This study investigates the efficiency of the Chinese metal futures （i.e. copper and aluminum） traded on China＇s Shanghai Futures Exchange. First, we thoroughly analyze the development of China＇s commodity futures...This study investigates the efficiency of the Chinese metal futures （i.e. copper and aluminum） traded on China＇s Shanghai Futures Exchange. First, we thoroughly analyze the development of China＇s commodity futures markets, which provides a fundamental background. Then we examine the random walk and unbiasedness hypotheses for two metal futures during 1999-2004. Based on the empirical evidence, we argue that China＇s copper and aluminum futures markets are efficient, and that they aid the process of price discovery because futures prices can be considered as unbiased predictors of future spot prices. We attribute this efficiency to the regulatory changes made in 1999 and the increased financial skills and acumen of the participants in the market.展开更多
Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk pred...Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.展开更多
基金This research is sponsored by theGuangdong Natural Science Foundation (No. 5300541).
文摘This study investigates the efficiency of the Chinese metal futures （i.e. copper and aluminum） traded on China＇s Shanghai Futures Exchange. First, we thoroughly analyze the development of China＇s commodity futures markets, which provides a fundamental background. Then we examine the random walk and unbiasedness hypotheses for two metal futures during 1999-2004. Based on the empirical evidence, we argue that China＇s copper and aluminum futures markets are efficient, and that they aid the process of price discovery because futures prices can be considered as unbiased predictors of future spot prices. We attribute this efficiency to the regulatory changes made in 1999 and the increased financial skills and acumen of the participants in the market.
文摘Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.
