Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence on their predictive performance, especially for time-series data, has been mixed. Whereas some applications find that ANNs provide better forecasts than more traditional estimation techniques, others find that they barely outperform basic benchmarks. The present article aims to provide guidance as to when the use of ANNs might result in better results in a general setting. We propose a flexible nonparametric model and extend existing theoretical results for the rate of convergence to include the popular Rectified Linear Unit (ReLU) activation function and compare the rate to other nonparametric estimators. Finite sample properties are then studied with the help of Monte-Carlo simulations to provide further guidance. An application to estimate the Value-at-Risk of portfolios of varying sizes is also considered to show the practical implications.

翻译：利用财务数据进行一系列模拟和预测任务时使用了人工神经网络(ANN),但是,关于预测性能的证据,特别是时间序列数据,却好坏参半,有些应用发现,ANN提供的预测比传统的估计技术要好,而另一些应用发现,它们几乎没有超过基本基准,本条款的目的是就使用ANNs在总体环境中取得较好结果的情况提供指导,我们提议了一个灵活的非参数模型,并将关于趋同率的现有理论结果扩大到包括广受欢迎的纠正线性线性装置(RELU)激活功能,并将比率与其他非参数性估计器进行比较,然后在蒙特-卡洛模拟的帮助下研究精度样品特性,以提供进一步的指导,还考虑对不同规模的组合的价值-风险进行估计,以显示实际影响。