A Study on Training Criteria for Financial Time Series Forecasting

摘要：

Traditional backpropagation neural networks training criterion is based on goodness-of-fit which is also the most popular criterion forecasting. How ever, in the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their target. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. This article reports the analysis on the performance of several neural network training criteria. The results show that the new approach does improve the forecastability of neural network models, for the financial application domain

作者: JingTao YAO Chew Lim TAN

作者单位: Dept of Information Systems Massey University Private Bag 11222 Palmerston North New Zealand Dept of Computer Science National University of Singapore 1 Science Drive 2 Singapore 117543 Singapo

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 795-799

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）