高新技術(shù)制造企業(yè)的動態(tài)財務(wù)危機(jī)預(yù)警研究
發(fā)布時間：2018-12-16 00:12
【摘要】：高新技術(shù)制造業(yè)是制造業(yè)的主力軍，具有高技術(shù)含量及高附加值兩個特點(diǎn)，對高新技術(shù)制造企業(yè)進(jìn)行財務(wù)預(yù)警研究，能夠幫助高新技術(shù)制造企業(yè)避免財務(wù)危機(jī)，保障高新技術(shù)企業(yè)的良性經(jīng)營，進(jìn)而促進(jìn)國民經(jīng)濟(jì)發(fā)展。本文回顧和總結(jié)了國內(nèi)外財務(wù)預(yù)警研究文獻(xiàn)，對比分析各類研究的優(yōu)缺點(diǎn)，認(rèn)為基于人工智能的組合模型是現(xiàn)代財務(wù)預(yù)警研究的有效方法。在界定財務(wù)危機(jī)概念時，結(jié)合我國上市公司實(shí)際情況，將公司被ST(特別處理)作為陷入財務(wù)危機(jī)的標(biāo)志。以高新技術(shù)制造業(yè)上市公司為研究對象，根據(jù)預(yù)警指標(biāo)體系的構(gòu)建原則和高新技術(shù)制造企業(yè)的行業(yè)特點(diǎn)，構(gòu)建了適用于高新技術(shù)制造企業(yè)的財務(wù)預(yù)警指標(biāo)體系。 由于企業(yè)財務(wù)危機(jī)的出現(xiàn)是一個連續(xù)的動態(tài)發(fā)展過程，本文從短期與長期兩個角度出發(fā)對高新技術(shù)制造業(yè)上市公司進(jìn)行財務(wù)危機(jī)預(yù)警研究。其中，短期動態(tài)預(yù)警是以季度為單位，將上市公司的季度財務(wù)面板數(shù)據(jù)引入綜合灰色預(yù)測GM(1,1)和BP神經(jīng)網(wǎng)絡(luò)的動態(tài)模型中來判斷公司財務(wù)狀況，并以“思達(dá)高科”上市公司作為實(shí)例進(jìn)行模型的應(yīng)用，結(jié)果表明基于灰色-BP神經(jīng)網(wǎng)絡(luò)模型能有效反映公司財務(wù)狀況的發(fā)展趨勢，時效性較強(qiáng)；長期動態(tài)預(yù)警是以年度為單位，將發(fā)生危機(jī)前兩年和前三年（T-2期和T-3期）的財務(wù)面板數(shù)據(jù)引入基于Logistic-BP神經(jīng)網(wǎng)絡(luò)模型中進(jìn)行動態(tài)預(yù)警，將預(yù)測結(jié)果與一般Logistic回歸分析和BP神經(jīng)網(wǎng)絡(luò)模型比較，證明Logistic-BP神經(jīng)網(wǎng)絡(luò)預(yù)警模型更能體現(xiàn)財務(wù)危機(jī)的發(fā)生機(jī)理，并以“新華制藥”上市公司作為實(shí)例進(jìn)行模型的應(yīng)用，結(jié)果證明了模型的有效性，體現(xiàn)了模型較高的預(yù)警精度。 本文的主要研究結(jié)論如下： 一、根據(jù)不同行業(yè)的特點(diǎn)選取適當(dāng)?shù)呢攧?wù)預(yù)警指標(biāo)，并對預(yù)警指標(biāo)進(jìn)行篩選和精簡是建立有效預(yù)警模型的前提； 二、本文針對高新技術(shù)制造業(yè)上市公司構(gòu)建的短期財務(wù)預(yù)警模型和長期財務(wù)預(yù)警模型，均具有較高的預(yù)警精度，企業(yè)可以根據(jù)相應(yīng)指標(biāo)的變化及時了解財務(wù)狀況，，做出合理的判斷，最終通過理性決策來避免財務(wù)危機(jī)； 三、通過對財務(wù)預(yù)警指標(biāo)的時序數(shù)據(jù)進(jìn)行分析，將短期與長期結(jié)合、靜態(tài)與動態(tài)結(jié)合構(gòu)建的財務(wù)預(yù)警模型可以充分挖掘企業(yè)財務(wù)信息，及時有效的反映財務(wù)狀況的發(fā)展趨勢，實(shí)現(xiàn)企業(yè)財務(wù)危機(jī)動態(tài)預(yù)警； 四、合理集成各個單一預(yù)測方法的混合分析模型能夠發(fā)揮各個方法的優(yōu)勢，提高模型的泛化能力，是未來創(chuàng)新研究的趨勢。
[Abstract]:The high-tech manufacturing industry is the main force of the manufacturing industry, with the characteristics of high technology content and high added value. To study the financial early-warning of high-tech manufacturing enterprises can help high-tech manufacturing enterprises to avoid financial crisis. Safeguard the benign management of high-tech enterprises, and then promote the development of the national economy. This paper reviews and summarizes the domestic and foreign financial early warning research literature, compares and analyzes the advantages and disadvantages of all kinds of research, and thinks that the combination model based on artificial intelligence is an effective method of modern financial early warning research. When defining the concept of financial crisis, combined with the actual situation of listed companies in China, the company is regarded as the sign of financial crisis by ST (special treatment). Taking the listed high-tech manufacturing companies as the research object, according to the construction principle of early-warning index system and the industry characteristics of high-tech manufacturing enterprises, the financial early-warning index system suitable for high-tech manufacturing enterprises is constructed. Because the emergence of enterprise financial crisis is a continuous dynamic development process, this paper carries on the financial crisis early warning research to the high-tech manufacturing industry listed company from the short-term and the long-term angle. Among them, the short-term dynamic early warning is based on the quarterly financial panel data of the listed company, which is introduced into the dynamic model of comprehensive grey forecast GM (1Q1) and BP neural network to judge the financial situation of the company. The application of the model based on grey BP neural network model shows that the model can reflect the development trend of the company's financial situation effectively and has strong timeliness. Long-term dynamic early warning is based on the Logistic-BP neural network model, which introduces the financial panel data of the first two years and the first three years (T-2 and T-3) into the dynamic early warning system based on the Logistic-BP neural network model. Comparing the prediction results with general Logistic regression analysis and BP neural network model, it is proved that the early warning model of Logistic-BP neural network can better reflect the occurrence mechanism of financial crisis, and the application of the model is carried out with the listed company of Xinhua Pharmaceutical Company as an example. The results show the validity of the model and the high warning accuracy of the model. The main conclusions of this paper are as follows: first, selecting appropriate financial early-warning indicators according to the characteristics of different industries, and screening and streamlining the early-warning indicators is the premise of establishing an effective early-warning model; Second, the short-term financial early-warning model and the long-term financial early-warning model constructed by listed companies in high-tech manufacturing industries have high warning accuracy. Enterprises can understand the financial situation in time according to the changes of corresponding indicators. Make reasonable judgment and finally avoid financial crisis through rational decision; Thirdly, through the analysis of the time series data of the financial early-warning index, the financial early-warning model, which combines the short-term and long-term, static and dynamic combination, can fully excavate the financial information of the enterprise. Timely and effectively reflect the development trend of financial situation and realize the dynamic early warning of enterprise financial crisis; Fourthly, it is the trend of innovation research in the future that the hybrid analysis model with reasonable integration of each single prediction method can give play to the advantages of each method and improve the generalization ability of the model.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F406.7;F276.44
本文編號：2381522
[Abstract]:The high-tech manufacturing industry is the main force of the manufacturing industry, with the characteristics of high technology content and high added value. To study the financial early-warning of high-tech manufacturing enterprises can help high-tech manufacturing enterprises to avoid financial crisis. Safeguard the benign management of high-tech enterprises, and then promote the development of the national economy. This paper reviews and summarizes the domestic and foreign financial early warning research literature, compares and analyzes the advantages and disadvantages of all kinds of research, and thinks that the combination model based on artificial intelligence is an effective method of modern financial early warning research. When defining the concept of financial crisis, combined with the actual situation of listed companies in China, the company is regarded as the sign of financial crisis by ST (special treatment). Taking the listed high-tech manufacturing companies as the research object, according to the construction principle of early-warning index system and the industry characteristics of high-tech manufacturing enterprises, the financial early-warning index system suitable for high-tech manufacturing enterprises is constructed. Because the emergence of enterprise financial crisis is a continuous dynamic development process, this paper carries on the financial crisis early warning research to the high-tech manufacturing industry listed company from the short-term and the long-term angle. Among them, the short-term dynamic early warning is based on the quarterly financial panel data of the listed company, which is introduced into the dynamic model of comprehensive grey forecast GM (1Q1) and BP neural network to judge the financial situation of the company. The application of the model based on grey BP neural network model shows that the model can reflect the development trend of the company's financial situation effectively and has strong timeliness. Long-term dynamic early warning is based on the Logistic-BP neural network model, which introduces the financial panel data of the first two years and the first three years (T-2 and T-3) into the dynamic early warning system based on the Logistic-BP neural network model. Comparing the prediction results with general Logistic regression analysis and BP neural network model, it is proved that the early warning model of Logistic-BP neural network can better reflect the occurrence mechanism of financial crisis, and the application of the model is carried out with the listed company of Xinhua Pharmaceutical Company as an example. The results show the validity of the model and the high warning accuracy of the model. The main conclusions of this paper are as follows: first, selecting appropriate financial early-warning indicators according to the characteristics of different industries, and screening and streamlining the early-warning indicators is the premise of establishing an effective early-warning model; Second, the short-term financial early-warning model and the long-term financial early-warning model constructed by listed companies in high-tech manufacturing industries have high warning accuracy. Enterprises can understand the financial situation in time according to the changes of corresponding indicators. Make reasonable judgment and finally avoid financial crisis through rational decision; Thirdly, through the analysis of the time series data of the financial early-warning index, the financial early-warning model, which combines the short-term and long-term, static and dynamic combination, can fully excavate the financial information of the enterprise. Timely and effectively reflect the development trend of financial situation and realize the dynamic early warning of enterprise financial crisis; Fourthly, it is the trend of innovation research in the future that the hybrid analysis model with reasonable integration of each single prediction method can give play to the advantages of each method and improve the generalization ability of the model.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F406.7;F276.44
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