Data Collaboration and Predictability of Financial Index

摘要：

This paper investigates the possibility of improving the predictability of financial time series by exploiting the effects of long-memory and cross market correlation. By using support vector machines (SVM) to predict S&P 500, we compare the forecasting performances of different kinds of data collaboration. Our results indicate that it is hard to improve the predictability of financial index by incorporating correlated time series into forecasting models. For a given forecasting horizon, the predictive performance could be improved, provided that the historical information is well organized. Furthermore, the directions of predictive errors of S&P 500 are almost contrary to the directions of the financial index return, regardless of daily return or accumulated multiday return.

关键词： data collaboration predictability support vector machines financial time series

作者: Xiaoxiao Feng Wenqi Duan

作者单位: Zhejiang Normal University School of Economics and ManagementJinhua, Zhejiang Province, PR China Zhejiang Normal University School of Economics and Management Jinhua, Zhejiang Province, PR China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Business Intelligence and Financial Engineering(第四届商务智能与金融工程国际会议 BIFE2011)

会议地点: 武汉

会议语种:英文

页码: 58-62

在线出版日期: 2011-10-17（万方平台首次上网日期，不代表论文的发表时间）