Developing a SVM-based risk-hedging prediction model under using derivatives for construction material suppliers

• Main Authors: Min-Feng Chiu, 邱敏鋒
• Other Authors: Jieh-Haur Chen
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/r5875c

碩士 === 國立中央大學 === 營建管理研究所 === 96 === Floating exchange rates and interest rates have enhanced financial risks for those corporations which conduct international business or contain debt capital. Risk hedging, through the use of derivatives, has provided an effective solution toward such financial risks in recent years. Most construction material suppliers usually expose to these types of risks due to a high debt capital structure and the nature of material import business. A tool that is able to predict whether such a material supplier, based on its financial status, should use derivatives to hedge financial risks is demanded. This research objective is to develop a prediction model using Support Vector Machine (SVM) to provide suggestions for hedging financial risks. The scope limits the database to all 640 financial statements published in recent 5 years from 32 listed construction material suppliers. A total of 10 input factors were identified and determined using literature review, t-test, and co linearity diagnostics. Having data trimming and normalization, 640 sets were downsized to 520 sets which contain 248 effective and 272 ineffective risk-hedging sets. The SVM prediction model, thus, based on the kernel radial basis function and normalized data, yielded the prediction accuracy rate at 80.65%. The evaluation using the cross validation method shows the practicability and validation of the model. This study concludes that (1) 10 financial ratios are proved influential to financial risk hedging using derivative, and (2) the proposed SVM prediction model is feasible and applicable for the construction material suppliers. Future studies are recommended to apply the model to construction companies.