Corporate hedging in Singapore : an insight into hedging activities of non-financial firms.

Corporate hedging has been widely discussed as a means for companies to manage risks. This paper will examine the rationales of hedging and analyze firms’ characteristics that might have the potential to affect hedging decisions, and finally evaluate the results based on empirical evidence. This is...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Heng, Xi Lei., Koh, Peck Fong., Kristanto Agung Pribadi Anggadijaya.
مؤلفون آخرون: Liu Wei-Lin
التنسيق: Final Year Project
اللغة:English
منشور في: 2009
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/10356/15126
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:Corporate hedging has been widely discussed as a means for companies to manage risks. This paper will examine the rationales of hedging and analyze firms’ characteristics that might have the potential to affect hedging decisions, and finally evaluate the results based on empirical evidence. This is to provide a further insight into the factors influencing hedging decisions among non-financial firms in Singapore. Systematic sampling was performed on the sampling frame to get a final sample of 60 firms. Data from their annual reports are then collected and numerical measures of central tendency and descriptive statistics were performed to analyze the data. Findings indicated that firms with lower revenues have lower tendency to hedge. Binary logistics regression and multiple linear regressions were also performed to examine the characteristics that indicate Singaporean firms’ tendency to hedge. The findings indicated that an increase in asset would increase firm’s likeliness to engage in interest hedging activity. It was also found that a company’s tendency to hedge currency risks is dependent on whether there are export activities but not on the amount of revenue derived from these activities. Findings from the multiple linear regression supported the results from the binary logistic regression. To examine the industry impact, the collected data was also tested with multiple linear regression for each industry. The findings again supported the binary and multiple regression.