本文選題：財產(chǎn)保險 + 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年代中國保險業(yè)務(wù)重新恢復(fù)發(fā)展以來,我國的保險業(yè)一直處于快速發(fā)展階段。截至2010年保險行業(yè)總資產(chǎn)達(dá)到4.9萬億,我國已經(jīng)成為全球最重要的新興保險大國。作為國民經(jīng)濟的重要組成部分,保險業(yè)的穩(wěn)定發(fā)展對我國經(jīng)濟的良好發(fā)展起著重要的作用。保險公司償付能力充足性,不僅影響保險公司的持續(xù)經(jīng)營,還會影響中國保險業(yè)和金融市場的穩(wěn)定發(fā)展。 目前我國建立了符合中國國情的以償付能力、市場行為、公司治理為三大支柱的保險監(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年全國仍至少有5家財險公司償付能力不足。如何發(fā)揮償付能力監(jiān)管在風(fēng)險防范中的核心作用,是我國保險業(yè)監(jiān)管的一項重要工作。建立一套靈敏的償付能力預(yù)警體系對完善我國的償付能力監(jiān)管體系具有重大意義。 本文對國內(nèi)外保險償付能力影響因素和償付能力預(yù)測的研究文獻(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)測的計量模型。實證過程選取了主成分分析法對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)對償付能力進(jìn)行預(yù)測。BP神經(jīng)網(wǎng)絡(luò)模仿、簡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、完成某種運算,有著傳統(tǒng)統(tǒng)計方法無法比擬的適應(yīng)性、容錯性及自組織性等優(yōu)點。但是在BP神經(jīng)網(wǎng)絡(luò)預(yù)測中,使用的指標(biāo)并不是越多越好,過多的指標(biāo)會造成BP神經(jīng)網(wǎng)絡(luò)在學(xué)習(xí)過程中受到過多的噪聲干擾,并且會由于隱含層過多,造成訓(xùn)練過度,從而影響預(yù)測的精度。在實證過程中,為了全面反映財險公司的財務(wù)狀況,選取的財務(wù)指標(biāo)相對較多,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)測之前,先利用主成分分析把多指標(biāo)轉(zhuǎn)化為少數(shù)幾個綜合指標(biāo)。 本文選取了33家在2007—2010年具有完整財務(wù)報表的財產(chǎn)保險公司,根據(jù)其財務(wù)報表計算了衡量保險公司償付能力的13個財務(wù)指標(biāo),利用主成分分析法得到6個主成分。本文把樣本分為訓(xùn)練組和檢驗組。把主成分分析法得到的6個主成分作為輸入變量,以償付能力充足率作為輸出變量,對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)對樣本公司未來一年和未來兩年的償付能力進(jìn)行預(yù)測研究。通過實證研究證明BP神經(jīng)網(wǎng)絡(luò)對償付能力不足的保險公司預(yù)測正確率達(dá)到90%以上預(yù)測效果顯著。最后,根據(jù)實證結(jié)果,給出了完善BP神經(jīng)網(wǎng)絡(luò)模型在我國財產(chǎn)保險償付能力預(yù)測運用的若干建議。
[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)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F842.3;F224

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