基于RS-ANN的房地产行业财务评价体系研究
发布时间：2018-07-16 08:28
【摘要】：1998年，我国进行了住房货币化改革。随后，房地产行业被确定为我国的国民经济支柱产业。近年来，，我国房地产行业仍然处于高速发展的状态。但是，伴随着大规模的地域扩张，房地产行业的优胜劣汰进程也越来越快。随着我国宏观经济调控政策（如土地供应、房屋预收、银行贷款、个人按揭等方面）的出台，房地产行业所处的经营环境也发生了很大的变化。从房地产行业所处的外部条件来看：2005年“国八条”、2006年“国六条”、2013年“国五条”的相继出台，土地房屋交易方式、交易费用的变革，以及国家对房地产行业的宏观调控（政策环境严厉、融资环境欠佳、项目成本攀升），整体形势较为严峻。 我国的房地产企业起步晚、基础差、规模小，远未实现规范化，许多房地产企业由于较高资产负债率已经出现了资金紧张、经营困难的局面，现实的各种风险和危机都给房地产上市公司带来了诸多不确定性的影响，财务危机成为其中最为关键的影响因素。房地产行业其资金投入大、开发周期长、变现能力差以及受不确定因素影响明显的特点更决定了房地产行业必然会面临巨大的财务风险。因此，根据目前的财务数据进行财务危机的提前预测，在财务危机出现萌芽的状态给予提醒，使公司采取相应的措施，从而预防财务危机发生与发展对于公司来说是十分重要的。人工神经网络作为一种非线性建模和预测方法，具有良好的非线性品质及较高的数值逼近能力和泛化能力。它通过模拟大脑神经元处理、记忆信息的方式对各种错综复杂的信息进行知识识别分类和目标预测。人工神经网络已被广泛应用于预测研究，并获得了良好的效果。 本文采用神经网络的方法，根据最新的数据资料，构建房地产上市公司的财务状况评价模型并检验其成果。本文在对财务状况评价的机理和关键财务指标分析的基础上，提出了基于粗糙集的财务指标属性约简方法，设计了财务状况评价模型的构建流程和检验标准，建立了基于BP神经网络的财务状况评价模型。在此基础上，利用matlab软件采用146家房地产上市公司的2007 2012年财务数据进行了研究，结果表明，基于BP神经网络的财务状况评价模型是可以对房地产公司的财务状况作出评价。为财务状况评价、财务危机预测提供了一种新的方法。
[Abstract]:In 1998, China carried out housing monetization reform. Subsequently, the real estate industry was identified as the pillar industry of our national economy. In recent years, China's real estate industry is still in a state of rapid development. However, with the large-scale regional expansion, the process of survival of the fittest in the real estate industry is also getting faster and faster. With the introduction of macroeconomic regulation and control policies (such as land supply, housing prepayment, bank loans, personal mortgage, etc.), the operating environment of the real estate industry has also changed greatly. From the external conditions of the real estate industry: in 2005 "eight articles", 2006 "six articles", 2013 "national five articles" one after another, land and housing transaction mode, transaction cost changes, And the macro-control of the real estate industry (the policy environment is strict, the financing environment is poor, the project cost is rising), the overall situation is more severe. The real estate enterprises in our country started late, had a poor foundation, a small scale, and were far from being standardized. Many real estate enterprises have been faced with a situation of tight capital and difficult management due to their high asset-liability ratio. The real risks and crises have brought many uncertain influences to the real estate listed companies, and the financial crisis has become the most important factor. The characteristics of the real estate industry, such as large capital investment, long development period, poor liquidity ability and obvious influence of uncertain factors, determine that the real estate industry is bound to face huge financial risks. Therefore, according to the current financial data to predict the financial crisis in advance, in the emergence of financial crisis to give a warning, so that the company to take appropriate measures, In order to prevent the occurrence and development of financial crisis for the company is very important. As a nonlinear modeling and prediction method, artificial neural network has good nonlinear quality, high numerical approximation ability and generalization ability. By simulating the processing of brain neurons and memorizing information, it can recognize and classify the complicated information and predict the target. Artificial neural network (Ann) has been widely used in prediction research and achieved good results. In this paper, according to the latest data, the financial status evaluation model of real estate listed companies is constructed and its results are verified by the method of neural network. Based on the analysis of the mechanism and key financial indicators of financial condition evaluation, this paper puts forward the attribute reduction method of financial index based on rough set, and designs the construction process and test standard of financial condition evaluation model. The financial condition evaluation model based on BP neural network is established. On this basis, the financial data of 2007 / 2012 of 146 listed real estate companies are studied by using matlab software. The results show that the financial status evaluation model based on BP neural network can be used to evaluate the financial status of real estate companies. It provides a new method for evaluating financial situation and predicting financial crisis.
【学位授予单位】：中国地质大学（北京）
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F275;F299.233.4
[Abstract]:In 1998, China carried out housing monetization reform. Subsequently, the real estate industry was identified as the pillar industry of our national economy. In recent years, China's real estate industry is still in a state of rapid development. However, with the large-scale regional expansion, the process of survival of the fittest in the real estate industry is also getting faster and faster. With the introduction of macroeconomic regulation and control policies (such as land supply, housing prepayment, bank loans, personal mortgage, etc.), the operating environment of the real estate industry has also changed greatly. From the external conditions of the real estate industry: in 2005 "eight articles", 2006 "six articles", 2013 "national five articles" one after another, land and housing transaction mode, transaction cost changes, And the macro-control of the real estate industry (the policy environment is strict, the financing environment is poor, the project cost is rising), the overall situation is more severe. The real estate enterprises in our country started late, had a poor foundation, a small scale, and were far from being standardized. Many real estate enterprises have been faced with a situation of tight capital and difficult management due to their high asset-liability ratio. The real risks and crises have brought many uncertain influences to the real estate listed companies, and the financial crisis has become the most important factor. The characteristics of the real estate industry, such as large capital investment, long development period, poor liquidity ability and obvious influence of uncertain factors, determine that the real estate industry is bound to face huge financial risks. Therefore, according to the current financial data to predict the financial crisis in advance, in the emergence of financial crisis to give a warning, so that the company to take appropriate measures, In order to prevent the occurrence and development of financial crisis for the company is very important. As a nonlinear modeling and prediction method, artificial neural network has good nonlinear quality, high numerical approximation ability and generalization ability. By simulating the processing of brain neurons and memorizing information, it can recognize and classify the complicated information and predict the target. Artificial neural network (Ann) has been widely used in prediction research and achieved good results. In this paper, according to the latest data, the financial status evaluation model of real estate listed companies is constructed and its results are verified by the method of neural network. Based on the analysis of the mechanism and key financial indicators of financial condition evaluation, this paper puts forward the attribute reduction method of financial index based on rough set, and designs the construction process and test standard of financial condition evaluation model. The financial condition evaluation model based on BP neural network is established. On this basis, the financial data of 2007 / 2012 of 146 listed real estate companies are studied by using matlab software. The results show that the financial status evaluation model based on BP neural network can be used to evaluate the financial status of real estate companies. It provides a new method for evaluating financial situation and predicting financial crisis.
【学位授予单位】：中国地质大学（北京）
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F275;F299.233.4
【参考文献】
相关期刊论文 前10条
1 阮平南;宋晋娜;;新视角下企业财务预警指标体系的构建[J];商业研究;2006年17期
2 徐鹿;边s
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