本文選題：財(cái)產(chǎn)保險(xiǎn) + 償付能力��； 參考：《浙江工商大學(xué)》2013年碩士論文

【摘要】：自上世紀(jì)80年代中國(guó)保險(xiǎn)業(yè)務(wù)重新恢復(fù)發(fā)展以來(lái),我國(guó)的保險(xiǎn)業(yè)一直處于快速發(fā)展階段。截至2010年保險(xiǎn)行業(yè)總資產(chǎn)達(dá)到4.9萬(wàn)億,我國(guó)已經(jīng)成為全球最重要的新興保險(xiǎn)大國(guó)。作為國(guó)民經(jīng)濟(jì)的重要組成部分,保險(xiǎn)業(yè)的穩(wěn)定發(fā)展對(duì)我國(guó)經(jīng)濟(jì)的良好發(fā)展起著重要的作用。保險(xiǎn)公司償付能力充足性,不僅影響保險(xiǎn)公司的持續(xù)經(jīng)營(yíng),還會(huì)影響中國(guó)保險(xiǎn)業(yè)和金融市場(chǎng)的穩(wěn)定發(fā)展。 目前我國(guó)建立了符合中國(guó)國(guó)情的以償付能力、市場(chǎng)行為、公司治理為三大支柱的保險(xiǎn)監(jiān)管體系,而償付能力監(jiān)管則居于監(jiān)管體系的核心地位。但是,截至2010年全國(guó)仍至少有5家財(cái)險(xiǎn)公司償付能力不足。如何發(fā)揮償付能力監(jiān)管在風(fēng)險(xiǎn)防范中的核心作用,是我國(guó)保險(xiǎn)業(yè)監(jiān)管的一項(xiàng)重要工作。建立一套靈敏的償付能力預(yù)警體系對(duì)完善我國(guó)的償付能力監(jiān)管體系具有重大意義。 本文對(duì)國(guó)內(nèi)外保險(xiǎn)償付能力影響因素和償付能力預(yù)測(cè)的研究文獻(xiàn)進(jìn)行了回顧和總結(jié),并比較了幾種典型的償付能力影響因素和償付能力預(yù)測(cè)的計(jì)量模型。實(shí)證過(guò)程選取了主成分分析法對(duì)影響償付能力的影響因素進(jìn)行分析,采用BP神經(jīng)網(wǎng)絡(luò)對(duì)償付能力進(jìn)行預(yù)測(cè)。BP神經(jīng)網(wǎng)絡(luò)模仿、簡(jiǎn)化和抽象生物大腦神經(jīng)系統(tǒng),能夠自身適應(yīng)環(huán)境、總結(jié)規(guī)律、完成某種運(yùn)算,有著傳統(tǒng)統(tǒng)計(jì)方法無(wú)法比擬的適應(yīng)性、容錯(cuò)性及自組織性等優(yōu)點(diǎn)。但是在BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)中,使用的指標(biāo)并不是越多越好,過(guò)多的指標(biāo)會(huì)造成BP神經(jīng)網(wǎng)絡(luò)在學(xué)習(xí)過(guò)程中受到過(guò)多的噪聲干擾,并且會(huì)由于隱含層過(guò)多,造成訓(xùn)練過(guò)度,從而影響預(yù)測(cè)的精度。在實(shí)證過(guò)程中,為了全面反映財(cái)險(xiǎn)公司的財(cái)務(wù)狀況,選取的財(cái)務(wù)指標(biāo)相對(duì)較多,并且指標(biāo)之間存在一定的相關(guān)性,反映的信息在一定程度上有重疊。因此在用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)之前,先利用主成分分析把多指標(biāo)轉(zhuǎn)化為少數(shù)幾個(gè)綜合指標(biāo)。 本文選取了33家在2007—2010年具有完整財(cái)務(wù)報(bào)表的財(cái)產(chǎn)保險(xiǎn)公司,根據(jù)其財(cái)務(wù)報(bào)表計(jì)算了衡量保險(xiǎn)公司償付能力的13個(gè)財(cái)務(wù)指標(biāo),利用主成分分析法得到6個(gè)主成分。本文把樣本分為訓(xùn)練組和檢驗(yàn)組。把主成分分析法得到的6個(gè)主成分作為輸入變量,以償付能力充足率作為輸出變量,對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行了訓(xùn)練。之后以償付能力充足率100%作為判定償付能力是否充足的標(biāo)準(zhǔn),利用BP神經(jīng)網(wǎng)絡(luò)對(duì)樣本公司未來(lái)一年和未來(lái)兩年的償付能力進(jìn)行預(yù)測(cè)研究。通過(guò)實(shí)證研究證明BP神經(jīng)網(wǎng)絡(luò)對(duì)償付能力不足的保險(xiǎn)公司預(yù)測(cè)正確率達(dá)到90%以上預(yù)測(cè)效果顯著。最后,根據(jù)實(shí)證結(jié)果,給出了完善BP神經(jīng)網(wǎng)絡(luò)模型在我國(guó)財(cái)產(chǎn)保險(xiǎn)償付能力預(yù)測(cè)運(yùn)用的若干建議。
[Abstract]:Since the reinstatement and development of China's insurance business in 1980's, China's insurance industry has been in the stage of rapid development. By 2010, the total assets of insurance industry has reached 4.9 trillion, China has become the most important emerging insurance country in the world. As an important part of the national economy, the steady development of the insurance industry plays an important role in the good economic development of our country. The adequacy of the solvency of an insurance company not only affects the continuing operation of the insurance company, but also affects the steady development of the insurance industry and the financial market in China. Corporate governance is a three-pillar insurance regulatory system, while solvency regulation is at the core of the regulatory system. However, as of 2010, there are still at least five financial insurance companies underpaid. How to play the central role of solvency regulation in risk prevention is an important work of insurance supervision in China. The establishment of a sensitive solvency warning system is of great significance to the improvement of China's solvency supervision system. This paper reviews and summarizes the research literature on the influencing factors and solvency prediction of insurance solvency both at home and abroad. Several typical influencing factors of solvency and the econometric models of solvency prediction are compared. The empirical process selects the principal component analysis method to analyze the influencing factors of solvency. BP neural network is used to predict the solvency. BP neural network is used to simulate and simplify and abstract the biological brain neural system, which can adapt itself to the environment. Summing up the rules and completing some operations have the advantages of adaptability, fault-tolerance and self-organization, which can not be compared with the traditional statistical methods. However, in the prediction of BP neural network, the more indexes are used, the better. Too many indexes will cause too much noise interference in the learning process of BP neural network, and it will cause excessive training because of too many hidden layers. Thus, the accuracy of prediction is affected. In the empirical process, in order to fully reflect the financial situation of property insurance companies, the financial indicators selected are relatively large, and there is a certain correlation between the indicators, and the information reflected is overlapped to a certain extent. Therefore, before using BP neural network to predict, the principal component analysis is used to transform multiple indexes into a few comprehensive indexes. In this paper, 33 property insurance companies with complete financial statements in 2007-2010 are selected. According to its financial statements, 13 financial indexes are calculated to measure the solvency of insurance companies, and six principal components are obtained by principal component analysis. The sample is divided into training group and test group. Taking the six principal components obtained by principal component analysis as input variables and solvency adequacy as output variables, BP neural network is trained. Then the solvency adequacy ratio of 100% is taken as the criterion to judge whether the solvency is sufficient or not. The BP neural network is used to predict the solvency of the sample company in the next year and the next two years. Through empirical research, it is proved that BP neural network has a remarkable effect on the forecasting accuracy of insurance companies with insufficient solvency to more than 90%. Finally, based on the empirical results, some suggestions on the application of BP neural network model in the prediction of property insurance solvency in China are given.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F842.3;F224

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