發(fā)布時(shí)間：2018-01-11 23:02

  本文關(guān)鍵詞：基于支持向量機(jī)的房地產(chǎn)上市公司財(cái)務(wù)危機(jī)預(yù)警研究　出處：《西南財(cái)經(jīng)大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 房地產(chǎn)上市公司 財(cái)務(wù)危機(jī)預(yù)警 支持向量機(jī) 因子分析

【摘要】：隨著市場(chǎng)經(jīng)濟(jì)的發(fā)展和金融制度的完善,通過(guò)上市實(shí)現(xiàn)融資的房地產(chǎn)公司業(yè)已達(dá)到139家。房地產(chǎn)上市公司經(jīng)營(yíng)業(yè)績(jī)的好壞與投資者收益的多少是息息相關(guān)的。通過(guò)研究上市公司財(cái)務(wù)報(bào)表,構(gòu)建財(cái)務(wù)危機(jī)預(yù)警模型對(duì)企業(yè)存在的財(cái)務(wù)風(fēng)險(xiǎn)進(jìn)行評(píng)估和預(yù)警,有利于企業(yè)及時(shí)發(fā)現(xiàn)風(fēng)險(xiǎn)、控制風(fēng)險(xiǎn),投資者合理投資、及時(shí)止損。 財(cái)務(wù)危機(jī)預(yù)警理論起源于西方,它通過(guò)利用企業(yè)財(cái)務(wù)數(shù)據(jù)對(duì)企業(yè)風(fēng)險(xiǎn)進(jìn)行識(shí)別,發(fā)現(xiàn)其潛在的財(cái)務(wù)風(fēng)險(xiǎn)提前進(jìn)行預(yù)警和控制。財(cái)務(wù)危機(jī)預(yù)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(gè)案分析法、標(biāo)準(zhǔn)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(jì)學(xué)理論和機(jī)器學(xué)習(xí)的發(fā)展,支持向量機(jī)作為一個(gè)新的方法被應(yīng)用到財(cái)務(wù)危機(jī)預(yù)警領(lǐng)域中來(lái)。 支持向量機(jī)本質(zhì)上是一個(gè)有約束的二次優(yōu)化問(wèn)題,作為監(jiān)督學(xué)習(xí)的一種,其在分類(lèi)和預(yù)測(cè)方面有著廣泛的應(yīng)用。由于它本身所具有的獨(dú)特優(yōu)勢(shì),支持向量機(jī)能夠有效地解決識(shí)別系統(tǒng)、信用評(píng)估等方面的問(wèn)題。本文借助支持向量機(jī)這種機(jī)器學(xué)習(xí)的方法來(lái)解決房地產(chǎn)上市公司的財(cái)務(wù)危機(jī)預(yù)警問(wèn)題,為公司財(cái)務(wù)危機(jī)預(yù)警提供了新思路、新方法。 本文建立在財(cái)務(wù)危機(jī)預(yù)警理論和支持向量機(jī)理論的基礎(chǔ)上,構(gòu)建基于支持向量機(jī)的財(cái)務(wù)危機(jī)預(yù)警模型,對(duì)房地產(chǎn)上市公司潛在的財(cái)務(wù)危機(jī)進(jìn)行預(yù)測(cè)。本文首先介紹了研究背景、研究問(wèn)題、研究意義與思路,接著對(duì)財(cái)務(wù)危機(jī)的基本定義和成因,財(cái)務(wù)危機(jī)預(yù)警的定義、意義和方法進(jìn)行闡述,介紹了國(guó)內(nèi)外學(xué)界在財(cái)務(wù)危機(jī)預(yù)警領(lǐng)域的研究成果。然后著重介紹了機(jī)器學(xué)習(xí)和支持向量機(jī)的基本概念和理論基礎(chǔ),對(duì)線性支持向量機(jī)和非線性支持向量機(jī)各自的算法進(jìn)行了闡述。接下來(lái)建立了財(cái)務(wù)預(yù)警指標(biāo)體系,對(duì)數(shù)據(jù)進(jìn)行標(biāo)準(zhǔn)化處理后進(jìn)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(jī),并且對(duì)比了不同參數(shù)值下的模型預(yù)測(cè)結(jié)果。 為了更好地體現(xiàn)支持向量機(jī)在財(cái)務(wù)危機(jī)預(yù)警精度方面的優(yōu)勢(shì),本文還采用了判別分析和Logistic回歸模型分別對(duì)樣本數(shù)據(jù)進(jìn)行財(cái)務(wù)危機(jī)預(yù)警,將其得到的預(yù)測(cè)結(jié)果與支持向量機(jī)的預(yù)測(cè)結(jié)果進(jìn)行比較發(fā)現(xiàn),支持向量機(jī)的預(yù)測(cè)結(jié)果要明顯優(yōu)于判別分析和Logistic回歸的預(yù)測(cè)結(jié)果。 借助本文構(gòu)建的財(cái)務(wù)危機(jī)預(yù)警模型,我們可以利用房地產(chǎn)上市公司往年的財(cái)務(wù)數(shù)據(jù)對(duì)其未來(lái)發(fā)生財(cái)務(wù)危機(jī)的概率進(jìn)行預(yù)測(cè)。對(duì)于發(fā)生財(cái)務(wù)危機(jī)概率高的企業(yè)投資者應(yīng)該重點(diǎn)關(guān)注,謹(jǐn)慎投資；這些企業(yè)的管理者應(yīng)該對(duì)企業(yè)存在的問(wèn)題及時(shí)進(jìn)行梳理解決,做到有效控制其財(cái)務(wù)風(fēng)險(xiǎn)。
[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(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F299.233.42

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