【摘要】：鋼鐵行業(yè)是我國國民經(jīng)濟(jì)的支柱產(chǎn)業(yè)，，目前全球經(jīng)濟(jì)持續(xù)放緩，鐵礦石、能源價(jià)格高位運(yùn)行，使得原本處于產(chǎn)能過剩的我國鋼鐵行業(yè)經(jīng)營狀況進(jìn)一步惡化，利潤率大幅下滑，財(cái)務(wù)風(fēng)險(xiǎn)加劇。鋼鐵業(yè)一旦發(fā)生財(cái)務(wù)風(fēng)險(xiǎn)，不僅危及其自身的生存和發(fā)展，也會(huì)給投資者和其他關(guān)聯(lián)產(chǎn)業(yè)帶來損失。因此，構(gòu)建一個(gè)有效實(shí)用的鋼鐵業(yè)上市公司財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警模型，滿足利益相關(guān)者的需要，具有較大的現(xiàn)實(shí)意義。 本文從智能理論角度著手，首先對粗糙集理論及其約簡屬性和BP神經(jīng)網(wǎng)絡(luò)的基本工作原理做了介紹，提出一種將粗糙集與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的技術(shù)方法，把該方法應(yīng)用于我國鋼鐵業(yè)上市公司財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警研究中。 首先，介紹了我國鋼鐵業(yè)上市公司財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警研究的研究背景、研究意義和國內(nèi)外研究現(xiàn)狀，并指出了以前研究的成果及其實(shí)用性，論證了本次研究的必要性； 其次，對財(cái)務(wù)風(fēng)險(xiǎn)做了界定，并對其形成因素做了詳細(xì)分析，分別闡述了粗糙集理論和BP神經(jīng)網(wǎng)絡(luò)的基本工作原理，詳細(xì)分析了二者結(jié)合的互補(bǔ)優(yōu)勢特性，為后文預(yù)警模型的建立打下了理論基礎(chǔ)； 再次，介紹了目前我國鋼鐵業(yè)上市公司財(cái)務(wù)風(fēng)險(xiǎn)的表現(xiàn)形式，并對影響風(fēng)險(xiǎn)產(chǎn)生的外部與內(nèi)部因素做了全面分析，在該分析基礎(chǔ)上結(jié)合鋼鐵行業(yè)自身特點(diǎn)，選取能夠表現(xiàn)鋼鐵企業(yè)財(cái)務(wù)狀況的財(cái)務(wù)指標(biāo)和非財(cái)務(wù)指標(biāo)，構(gòu)建財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警指標(biāo)體系； 最后，選取30家鋼鐵業(yè)上市公司為研究樣本，對樣本的指標(biāo)數(shù)據(jù)按照上述方法進(jìn)行處理，針對傳統(tǒng)方法在預(yù)警模型建立方面存在的局限性，本文創(chuàng)造性地利用層次聚類分析將樣本企業(yè)財(cái)務(wù)狀況劃分為遞進(jìn)的五級層次，構(gòu)建BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型，多級分類的財(cái)務(wù)狀況為預(yù)警模型提供精確的輸出層目標(biāo)。訓(xùn)練BP神經(jīng)網(wǎng)絡(luò)后，用檢測樣本對其進(jìn)行檢驗(yàn)，證明模型預(yù)警效果良好。實(shí)驗(yàn)結(jié)果證實(shí)針對鋼鐵業(yè)上市公司所構(gòu)建的粗糙集—BP神經(jīng)網(wǎng)絡(luò)財(cái)務(wù)預(yù)警模型是有效的。
[Abstract]:The iron and steel industry is the pillar industry of our national economy. At present, the global economy continues to slow down, and the iron ore and energy prices are running high. This has further worsened the operating situation of the steel industry in China, which was originally under overcapacity, and its profit margin has fallen sharply. The financial risk intensifies. Once the financial risk occurs in the steel industry, it not only endangers its own survival and development, but also brings losses to investors and other related industries. Therefore, it is of great practical significance to construct an effective and practical financial risk early warning model for listed steel companies to meet the needs of stakeholders. In this paper, from the angle of intelligence theory, the rough set theory and its reduction attribute and the basic working principle of BP neural network are introduced, and a technical method combining rough set with BP neural network is proposed. This method is applied to the financial risk early warning research of listed steel companies in China. First of all, it introduces the research background, significance and current situation of financial risk early warning research of listed steel companies in China, and points out the results of previous research and its practicability, and demonstrates the necessity of this study. Secondly, financial risk is defined, and its forming factors are analyzed in detail. The basic working principles of rough set theory and BP neural network are expounded respectively, and the complementary advantages of the two combination are analyzed in detail. It lays a theoretical foundation for the establishment of the early warning model. Thirdly, it introduces the expression forms of financial risks of listed steel companies in China, and makes a comprehensive analysis of the external and internal factors that affect the emergence of the risks. On the basis of this analysis, combining the characteristics of iron and steel industry, select the financial indicators and non-financial indicators that can express the financial situation of iron and steel enterprises, and construct the financial risk warning index system. 30 listed steel companies are selected as the research samples, and the index data of the samples are processed in accordance with the above methods, aiming at the limitations of the traditional methods in the establishment of early warning models. This paper creatively uses hierarchical clustering analysis to divide the financial situation of sample enterprises into progressive five levels, and constructs a BP neural network early warning model. The financial situation of multilevel classification provides the accurate output level target for the early warning model. After training BP neural network, it is proved that the early warning effect of the model is good. The experimental results show that the rough set-BP neural network financial early warning model for steel listed companies is effective.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F275;F426.31;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鮑新中;孫瑩;;基于粗糙集理論的冶金企業(yè)備件供應(yīng)商選擇多屬性決策[J];北京科技大學(xué)學(xué)報(bào);2010年08期

2 徐曉雯;;反傾銷視角下鋼鐵行業(yè)成本核算方法的探究[J];財(cái)經(jīng)界(學(xué)術(shù)版);2010年04期

3 宋力,李晶;上市公司財(cái)務(wù)危機(jī)預(yù)警模型的實(shí)證研究[J];財(cái)經(jīng)論叢(浙江財(cái)經(jīng)學(xué)院學(xué)報(bào));2004年01期

4 田志龍;賀遠(yuǎn)瓊;衣光喜;趙昌旭;;寡頭壟斷行業(yè)的價(jià)格行為——對我國鋼鐵行業(yè)的案例研究[J];管理世界;2005年04期

5 張宏玲;張宏;;外匯風(fēng)險(xiǎn)管理在宣鋼的應(yīng)用[J];財(cái)務(wù)與會(huì)計(jì);2005年08期

6 王璐璐;;淺談國內(nèi)鋼鐵企業(yè)財(cái)務(wù)風(fēng)險(xiǎn)管理系統(tǒng)[J];經(jīng)營管理者;2011年17期

7 李擁軍;王德春;;我國鋼鐵工業(yè)的利潤空間、結(jié)構(gòu)均衡及資源控制力[J];宏觀經(jīng)濟(jì)研究;2009年07期

8 孫海剛;;鋼鐵上市公司的技術(shù)效率與企業(yè)績效——基于我國鋼鐵產(chǎn)業(yè)競爭力的提升[J];經(jīng)濟(jì)管理;2009年07期

9 趙燕娜;孫育強(qiáng);馬玉蘭;朝霞;;我國大型鋼鐵企業(yè)集成風(fēng)險(xiǎn)預(yù)警體系研究初探[J];經(jīng)濟(jì)師;2006年02期

10 章忠志,符林,唐煥文;基于人工神經(jīng)網(wǎng)絡(luò)的商業(yè)銀行信用風(fēng)險(xiǎn)模型[J];經(jīng)濟(jì)數(shù)學(xué);2003年03期

本文編號：2272360