【作者】 郑立琴；

【导师】 龙胜平；

【作者基本信息】 华东师范大学 ， 企业管理， 2007， 硕士

【摘要】 众所周知,房地产业同其他行业相比,具有典型的高投资、高风险、周期长、高收益的特点。由于我国房地产业起步晚、基础差、规模小、行业尚未实现规范化,加上我国金融体系的不完善,使得不少房地产开发企业有较高的预收账款,较高的借款费用,资产负债率在70%以上,有的甚至高达90%,负债比率过高,要求企业提高偿债能力,这会在一定程度上制约企业的经营活动,从而影响企业的盈利能力,甚至有可能导致企业在某个时候面临资金短缺、入不敷出的风险。从而使企业承受着巨大的财务风险,以至于在实际投资中,众多房地产开发项目因无力偿还到期银行债务而停工,导致“烂尾楼”现象时有发生。如何防范房地产企业陷入财务危机是当前急需解决的问题。企业财务风险预警一直是国内外学者研究的热点,但绝大多数都是针对上市公司而研究的,极少预警研究会专注于某一具体行业。在学习了国内外先进的预警理论后,本文首次将财务预警研究应用于我国的房地产业,根据我国房地产企业自身特点及国内宏观经济环境的特点,以房地产上市企业为研究样本,在前人研究的基础上,以主成分分析和Logistic回归分析相结合的方法,构建起房地产企业财务风险预警模型。Logistic回归分析具有预测精度高、不需要严格的假设条件、自变量不需要满足多元正态分布等优点,已经广泛应用于财务预警研究中,但它也存在一定的缺点,如不能解决多重共线性、多维等问题。为此,本文引入主成分分析法,从而解决了共线性问题并从众多变量中提取少量主成分因子,弥补了Logistic回归分析的不足。这样,构建的模型即不会使大量信息丢失,又能很好地解释企业的财务状况。实证研究表明,影响我国房地产企业财务状况好坏的因素主要是盈利能力因素、综合能力因素、现金流量因素和发展能力因素,其中影响其财务状况的主要财务指标为:流动比率、股东权益周转率、总资产周转率、净利润增长率、净资产增长率、主营业务利润率、总资产收益率、净资产收益率、每股收益、现金比率、可持续增长率。该预警模型的总体预测效果为94.737%,模型的拟和程度高,说明该预警模型具有较高的实用价值,能够很好的判断房地产企业的财务状况,并据此作为企业采取措施的有力依据。因此,该预警模型体现行业特色,实用性强,可用以预测我国房地产开发企业的财务危机,以期减少企业破产造成的损失,具有较强的现实意义。财务危机预警模型将会有广泛的应用前景和扩展空间,具体表现在以下几个方面:1.预警模型能够给投资者更多的预测性,能反映企业真实价值的信息,投资者及早做出决策,规避风险。2.该预警模型能够帮助房地产企业提高自身防范风险的能力,及早发现财务恶化的征兆,采取有效措施避开或化解可能出现的财务危机。3.银行可以利用该预警模型帮助做出贷款决策,并对房地产企业进行贷款或质押贷款控制,避免贷款风险,同时也增强整个国民经济运行发展的安全性。4.该预警模型能够帮助审计人员确定审计范围,制定必要的审计程序,并帮助判断被审计单位是否能持续经营或保持良好的前景,进而提高审计人员的评估能力,降低审计风险。特别是审计人员与客户发生意见分歧是,审计人员有客观分析模型加以支持,客户往往更容易接受审计意见。5.该预警模型还可以帮助改善资源分配和控制投资风险。另外,信用担保机构也可以凭预警信号提醒客户注意,并有可能提供解决措施和采取行动以帮助客户避免危机的发生。此外,本文还存在以下几个特点:第一,从数据采集上看,本文采集了大量的数据,但都是来自房地产上市企业的财务报表,未能包含大量的中小型企业,这样会造成研究结果的片面性,但该模型也能对中小型企业的财务预警起到很好的参考作用。在后续研究中,应从多角度研究,进一步验证该模型的实用性。第二,本研究建立的财务预警模型主要以财务指标作为输入变量进行判别和预警,利用了上市公司直接公布和本人间接计算的财务指标。模型只涉及到会计数据和财务比率,没有考虑到非量化因素。而在揭露财务状况方面,非量化因素可能要比财务指标更可靠、更有效。比如,企业过分依赖银行贷款、行业不景气、企业市场定位不明确等都预示着企业存在潜在的财务危机,但财务比率无法反映。所以模型仅能从一方面预测房地产企业的财务状况。在今后的研究中,要注意在财务指标的选取上,既要涉及定量信息,又要涉及定性信息,将定性信息采用评分的方法,以评分的结果作为预警的一个部分,使得预警模型涵盖范围更广,预警能力更健全。第三,受财务资料保密的限制,本人只能根据上市公司的财务报表作为分析数据。财务预警模型的构建要求上市公司公布的财务数据必须是真实可靠的,但由于目前上市公司会计信息失真现象依然存在,企业财务数据存在被操纵的可能性。对于那些可以操纵财务数据的上市公司,财务预警模型无法进行预警。尽管如此,基于主成分分析的Logistic回归分析所构建的财务预警模型所提供的分析思路仍有较强的指导意义,并且随着会计信息真实性的提高以及科学的发展,该模型的运用前景将更为广泛。本文共分为五章,具体如下:第一章,绪论。主要介绍财务预警的含义,以及房地产企业建立财务预警模型的必要性,系统回顾和总结国内外的研究成果。在对这些成果进行评析的基础上,提出本文的观点,即基于主成分分析的Logistic回归分析进行多变量的财务预警模型研究。第二章,房地产企业财务风险概述。结合房地产企业自身的特点,及我国宏观经济政策和行业生命周期等因素,阐述我国房地产企业面临的财务风险。导致房地产企业财务风险主要得主要因素分为客观因素和主观因素。客观因素主要包括:周期风险、法律政策环境、利率风险、通货膨胀风险、市场供求风险、变现风险等。而主观因素主要跟企业经营特点、资金结构、财务管理水平、风险意识等有关。所有这些风险都贯穿在企业的筹资、投资、资金回收和收益分配等活动中。第三章,研究模型概述。系统的解释本研究所需要采用的两个研究方法:主成分分析法和Logistic回归分析法,并说明模型构建的思路:样本选取-财务指标的确定-T检验-多重共线性检验-主成分分析-Logistic回归分析-模型构建-模型有效性检验。第四章,房地产上市公司财务预警模型的构建及预警效果分析。以我国78家房地产上市企业作为研究样本,其中35家为财务危机企业,41家为正常企业;考虑到我国房地产企业的实际情况,选取了样本具体数据,期限为一年,利用主成分分析法提取影响房地产企业财务状况的主成分因子,在此基础上通过Logistic回归分析提取出更少的影响因素,并形成最终的预警模型。最后,对预警模型进行预测效果的检验和拟和优度的检验,从而论证预警模型的实用价值。第五章,研究结论和应用前景。总结本文的研究成果和应用前景,阐述本文的创新点以及存在的不足。更多还原

【Abstract】 The Real Estate Industry, as is known to all, is characterized by huge investment, high risks, high returns and a prolonged cycle. In China the industry is still quite young; besides, the domestic financial system is not sound yet; as a result, the industry is facing such problems as small scales and a poor system of industrial standards. For example, quite a few real estate developers have a higher proportion of advance receipt account, a high loan-to-value ratio which leads to an above 70% debt-to-assets ratio, sometimes even as high as 90%. The high indebtness ratio has brought about enormous financial risks for the developers, resulting in many unfinished projects in many cases. Hence, the urgent problem is how to prevent real estate companies from such financial crises."The Warning System against Corporate Financial Risks" has been a hot topic in recent years, on which studies from home and abroad are abundant in number. Yet the majority of researchers have focused on companies with public stocks in the stock markets, and few have paid attention to one specific line of business. After absorbing some advanced theories of warning system, the author of this dissertation will be the first to apply the financial warning system theory to the Real Estate Industry in China. A model of a warning system against financial risks designed for real estate corporations will be constructed, based on the features of domestic companies and the macro economic climate. Some cases of real estate companies with public stocks will be quoted as samples. The author would like to employ a combination of analytical methods, Principal Components Analysis and a Logistic Regression Analysis. The selection of the Logistic Retrospective Analytical Method is supported by various advantages of the method itself. It is highly precise and requires few hypotheses; the variants don’t have to fall into the normal deviation scope. The method has already been widely used in the related studies. However, certain shortcomings are inevitable, such as its inability to solve multi-collinearity and multi-dimensional problems. Therefore, Principal Components Analysis is jointly employed to make up for the deficiency. The two methods together will ensure the integrity of statistics, and well explain the financial status of corporations.The dissertation is comprised of five chapters,Chapter 1 Introduction; The definition and the necessity of a Warning System against Financial Risks will be given, based on the review of related literature of home and abroad. The main purpose of the dissertation is to study the model of Warning System by employing two methods, the main-factor analysis and logistic retrospective analysis. Chapter 2 A Summary of Financial Risks of Real Estate Corporation. The financial risks confronted by domestic real estate companies will be discussed here. Risks fall into two categories, the systematic risk and non-systematic risk. Systematic risks include periodic risks, and risks from legal and political aspect, interest rate, inflation, demand-supply changes, discounting, etc. Non-systematic risks are closely related to a company’s style of operating, capital structure, financial management, consciousness of risks, etc. Risks are ubiquitous in all activities of any corporation.Chapter 3 A General Introduction of the model. In this part the author would deliberate on the two analytical methods, the main-factor analysis and the logistic retrospective analysis. The construction of the model has the following steps: first, selection of samples; second, selections of financial indicators; third, T test; fourth, multi-linear test; fifty, main-factor analysis; sixth, logistic retrospective analysis; seventh, the construction of the model; eighth, validity test of the model.Chapter 4 The analysis of the effects of such a warning system model. The author has chosen 78 real estate companies as research samples, among which 35 have financial problems and 41 are normal. Data spanning a year are employed. By applying the two analytical methods the author manages to establish a warning system. The model will finally be tested for its real effects and, in order to prove the practical value of such a model.Chapter 5 Conclusion and the Potential Application.更多还原