本文選題：BP神經(jīng)網(wǎng)絡(luò)　切入點：VaR方法　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著中國證券市場改革的深入,我國主板市場新股發(fā)行定價的準(zhǔn)確性也愈發(fā)重要。由于每一個國家的證券市場都具有不可完全復(fù)制性,因此在探索與借鑒的基礎(chǔ)上,找到適合我國主板市場的定價方法,尋求合理的股票價格成為本文的核心問題。本文首先研究傳統(tǒng)定價方法,并比較它們之間的優(yōu)缺點。在此基礎(chǔ)上,尋找理論與方法的突破,研究發(fā)現(xiàn)傳統(tǒng)方法已經(jīng)不適合現(xiàn)有的中國主板市場,而期權(quán)、風(fēng)險、神經(jīng)網(wǎng)絡(luò)、博弈等概念的引入給予新股發(fā)行定價一個進(jìn)步的空間。對于主板市場的定價,BP神經(jīng)網(wǎng)絡(luò)方法相對適用,它可以使誤差率達(dá)到15%左右,但是,我們認(rèn)為,精確的風(fēng)險評估與因素選取可以使定價的準(zhǔn)確性提高,因此我們引入風(fēng)險評估并對因素進(jìn)行選取。同時,各國上市環(huán)境的差異是造成定價方法不能直接生搬硬套的另一個原因,所以篩選比較具有代表性的美國、新加坡、日本和德國這4個國家,與我國進(jìn)行上市制度的比較。本文探索BP神經(jīng)網(wǎng)絡(luò)與VaR風(fēng)險定價結(jié)合的方法來進(jìn)行股票定價,并得出CVaR比VaR方法更能準(zhǔn)確衡量風(fēng)險的結(jié)論,因為前者誤差率較低,平均僅達(dá)到12.69%,較以前的BP神經(jīng)網(wǎng)絡(luò)方法降低2%以上。同時加入政府制度的因素:將不可量化的政府制度作為上市成本因素,且利用歸一化方法統(tǒng)一不同的量綱。在深入研究中國、美國、新加坡、日本、德國這五個國家的上市制度的基礎(chǔ)上,利用改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)一步降低誤差率至11.96%,使定價更有效,這也是上市時間最短、上市資金最少、監(jiān)管力度最強的德國政府制度下的定價模型所得到的誤差率,同時我們得出監(jiān)管力度是上市時間、上市資金和監(jiān)管力度這三個因素中最重要的因素的結(jié)論,對中國政府進(jìn)一步改革上市制度有很大的助益。
[Abstract]:With the deepening of the reform of China stock market, IPO pricing accuracy of the motherboard market shares in China is increasingly important. Because of each country's securities market has not completely copied, so the exploration and based on the reference, to find suitable pricing methods in China stock market, to find a reasonable stock price has become the core issue in this paper. This paper studies the traditional pricing method, and compare the advantages and disadvantages between them. On this basis, the theory and method of looking for breakthrough, research found that the traditional method is not suitable for the existing Chinese motherboard market, and option, risk, neural network, introduce the game concept to IPO pricing a progress space for the motherboard market pricing, BP neural network method for it can reduce the error rate of about 15%, but we believe that accurate risk assessment and selection factors To improve the pricing accuracy, so we introduce the risk assessment and the factors were selected. At the same time, the differences of the listed environment is another cause of the pricing method cannot be directly applied mechanically, so we compared the representative of the United States, Singapore, Japan and Germany in the 4 countries, compared with the listing system I China. This paper explores method combined with BP neural network and VaR pricing risk for stock pricing, and that CVaR can measure the risk more accurately than the VaR method because the former conclusion, the error rate is low, the average reached only 12.69%, compared with the previous methods of BP neural network is reduced by more than 2%. At the same time to join the government: system factors the non quantifiable government system as listed cost factors, and using the normalization method of different dimension unity. In the study China, America, Singapore, Japan, the five countries of Germany Based on the market system, to further reduce the error rate to 11.96% by using the improved BP neural network model, the pricing is more effective, which is listed in the shortest time, the least error listed funds, pricing model of the German government supervision system under the strongest rate, at the same time, we come to the conclusion that supervision is time to market factors the most important of the three factors listed funds and supervision of the conclusions are of great help to the further reform of listed China government system.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51

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本文編號：1556493