【摘要】：1998年，我國進行了住房貨幣化改革。隨后，房地產(chǎn)行業(yè)被確定為我國的國民經(jīng)濟支柱產(chǎn)業(yè)。近年來，，我國房地產(chǎn)行業(yè)仍然處于高速發(fā)展的狀態(tài)。但是，伴隨著大規(guī)模的地域擴張，房地產(chǎn)行業(yè)的優(yōu)勝劣汰進程也越來越快。隨著我國宏觀經(jīng)濟調(diào)控政策（如土地供應(yīng)、房屋預(yù)收、銀行貸款、個人按揭等方面）的出臺，房地產(chǎn)行業(yè)所處的經(jīng)營環(huán)境也發(fā)生了很大的變化。從房地產(chǎn)行業(yè)所處的外部條件來看：2005年“國八條”、2006年“國六條”、2013年“國五條”的相繼出臺，土地房屋交易方式、交易費用的變革，以及國家對房地產(chǎn)行業(yè)的宏觀調(diào)控（政策環(huán)境嚴厲、融資環(huán)境欠佳、項目成本攀升），整體形勢較為嚴峻。 我國的房地產(chǎn)企業(yè)起步晚、基礎(chǔ)差、規(guī)模小，遠未實現(xiàn)規(guī)范化，許多房地產(chǎn)企業(yè)由于較高資產(chǎn)負債率已經(jīng)出現(xiàn)了資金緊張、經(jīng)營困難的局面，現(xiàn)實的各種風險和危機都給房地產(chǎn)上市公司帶來了諸多不確定性的影響，財務(wù)危機成為其中最為關(guān)鍵的影響因素。房地產(chǎn)行業(yè)其資金投入大、開發(fā)周期長、變現(xiàn)能力差以及受不確定因素影響明顯的特點更決定了房地產(chǎn)行業(yè)必然會面臨巨大的財務(wù)風險。因此，根據(jù)目前的財務(wù)數(shù)據(jù)進行財務(wù)危機的提前預(yù)測，在財務(wù)危機出現(xiàn)萌芽的狀態(tài)給予提醒，使公司采取相應(yīng)的措施，從而預(yù)防財務(wù)危機發(fā)生與發(fā)展對于公司來說是十分重要的。人工神經(jīng)網(wǎng)絡(luò)作為一種非線性建模和預(yù)測方法，具有良好的非線性品質(zhì)及較高的數(shù)值逼近能力和泛化能力。它通過模擬大腦神經(jīng)元處理、記憶信息的方式對各種錯綜復(fù)雜的信息進行知識識別分類和目標預(yù)測。人工神經(jīng)網(wǎng)絡(luò)已被廣泛應(yīng)用于預(yù)測研究，并獲得了良好的效果。 本文采用神經(jīng)網(wǎng)絡(luò)的方法，根據(jù)最新的數(shù)據(jù)資料，構(gòu)建房地產(chǎn)上市公司的財務(wù)狀況評價模型并檢驗其成果。本文在對財務(wù)狀況評價的機理和關(guān)鍵財務(wù)指標分析的基礎(chǔ)上，提出了基于粗糙集的財務(wù)指標屬性約簡方法，設(shè)計了財務(wù)狀況評價模型的構(gòu)建流程和檢驗標準，建立了基于BP神經(jīng)網(wǎng)絡(luò)的財務(wù)狀況評價模型。在此基礎(chǔ)上，利用matlab軟件采用146家房地產(chǎn)上市公司的2007 2012年財務(wù)數(shù)據(jù)進行了研究，結(jié)果表明，基于BP神經(jīng)網(wǎng)絡(luò)的財務(wù)狀況評價模型是可以對房地產(chǎn)公司的財務(wù)狀況作出評價。為財務(wù)狀況評價、財務(wù)危機預(yù)測提供了一種新的方法。
[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.
【學位授予單位】：中國地質(zhì)大學（北京）
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F275;F299.233.4

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