本文選題：數(shù)據(jù)挖掘 + 稅收分析　； 參考：《財(cái)政部財(cái)政科學(xué)研究所》2014年碩士論文

【摘要】：隨著人工智能和現(xiàn)代信息技術(shù)的不斷發(fā)展,人類(lèi)社會(huì)邁入了信息化時(shí)代,大量的信息充斥于各行各業(yè)。通過(guò)稅收信息化系統(tǒng)的建設(shè)和推廣應(yīng)用,我國(guó)稅務(wù)部門(mén)掌握了龐雜的能反應(yīng)國(guó)民經(jīng)濟(jì)運(yùn)行情況的原始稅務(wù)數(shù)據(jù)。對(duì)這些數(shù)據(jù)進(jìn)行分析,發(fā)現(xiàn)其所蘊(yùn)含的有價(jià)值的潛在信息,有助于政府部門(mén)對(duì)社會(huì)主義市場(chǎng)經(jīng)濟(jì)進(jìn)行宏觀指導(dǎo)。 我國(guó)傳統(tǒng)的稅收分析工作過(guò)于簡(jiǎn)單,僅限于一些定性的描述和同比數(shù)據(jù)分析,稅收分析報(bào)告的內(nèi)容不能滿足各界人士的使用需求。國(guó)家稅收征管系統(tǒng)中的大量數(shù)據(jù)沒(méi)有得到有效的挖掘,不能傳遞出有用的信息,造成了信息資源的浪費(fèi)。 數(shù)據(jù)挖掘技術(shù)的發(fā)展為解決這種困境提供了技術(shù)方法。本文在學(xué)習(xí)數(shù)據(jù)挖掘理論和常用算法的基礎(chǔ)上,結(jié)合我國(guó)現(xiàn)行稅收分析工作的主要內(nèi)容,提出了可以利用回歸預(yù)測(cè)、K-均值聚類(lèi)算法和因子分析對(duì)傳統(tǒng)的稅收分析報(bào)告做出一定的改進(jìn),并在之后的實(shí)證分析中驗(yàn)證了方法的可行性。 首先利用回歸分析對(duì)稅收收入總量進(jìn)行曲線擬合,發(fā)現(xiàn)我國(guó)稅收收入總量的增長(zhǎng)呈現(xiàn)出指數(shù)曲線的趨勢(shì)；其次利用回歸分析對(duì)稅收收入總量和國(guó)內(nèi)生產(chǎn)總值進(jìn)行對(duì)數(shù)曲線擬合,回歸曲線的自變量系數(shù)就是稅收彈性的估計(jì)值；再利用K-均值聚類(lèi)算法對(duì)我國(guó)省級(jí)行政單位進(jìn)行聚類(lèi)分析,分類(lèi)依據(jù)是省級(jí)單位分稅種稅收收入,分類(lèi)結(jié)果將我國(guó)省級(jí)行政單位劃分了三大類(lèi),且經(jīng)濟(jì)意義較為顯著；然后利用因子分析對(duì)上市公司的財(cái)務(wù)指標(biāo)進(jìn)行降維處理,目的是利用因子得分對(duì)企業(yè)所得稅稅源進(jìn)行監(jiān)控,結(jié)論是因子得分位于中間段的企業(yè),財(cái)務(wù)報(bào)表被操縱導(dǎo)致稅源不穩(wěn)定的可能性比較大；最后利用同比增長(zhǎng)平均值和對(duì)數(shù)回歸曲線對(duì)稅收收入進(jìn)行預(yù)測(cè),相較而言,指數(shù)曲線的預(yù)測(cè)效果較好。 我國(guó)的稅收信息化建設(shè)已取得一定成就,將數(shù)據(jù)挖掘技術(shù)引入到稅收分析中有助于進(jìn)一步提升稅收管理工作的質(zhì)量和效率。
[Abstract]:With the continuous development of artificial intelligence and modern information technology, human society has entered the information age, a large number of information flooded in various industries. Through the construction and popularization of the tax information system, the tax authorities of our country have grasped a lot of original tax data which can reflect the operation of the national economy. Through the analysis of these data, the valuable potential information contained in the data is found, which is helpful for government departments to guide the socialist market economy macroscopically. The traditional tax analysis work in our country is too simple, limited to some qualitative description and comparative data analysis, and the contents of tax analysis report can not meet the needs of people from all walks of life. A large number of data in the national tax collection and management system have not been effectively mined and can not transmit useful information, resulting in a waste of information resources. The development of data mining technology provides a technical method to solve this dilemma. On the basis of studying the theory of data mining and common algorithms, this paper combines the main contents of the current tax analysis work in our country. It is proposed that the traditional tax analysis report can be improved by using regression prediction K-means clustering algorithm and factor analysis, and the feasibility of the method is verified in the later empirical analysis. First, the regression analysis is used to fit the total tax revenue, and it is found that the growth of the total tax revenue in China shows an exponential curve trend; secondly, the logarithmic curve fitting of the total tax revenue and GDP is carried out by using the regression analysis. The independent variable coefficient of the regression curve is the estimated value of tax elasticity, and then using the K-means clustering algorithm to cluster the provincial administrative units in China, the classification is based on the tax revenue of the provincial units. The classification result divides the provincial administrative units of our country into three categories, and the economic significance is significant. Then, the factor analysis is used to reduce the dimension of the financial indexes of the listed companies, in order to monitor the enterprise income tax sources by using the factor score. The conclusion is that if the factor score is in the middle, the possibility of the financial statements being manipulated will lead to the instability of the tax source. Finally, the average growth and logarithmic regression curve are used to forecast the tax revenue, compared with the average value of the annual growth and the logarithmic regression curve. The prediction effect of exponential curve is better. Some achievements have been made in the construction of tax information in our country. The introduction of data mining technology into tax analysis will help to further improve the quality and efficiency of tax management.
【學(xué)位授予單位】：財(cái)政部財(cái)政科學(xué)研究所
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP311.13;F812.42

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本文編號(hào)：1961928