【摘要】：隨著我國市場經(jīng)濟(jì)建設(shè)的完善,我國各行業(yè)迅速發(fā)展,各項(xiàng)經(jīng)濟(jì)指標(biāo)每年以指數(shù)倍數(shù)增長,尤其是制造業(yè)已成為支撐我國GDP的主要產(chǎn)業(yè)之一,我國也成為制造業(yè)大國,在國際上也有“世界工廠”的稱號。但是,由于能源、環(huán)境、人口等各項(xiàng)限制條件,中國的經(jīng)濟(jì)增長趨勢放緩,究其原因,為了響應(yīng)創(chuàng)新型國家建設(shè)的步伐,我國制造業(yè)逐步由“世界工廠”向高級技術(shù)創(chuàng)新類產(chǎn)業(yè)過度,轉(zhuǎn)型的過程中企業(yè)生產(chǎn)經(jīng)營活動都會面臨困境,尤其是和日常運(yùn)作相關(guān)的營運(yùn)資金的管理和控制,有必要投入更多的精力關(guān)注因營運(yùn)資金運(yùn)作不當(dāng)而帶來的財(cái)務(wù)風(fēng)險。因此為了制造業(yè)的穩(wěn)步改革發(fā)展,需要準(zhǔn)確的對制造業(yè)上市公司的營運(yùn)資金風(fēng)險進(jìn)行預(yù)警研究。 本研究在前人研究營運(yùn)資金風(fēng)險的基礎(chǔ)上,首先,闡述營運(yùn)資金風(fēng)險預(yù)警和數(shù)據(jù)挖掘技術(shù)的相關(guān)理論基礎(chǔ),分析數(shù)據(jù)挖掘方法在預(yù)警分析中的優(yōu)勢和可行性；其次,選取兩市A股被預(yù)警處理的制造業(yè)為研究對象,并在指標(biāo)體系的選擇準(zhǔn)則的基礎(chǔ)上,構(gòu)建營運(yùn)資金風(fēng)險預(yù)警指標(biāo)體系,并詳細(xì)描述數(shù)據(jù)挖掘中BP神經(jīng)網(wǎng)絡(luò)、Logistic回歸分析、C5.0決策樹三種預(yù)測方法的基本原理和模型的構(gòu)建；最后,結(jié)合SPSS Clementine運(yùn)行程序,對選取的36家制造業(yè)樣本公司進(jìn)行營運(yùn)資金風(fēng)險預(yù)警實(shí)證對比分析,并對三種預(yù)測方法進(jìn)行模型精確度評價,得到對比分析后的研究結(jié)論。 通過研究對比分析得知,基于數(shù)據(jù)挖掘的上市公司營運(yùn)資金風(fēng)險預(yù)警模型具有很強(qiáng)預(yù)警能力；并且三種預(yù)測模型越靠近被預(yù)警處理的年限,預(yù)測精度越高,表明了上市公司營運(yùn)資金風(fēng)險是一個動態(tài)的變量,預(yù)警模型也有很強(qiáng)的時效性；通過縱向比較分析歸納出,本研究建立的預(yù)測模型中BP神經(jīng)網(wǎng)絡(luò)模型最好,Logistic回歸模型最差,C5.0決策樹模型居中；數(shù)據(jù)挖掘方法中,以知識發(fā)現(xiàn)為理論基礎(chǔ)的模型預(yù)測精度較高,優(yōu)于以統(tǒng)計(jì)分析為基礎(chǔ)的預(yù)測模型。因此數(shù)據(jù)挖掘技術(shù)在營運(yùn)資金風(fēng)險預(yù)警分析中具有可行性,企業(yè)完全可以應(yīng)用數(shù)據(jù)挖掘技術(shù),進(jìn)行營運(yùn)資金風(fēng)險管理決策,以提高資金使用效率。
[Abstract]:With the perfection of market economy construction in our country and the rapid development of various industries in our country, every economic index is increasing exponentially every year, especially the manufacturing industry has become one of the main industries supporting our GDP, and our country has also become a big manufacturing country. There is also the title of "World Factory" in the world. However, due to the restrictions on energy, environment and population, China's economic growth trend has slowed down. In order to respond to the pace of building an innovative country, The manufacturing industry of our country has gradually changed from "world factory" to "advanced technology innovation industry". During the process of transformation, enterprises will face difficulties in their production and operation activities, especially the management and control of working capital related to daily operation. It is necessary to devote more attention to the financial risks caused by the improper operation of working capital. Therefore, for the steady reform and development of manufacturing industry, it is necessary to study the working capital risk of listed manufacturing companies accurately. On the basis of the previous research on working capital risk, firstly, the paper expounds the relevant theoretical basis of working capital risk early warning and data mining technology, and analyzes the advantages and feasibility of data mining method in early warning analysis. Based on the selection criteria of the index system, the working capital risk early warning index system is constructed. The basic principle and model construction of three prediction methods of BP neural network and logistic regression analysis and C5.0 decision tree in data mining are described in detail. Finally, combined with SPSS Clementine running program, The empirical comparative analysis of working capital risk early warning is carried out on 36 manufacturing industry sample companies, and the model accuracy of three forecasting methods is evaluated, and the conclusion is obtained. Through comparative analysis, we know that the working capital risk early-warning model of listed companies based on data mining has strong early-warning ability, and the closer the three forecasting models are to the years of early warning, the higher the prediction accuracy is. It shows that the working capital risk of listed companies is a dynamic variable, and the early warning model has strong timeliness. In the prediction model established in this study, the BP neural network model is the best and the logistic regression model is the worst C5.0 decision tree model, and in the data mining method, the prediction accuracy of the model based on knowledge discovery is higher than that of the model based on knowledge discovery. It is superior to the prediction model based on statistical analysis. Therefore, data mining technology is feasible in the early warning analysis of working capital risk. Enterprises can use data mining technology to make working capital risk management decision, in order to improve the efficiency of capital use.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F406.7;F425

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