發(fā)布時間：2018-07-09 19:36

  本文選題：預測 + 審計意見��； 參考：《重慶理工大學》2017年碩士論文

【摘要】：上市公司定期公布的財務報告是利益相關者進行決策的依據(jù),注冊會計師作為獨立的第三方對被審計單位財務報告進行審計出具的鑒證性審計意見,對增強上市公司財務信息的可信性起著至關重要的作用,經(jīng)審計后的財務報表更可靠,使信息使用者能更加有信心,能減少投資決策失誤,注冊會計師出具的審計意見類型對公司利益相關者的投資決策行為具有重大影響,因此對上市公司審計意見預測模型的研究具有一定的現(xiàn)實意義。本文構建了基于灰色神經(jīng)網(wǎng)絡的上市公司審計意見預測模型,為保證預測效果,在以往研究大多使用財務指標的基礎上,建立了包括財務指標和公司治理、會計師事務所等非財務指標的預測指標體系。選取2013-2015年滬深兩市全部A股制造業(yè)被出具非標準審計意見的上市公司作為研究樣本,根據(jù)公司的資產(chǎn)規(guī)模、行業(yè)以及1:1配比原則,并剔除缺失值之后,最終得到訓練組、檢驗組以及預測組樣本;為降低訓練的復雜性,保證預測效果,采用鄰域粗糙集方法對樣本指標進行篩選,在不影響預測效果的情況下,剔除次要指標,以保留相對重要指標;利用BP神經(jīng)網(wǎng)絡方法對訓練組樣本數(shù)據(jù)進行訓練建模及回判仿真,并利用檢驗組數(shù)據(jù)對所建立的模型進行檢驗;選取預測組樣本時點數(shù)據(jù),構建基于灰色神經(jīng)網(wǎng)絡方法的審計意見預測模型,該模型綜合了灰色預測模型建模所需信息較少及神經(jīng)網(wǎng)絡模型自適應、自學習的優(yōu)點。研究結果表明,加入非財務指標的綜合預測模型的預測準確度高于僅用財務指標建模的預測準確度,構建的基于灰色神經(jīng)網(wǎng)絡方法的審計意見預測模型具有較好的預測能力。
[Abstract]:The financial reports issued regularly by listed companies are the basis for stakeholders to make decisions. Certified public accountants, as independent third parties, audit the financial reports of audited units and issue an authenticated audit opinion. It plays an important role in enhancing the credibility of financial information of listed companies. The audited financial statements are more reliable, so that the information users can have more confidence and can reduce the mistakes in investment decisions. The type of audit opinion issued by CPA has great influence on the investment decision-making behavior of corporate stakeholders, so it is of practical significance to study the prediction model of audit opinion of listed companies. In this paper, a forecasting model of audit opinion of listed companies based on grey neural network is constructed. In order to ensure the forecasting effect, the financial indexes and corporate governance are established on the basis of the financial indicators used in most previous studies. Accounting firms and other non-financial indicators of the forecast index system. From 2013 to 2015, all the A-share manufacturing companies in Shanghai and Shenzhen stock markets were selected as the research samples. According to the company's asset size, industry and 1:1 matching principle, and after eliminating the missing value, the training group was finally obtained. In order to reduce the complexity of training and ensure the prediction effect, the neighborhood rough set method is used to screen the sample index, and the secondary index is eliminated without affecting the prediction effect, so as to retain the relative important index. BP neural network method is used for training modeling and simulation of training group sample data, and the test group data is used to test the established model, and the prediction group sample time point data is selected. An audit opinion prediction model based on grey neural network is constructed. The model combines the advantages of less information needed in grey prediction model and adaptive and self-learning of neural network model. The research results show that the prediction accuracy of the comprehensive forecasting model with non-financial indexes is higher than that of only using the financial indicators, and the prediction model based on the grey neural network method has a better forecasting ability.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F239.4

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本文編號：2110438