制造業(yè)上市公司營運資金風險預警對比研究
發(fā)布時間：2018-07-14 13:57
【摘要】：隨著我國市場經濟建設的完善,我國各行業(yè)迅速發(fā)展,各項經濟指標每年以指數倍數增長,尤其是制造業(yè)已成為支撐我國GDP的主要產業(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、人口等各項限制條件,中國的經濟增長趨勢放緩,究其原因,為了響應創(chuàng)新型國家建設的步伐,我國制造業(yè)逐步由“世界工廠”向高級技術創(chuàng)新類產業(yè)過度,轉型的過程中企業(yè)生產經營活動都會面臨困境,尤其是和日常運作相關的營運資金的管理和控制,有必要投入更多的精力關注因營運資金運作不當而帶來的財務風險。因此為了制造業(yè)的穩(wěn)步改革發(fā)展,需要準確的對制造業(yè)上市公司的營運資金風險進行預警研究。 本研究在前人研究營運資金風險的基礎上,首先,闡述營運資金風險預警和數據挖掘技術的相關理論基礎,分析數據挖掘方法在預警分析中的優(yōu)勢和可行性；其次,選取兩市A股被預警處理的制造業(yè)為研究對象,并在指標體系的選擇準則的基礎上,構建營運資金風險預警指標體系,并詳細描述數據挖掘中BP神經網絡、Logistic回歸分析、C5.0決策樹三種預測方法的基本原理和模型的構建；最后,結合SPSS Clementine運行程序,對選取的36家制造業(yè)樣本公司進行營運資金風險預警實證對比分析,并對三種預測方法進行模型精確度評價,得到對比分析后的研究結論。 通過研究對比分析得知,基于數據挖掘的上市公司營運資金風險預警模型具有很強預警能力；并且三種預測模型越靠近被預警處理的年限,預測精度越高,表明了上市公司營運資金風險是一個動態(tài)的變量,預警模型也有很強的時效性；通過縱向比較分析歸納出,本研究建立的預測模型中BP神經網絡模型最好,Logistic回歸模型最差,C5.0決策樹模型居中；數據挖掘方法中,以知識發(fā)現為理論基礎的模型預測精度較高,優(yōu)于以統(tǒng)計分析為基礎的預測模型。因此數據挖掘技術在營運資金風險預警分析中具有可行性,企業(yè)完全可以應用數據挖掘技術,進行營運資金風險管理決策,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.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F406.7;F425
本文編號：2121863
[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.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F406.7;F425
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