發(fā)布時間：2018-01-11 23:02

  本文關(guān)鍵詞：基于支持向量機的房地產(chǎn)上市公司財務危機預警研究　出處：《西南財經(jīng)大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 房地產(chǎn)上市公司 財務危機預警 支持向量機 因子分析

【摘要】：隨著市場經(jīng)濟的發(fā)展和金融制度的完善,通過上市實現(xiàn)融資的房地產(chǎn)公司業(yè)已達到139家。房地產(chǎn)上市公司經(jīng)營業(yè)績的好壞與投資者收益的多少是息息相關(guān)的。通過研究上市公司財務報表,構(gòu)建財務危機預警模型對企業(yè)存在的財務風險進行評估和預警,有利于企業(yè)及時發(fā)現(xiàn)風險、控制風險,投資者合理投資、及時止損。 財務危機預警理論起源于西方,它通過利用企業(yè)財務數(shù)據(jù)對企業(yè)風險進行識別,發(fā)現(xiàn)其潛在的財務風險提前進行預警和控制。財務危機預警的方法大致可分為定性分析法和定量分析法兩種。定性分析法主要包括個案分析法、標準化調(diào)查法等,定量分析法主要包括單變量模型、多變量模型等。隨著統(tǒng)計學理論和機器學習的發(fā)展,支持向量機作為一個新的方法被應用到財務危機預警領(lǐng)域中來。 支持向量機本質(zhì)上是一個有約束的二次優(yōu)化問題,作為監(jiān)督學習的一種,其在分類和預測方面有著廣泛的應用。由于它本身所具有的獨特優(yōu)勢,支持向量機能夠有效地解決識別系統(tǒng)、信用評估等方面的問題。本文借助支持向量機這種機器學習的方法來解決房地產(chǎn)上市公司的財務危機預警問題,為公司財務危機預警提供了新思路、新方法。 本文建立在財務危機預警理論和支持向量機理論的基礎上,構(gòu)建基于支持向量機的財務危機預警模型,對房地產(chǎn)上市公司潛在的財務危機進行預測。本文首先介紹了研究背景、研究問題、研究意義與思路,接著對財務危機的基本定義和成因,財務危機預警的定義、意義和方法進行闡述,介紹了國內(nèi)外學界在財務危機預警領(lǐng)域的研究成果。然后著重介紹了機器學習和支持向量機的基本概念和理論基礎,對線性支持向量機和非線性支持向量機各自的算法進行了闡述。接下來建立了財務預警指標體系,對數(shù)據(jù)進行標準化處理后進行因子分析,提取主因子后構(gòu)建基于不同參數(shù)值的非線性支持向量機,并且對比了不同參數(shù)值下的模型預測結(jié)果。 為了更好地體現(xiàn)支持向量機在財務危機預警精度方面的優(yōu)勢,本文還采用了判別分析和Logistic回歸模型分別對樣本數(shù)據(jù)進行財務危機預警,將其得到的預測結(jié)果與支持向量機的預測結(jié)果進行比較發(fā)現(xiàn),支持向量機的預測結(jié)果要明顯優(yōu)于判別分析和Logistic回歸的預測結(jié)果。 借助本文構(gòu)建的財務危機預警模型,我們可以利用房地產(chǎn)上市公司往年的財務數(shù)據(jù)對其未來發(fā)生財務危機的概率進行預測。對于發(fā)生財務危機概率高的企業(yè)投資者應該重點關(guān)注,謹慎投資；這些企業(yè)的管理者應該對企業(yè)存在的問題及時進行梳理解決,做到有效控制其財務風險。
[Abstract]:With the development of market economy and financial system, through the implementation of the listed financing Real Estate Company has reached 139. How many quality and investors operating performance of real estate listed companies are closely related. Through the study of the financial statements of listed companies, construct the financial crisis early-warning model for assessment and early warning of enterprise financial risk exists in favor of enterprise timely detection of risk, control risk, investors rational investment, timely stop.
The origin of financial crisis early-warning theory in the west, which based on the enterprise risk identification using the financial data of the enterprise, find the potential financial risk early warning and control method. The financial crisis early warning can be divided into qualitative analysis method and quantitative analysis method. Two kinds of qualitative analysis methods mainly include case analysis, standardized survey method, quantitative analysis method mainly includes single variable model, multi variable model. With the development of the theory of statistics and machine learning, support vector machine is used as a new method has been applied to the field of financial crisis early warning.
Support vector machine is essentially a constrained optimization problem two times, as a supervised learning, which is widely used in classification and prediction. Because its itself has the unique superiority, the support vector machine can effectively solve the recognition system, credit evaluation and other aspects of the problem. In this paper, with the support of this method of vector machine learning to solve the financial crisis early warning of listed real estate companies, provides new ideas and new methods for early warning of financial crisis.
This paper is based on the financial crisis early warning theory and the support vector machine theory, construct the financial crisis early-warning model based on support vector machine, the forecast of real estate listed companies' potential financial crisis. This paper firstly introduces the research background, research questions, research significance and ideas, then the basic definition and the causes of the financial crisis and the definition of the financial crisis early warning, significance and methods are presented in this paper, introduces the research results of domestic and foreign scholars in the field of financial crisis. Then it focuses on the machine learning support vector machine and the basic concepts and theoretical basis of linear support vector machine and support vector machine algorithm of nonlinear respectively are discussed. Next the establishment of the financial early-warning index system, factor analysis on standardized data, extract the main factor after the construction of non linear parameter values based on different support Vector machines, and compare the model prediction results under different parameter values.
In order to better reflect the advantages of support vector machine in the financial crisis early warning accuracy, this paper also uses discriminant analysis and Logistic regression model were used to the financial crisis early warning of the sample data, the prediction results obtained by the support vector machine and comparing the results, the prediction results of support vector machine to predict the outcome of superior discrimination analysis and Logistic regression.
With the help of the financial crisis early-warning model constructed in this paper, we can utilize the financial data of listed real estate companies in previous years to predict the probability of future financial crisis. The financial crisis occurred with high probability of enterprise investors should focus on, prudent investment; the management of these companies should be on business problems to sort out and resolve in a timely manner, do the effective control of the financial risk.

【學位授予單位】：西南財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F299.233.42

【參考文獻】

相關(guān)期刊論文 前10條

1 張根明;向曉驥;孫敬宜;;基于BP神經(jīng)網(wǎng)絡的制造業(yè)上市公司財務預警[J];山東工商學院學報;2006年04期

2 梁琪;企業(yè)信用風險的主成分判別模型及其實證研究[J];財經(jīng)研究;2003年05期

3 盧永艷;王維國;;財務困境預測中的變量篩選——基于平均影響值的SVM方法[J];系統(tǒng)工程;2011年08期

4 姚宏善,沈軼;用遺傳神經(jīng)網(wǎng)絡模型預測公司財務困境[J];華中師范大學學報(自然科學版);2005年02期

5 吳世農(nóng),盧賢義;我國上市公司財務困境的預測模型研究[J];經(jīng)濟研究;2001年06期

6 周首華,楊濟華,王平;論財務危機的預警分析——F分數(shù)模式[J];會計研究;1996年08期

7 蔡志岳;吳世農(nóng);;董事會特征影響上市公司違規(guī)行為的實證研究[J];南開管理評論;2007年06期

8 吳崇明;王曉丹;白冬嬰;張宏達;;利用KKT條件與類邊界包向量的SVM增量學習算法[J];計算機工程與設計;2010年08期

9 張玲;財務危機預警分析判別模型[J];數(shù)量經(jīng)濟技術(shù)經(jīng)濟研究;2000年03期

10 常玉清;王福利;王小剛;呂哲;;基于支持向量機的軟測量方法及其在生化過程中的應用[J];儀器儀表學報;2006年03期

，

本文編號：1411590