareholders and external auditing content. Based on a certain understanding of the characteristics of Listed Companies in financial distress before the in this paper, establishes the index system, and through data processing and rigorous analysis, 11 indices were chosen as the main variables, these variables in a certain extent can distinguish positive Often companies and financial distress enterprises. Then we construct a random forest financial distress discriminant model, the model contains 1000 random tree tree, and in every operation were randomly selected from 6 discriminant variables, using 2006 to 2012 samples as study samples, from 2013 to 2015 as sample test samples, the accuracy rate of up to 83%, 4 to 20 percentage points higher than the traditional classification method. In the model, the random trees from 0 to 500 were randomly selected from 1 to 10, the accuracy rate of discriminant variables, random forest algorithm remains high. When the random tree number is greater than 100, the accuracy rate gradually stabilized at 80% from 4 to 7 and above; discriminant variables, model accuracy is higher, it reflects the integrated learning method robustness. Finally, random forest can be obtained from the model results, earnings per share and net assets yield two The index plays an important role in judging the listed company's financial difficulties, the importance in the index system of up to 35% and 20%. due to various subjective and objective factors, the article still has some shortcomings, such as limited sample size, not to cover all the listed companies, while the selected index also failed to reflect the result of all factors of listed companies financial distress. According to the theoretical analysis and the empirical results above, this paper thinks that the ensemble learning method based on corporate financial distress discriminant model for enterprise managers, investors and creditors, has high practical value.

【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F275;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李光榮;李風(fēng)強(qiáng);;基于幾種神經(jīng)網(wǎng)絡(luò)方法的公司財(cái)務(wù)風(fēng)險(xiǎn)判別研究[J];經(jīng)濟(jì)經(jīng)緯;2017年02期

2 劉玉敏;劉莉;任廣乾;;基于非財(cái)務(wù)指標(biāo)的上市公司財(cái)務(wù)預(yù)警研究[J];商業(yè)研究;2016年10期

3 關(guān)欣;王征;;基于Logistic回歸和BP神經(jīng)網(wǎng)絡(luò)的財(cái)務(wù)預(yù)警模型比較[J];統(tǒng)計(jì)與決策;2016年17期

4 李揚(yáng);李竟翔;馬雙鴿;;不平衡數(shù)據(jù)的企業(yè)財(cái)務(wù)預(yù)警模型研究[J];數(shù)理統(tǒng)計(jì)與管理;2016年05期

5 武玉青;李忠衛(wèi);彭帥;;基于Panel logit模型的上市公司財(cái)務(wù)困境預(yù)警研究[J];財(cái)會通訊;2016年15期

6 王德魯;鄭建萍;馬剛;;基于多分類器融合的上市公司產(chǎn)品傷害事件風(fēng)險(xiǎn)預(yù)警系統(tǒng)[J];情報(bào)雜志;2015年09期

7 王宗勝;尚姣姣;;我國制造業(yè)上市公司財(cái)務(wù)困境預(yù)警分析[J];統(tǒng)計(jì)與決策;2015年03期

8 馬若微;張微;;基于Logistic與Fisher的上市公司財(cái)務(wù)困境判別模型比較研究[J];北京工商大學(xué)學(xué)報(bào)(社會科學(xué)版);2014年02期

9 孟杰;;隨機(jī)森林模型在財(cái)務(wù)失敗預(yù)警中的應(yīng)用[J];統(tǒng)計(jì)與決策;2014年04期

10 時(shí)建中;程龍生;牛俊磊;;基于RS-Bag分類器集成技術(shù)的上市公司財(cái)務(wù)危機(jī)預(yù)測[J];數(shù)理統(tǒng)計(jì)與管理;2013年05期

，

本文編號：1365335