本文關(guān)鍵詞：基于BP神經(jīng)網(wǎng)絡(luò)的上市公司財(cái)務(wù)危機(jī)預(yù)警研究　出處：《南華大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 現(xiàn)金流量指標(biāo) BP神經(jīng)網(wǎng)絡(luò) 財(cái)務(wù)預(yù)警

【摘要】：目前，我國(guó)證券市場(chǎng)的不斷發(fā)展導(dǎo)致目前關(guān)于我國(guó)上市公司的相關(guān)政策和法規(guī)不斷趨于完善，這對(duì)于我國(guó)上市公司而言既是機(jī)會(huì)也是威脅。機(jī)會(huì)在于法律法規(guī)的不斷完善使證券市場(chǎng)變得更趨于規(guī)范化；威脅在于這也大大的約束了上市公司，稍不留神公司也許就會(huì)被“ST”或“*ST”。因此，在現(xiàn)代激烈的市場(chǎng)競(jìng)爭(zhēng)下，企業(yè)若想生存、發(fā)展和獲利就必須要加強(qiáng)對(duì)企業(yè)財(cái)務(wù)風(fēng)險(xiǎn)的控制和財(cái)務(wù)危機(jī)的防范。鑒于此，建立一套行之有效的上市公司財(cái)務(wù)危機(jī)預(yù)警系統(tǒng)迫在眉睫。 本文首先從BP神經(jīng)網(wǎng)絡(luò)和現(xiàn)金流量指標(biāo)的選取兩方面對(duì)國(guó)內(nèi)外相關(guān)文獻(xiàn)進(jìn)行了簡(jiǎn)單回顧，然后介紹了財(cái)務(wù)預(yù)警相關(guān)的理論基礎(chǔ)，界定了財(cái)務(wù)危機(jī)的含義及實(shí)證中財(cái)務(wù)危機(jī)樣本的類型，也詳細(xì)闡述了BP神經(jīng)網(wǎng)絡(luò)相關(guān)理論。隨后在前人研究的基礎(chǔ)上，本文從償債能力，運(yùn)營(yíng)能力，盈利和獲現(xiàn)能力，成長(zhǎng)能力，財(cái)務(wù)彈性以及現(xiàn)金流量結(jié)構(gòu)等六個(gè)方面共選取了26個(gè)指標(biāo)，建立了一套以現(xiàn)金流量指標(biāo)為主財(cái)務(wù)預(yù)警指標(biāo)體系。本文的實(shí)證部分選取了2007年到2012年間滬、深兩市A股共77家ST公司，并按照同行業(yè)、同時(shí)期、同規(guī)模的原則選取77家非ST公司做為本文的訓(xùn)練樣本，用樣本被ST前一年、二年、三年的數(shù)據(jù)對(duì)指標(biāo)進(jìn)行篩選，并用篩選出來(lái)的指標(biāo)建模。本文用神經(jīng)網(wǎng)絡(luò)的方法建模，實(shí)證分析的結(jié)果表明BP神經(jīng)網(wǎng)絡(luò)方法所建立的財(cái)務(wù)預(yù)警模型在上市公司被ST前三年的預(yù)警準(zhǔn)確率分別達(dá)到了96.08%、88.24%和78.92%，，從而證實(shí)了神經(jīng)網(wǎng)絡(luò)財(cái)務(wù)預(yù)警模型的優(yōu)越性及其預(yù)測(cè)的準(zhǔn)確性。 本文得到的結(jié)論如下：（1）本文所建立的以現(xiàn)金流量為主的財(cái)務(wù)預(yù)警指標(biāo)體系具有很好的財(cái)務(wù)預(yù)警效果；（2）本文用BP神經(jīng)網(wǎng)絡(luò)的方法建立的財(cái)務(wù)預(yù)警模型預(yù)測(cè)精確度高，具有很強(qiáng)的應(yīng)用價(jià)值；（3）本文構(gòu)建的神經(jīng)網(wǎng)絡(luò)模型，當(dāng)模型輸入數(shù)據(jù)在[0,1]之間時(shí)，能夠取得較為穩(wěn)定的預(yù)測(cè)結(jié)果。
[Abstract]:At present, the continuous development of the securities market in China has led to the improvement of the relevant policies and regulations of listed companies in China. This is both an opportunity and a threat to our listed companies. The opportunity lies in the continuous improvement of laws and regulations to make the securities market more standardized; The threat lies in the fact that this greatly restricts the listed company, and the company may be "St" or "St". Therefore, in the modern fierce market competition, the enterprise wants to survive. In order to develop and make profits, we must strengthen the control of financial risk and the prevention of financial crisis. In view of this, it is urgent to establish a set of effective financial crisis warning system of listed companies. In this paper, the BP neural network and the selection of cash flow indicators of the two aspects of the domestic and foreign literature were reviewed, and then introduced the theoretical basis of financial early warning. Define the meaning of financial crisis and the types of financial crisis samples in the empirical, but also elaborate the relevant theory of BP neural network. Then on the basis of previous studies, this paper from the solvency, operational capacity. A total of 26 indicators were selected in six aspects, namely, profitability and cash flow structure, growth capacity, financial elasticity and cash flow structure. Established a set of cash flow indicators as the main financial warning index system. The empirical part of this paper selected a total of 77 St companies in Shanghai and Shenzhen A shares from 2007 to 2012, and according to the same industry. At the same time, the same scale of 77 non-St companies as the training sample of this paper, with the sample of St one year, two years, three years to screen the indicators. Modeling with the selected index. This paper uses the method of neural network modeling. The results of empirical analysis show that the financial early warning model established by BP neural network method in listed companies before St three years of warning accuracy reached 96.08% 88.24% and 78.92% respectively. The superiority of neural network financial early warning model and the accuracy of its prediction are verified. The conclusion of this paper is as follows: 1) the financial forewarning index system based on cash flow established in this paper has a good financial early warning effect; 2) the financial early warning model established by BP neural network has high accuracy and has strong application value. 3) the neural network model constructed in this paper, when the model input data in [Between 0 and 1, a more stable prediction result can be obtained.
【學(xué)位授予單位】：南華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;F832.51;F275

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張根明;向曉驥;孫敬宜;;基于BP神經(jīng)網(wǎng)絡(luò)的制造業(yè)上市公司財(cái)務(wù)預(yù)警[J];山東工商學(xué)院學(xué)報(bào);2006年04期

2 周娟;王麗娟;;基于現(xiàn)金流量指標(biāo)的財(cái)務(wù)危機(jī)預(yù)警模型分析[J];財(cái)會(huì)通訊(學(xué)術(shù)版);2005年12期

3 唐敏;王華;張沔;;企業(yè)財(cái)務(wù)危機(jī)內(nèi)涵探究及相關(guān)概念辨析[J];財(cái)會(huì)通訊(學(xué)術(shù)版);2007年12期

4 王金鳳 ,楊松濤;上市公司惡性財(cái)務(wù)危機(jī)預(yù)警模型有效性研究[J];財(cái)會(huì)月刊;2005年21期

5 梁飛媛;基于現(xiàn)金流的財(cái)務(wù)危機(jī)預(yù)警系統(tǒng)實(shí)證研究——以機(jī)械設(shè)備行業(yè)為例[J];財(cái)貿(mào)經(jīng)濟(jì);2005年02期

6 孫靜;張子健;;傳統(tǒng)財(cái)務(wù)預(yù)警的指標(biāo)與現(xiàn)金流指標(biāo)的整合研究[J];中國(guó)管理信息化;2009年09期

7 梁飛媛;基于現(xiàn)金流的財(cái)務(wù)危機(jī)預(yù)警指標(biāo)體系初探[J];貴州財(cái)經(jīng)學(xué)院學(xué)報(bào);2005年04期

8 谷祺,劉淑蓮;財(cái)務(wù)危機(jī)企業(yè)投資行為分析與對(duì)策[J];會(huì)計(jì)研究;1999年10期

9 儲(chǔ)一昀,王安武;上市公司盈利質(zhì)量分析[J];會(huì)計(jì)研究;2000年09期

10 劉洪,何光軍;基于人工神經(jīng)網(wǎng)絡(luò)方法的上市公司經(jīng)營(yíng)失敗預(yù)警研究[J];會(huì)計(jì)研究;2004年02期

本文編號(hào)：1376583