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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/3062

    Title: 以財務比率導向建構使用衍生性金融商品避險之預測模式-以建設公司及營造廠為例;Developing prediction model using financial ratio viewpoint toward financial risk hedging: construction companies
    Authors: 劉柏毅;Po-Yi Liu
    Contributors: 營建管理研究所
    Keywords: 預測模型;財務比率因子;衍生性金融商品;多維矩形複合式類神經網路;Derivatives;Risk Hedging;Financial Risk;Prediction Model;Construction Company.
    Date: 2008-03-20
    Issue Date: 2009-09-21 12:04:13 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 全球經濟發展的趨勢帶動著我國產業經濟整體大環境呈現巨幅的改變,即公司企業於財務操作風險便因大環境巨幅改變而擴大行成為全球性風險。營建業者於未來必須面臨因風險全球化所帶來如貨幣緊急貸款利率調漲、美元對其它貨幣之匯率下跌、國際油價、鐵礦石價格上漲、鋼筋建材價格高漲、以及國際砂石產量限制等財務風險。故,公司企業為求公司最大利益,勢必使用適當之利率、匯率以及原物料價格等財務避險操作。因此,使用衍生性金融商品能有效達到相當程度之財務避險效果,係將成為營建業者規避財務風險之主要財務工具。 研究目的係幫助建設公司及營造廠瞭解本身財務狀態與使用衍生性金融商品避險之關聯性,於財務操作上應衡量本身財務狀態,使用衍生性金融商品來規避財務操作之風險;並且以建設公司及營造廠為蒐集樣本,利用多維矩形複合式類神經網路來建立預測模型,以提供營建業者作為參考。研究方法上先以文獻蒐集彙整出與使用衍生性金融商品相關之財務比率因子,並且利用會計科目表示及資產負債表中長期投資項目進行公式推導,以驗證兩者間之關聯性,再透過回歸分析結果之解釋能力表示財務比率因子對於使用衍生性金融商品間之關聯性程度。最後,將分析獲得之財務比率因子利用多維矩形複合式類神經網路進行預測模型建構,以上市建設公司以及營造廠作為模型建構之樣本。 本研究可歸納出以下兩點貢獻:1.建設公司及營造廠可透過本身財務狀態中之16項財務比率因子來判斷是否需要使用衍生性金融商品。2.本研究利用多維矩形複合式類神經網路所建立出來可達到80.6%正確率之預測模型,足以作為建設公司及營造廠於判斷是否需要使用衍生性金融商品避險時之參考依據。 It is well-known that financial risk is one of critical factors driving construction companies to difficulty. Costs of materials such as concrete and steel bars, variable currency exchange rate, and increase of interest rate can easily enlarge financial risks for construction companies. Using an effective method to reduce financial risks is desired. Risk hedging through the use of derivatives is a wide accepted financial technique and has been used in many industries and countries. The objectives of this research are to (1) identify the impact factors among the corporations’ financial ratios revealed in public while derivatives are used for financial risk hedging, and to (2) construct a prediction model using Hyper Rectangular Composite Neural Networks (HRCNNs). The determination of the impact factors and the proposed model are in the basis of the database which has a range of all 38 listed construction companies and their corresponding quarterly financial statements from 2002 to 2006. A total of 16 financial ratios are considered as the impact factors, also being used to establish the HRCNNs prediction model. This study concludes that, from the financial analysis viewpoint, these 16 impact factors pay an important role to assist decision-makers in using derivatives for risk hedging. The model is also verified with an accurate rate at 80.6%, presenting a good prediction model to suggest while using derivatives to hedge financial risks.
    Appears in Collections:[營建管理研究所 ] 博碩士論文

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