銀行如何訂定建築業的授信政策; How does a commercial bank to enact a loan strategy about building industries

NCU Institutional Repository > 管理學院 > 財務金融學系碩士在職專班 > 博碩士論文 > Item 987654321/12418

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题名:	銀行如何訂定建築業的授信政策;How does a commercial bank to enact a loan strategy about building industries
作者:	王薰慶;Hsun-ching Wang
贡献者:	財務金融學系碩士在職專班
关键词:	土地開發分析法;財務指標;Logit;台灣經濟報;財務危機預警模型;Finance target;Logit;Taiwan economically newspaper;Land development method of analysis;Finance crisis forewarn model.
日期:	2008-06-26
上传时间:	2009-09-22 14:48:28 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	本研究以採用土地開發分析法評估土地開發個案，並以現金流量分析探討開發案各期現金流量及資金缺口，利用敏感性分析探討銀行未增加授信額度時，市場價格及造價成本變動對企業土地開發案之利潤率、各期現金流量及資金缺口變動，藉以瞭解物價變動對銀行授信風險之影響。銀行為降低現金流量風險，採行營建資金存入備償專戶控管資金流向外，另探討以不動產開發信託控管土地開發之現金流量，有助於降低銀行授信風險。另以台灣經濟報(TEJ)資料庫之上市(櫃)建設公司公開財務資料為取樣，選出10家財務危機建設公司；而對照危機的正常公司，則以危機發生年度與該危機公司營收或規模相近者為準。選取樣本的比率以1：2為配對原則，選取危機公司與正常公司30家，且以發生危機前二年財務指標平均數為研究樣本。採用 Logit模型分別建立傳統財務指標、現金流量財務指標、綜合財務指標建立財務危機預警模型，藉了解加入現金流量指標對財務危機預警模型是否可以提昇正確率。研究結果以土地開發個案為基礎，探討開發案之各項授信風險，以及三項財務指標建立之預警模型判別危機公司，綜合財務指標建立之預警模型判別正常公司之正確率為78.3%、危機公司之正確率為73.3%，總計76.7%，可改善傳統財務指標及現金流量指標之正確率。上述研究結果提供銀行管理階層訂定授信政策之參考。 This researches as adopt land development method of analysis evaluate land development individual cases, and as cash rate of flow analysis inquire into development case each cash rate of flow and fund breach, inquiring when bank do not increase give believe volume time, market price and cost of building or manufacture change to enterprise land development case zhi profit margin, each a cash rate of flow and fund breach alteration in taking advantage of sensitive analyze, by means of as knows commodity price change to bank give believe risk's affection. Bank besides picks up storing operating fund in acquit account to control outside fund flow direction in order to decrease cash rate of flow risk, and inquires into as estate develop affiance control manage land development zhi cash rate of flow, assistants to debase bank give believe risk. Another as Taiwan economically newspaper ( TEJ) Data base's appear on market ( Cupboard) Constructs company open finance data for sampling, picking up 10 finances crises construct company; But contrasts crisis' normal company, Then as crisis take place year with should crisis company camp receive or magnitude close person as standard. Selects the ratio of sample as 1:2 is match principle, select each 30 samples of crisis company and normal company, and the average number of two year financial indexs which happened before crisis events as our research samples. Adopts Logit model difference establish tradition finance target, cash rate of flow finance target, synthesizes finance target establish finance crisis forewarn model, by means of understanding joins cash rate of flow target to finance crisis forewarn model whether can promotion right rate. Findings as land development individual cases for basis, which inquires into develop case's each item loan risk, and three finances target establish zhi forewarn model differentiate crisis company. Synthesizes finance target establish zhi forewarn model differentiate normal company zhi right rate for 95.0%, crisis company zhi right rate for 90.0%, and amount to 93.3% that can ameliorate tradition finance and cash rate of flow target zhi right rate. Aboves findings supply bank administer estate fixloan strategy's for referrence.
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