Artificial Neural Networks in Fixed Income Markets for Yield Curve Forecasting
Artificial Neural Networks in Fixed Income Markets for Yield Curve Forecasting
复制标题
DOI：
10.2139/ssrn.3144622
复制
发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Manuel Nunes;E. Gerding;F. McGroarty;M. Niranjan
中科院分区：
文献类型：
--
作者：
Manuel Nunes;E. Gerding;F. McGroarty;M. Niranjan
收益率曲线是债券市场的核心，债券市场是一个庞大的资产类别，总规模达100万亿美元，使用机器学习的研究相对不足。本文是第一个全面的研究，利用人工神经网络的背景下，收益率曲线预测。具体而言，两个模型用于预测欧洲收益率曲线：多元线性回归和多层感知器（MLP），在五个预测范围，从第二天到20天。MLP的五个变体用不同的特征集进行了分析：预测目标（单变量）;最相关的特征;所有生成的特征;前两个包含由线性回归模型生成的合成数据。此外，采用了两种不同的多任务学习技术：同时建模和转换成多个单任务学习。结果表明，考虑到所有的预测水平，MLP使用最相关的功能取得了最好的结果，合成数据的添加往往会提高精度。此外，不同的目标和预测范围导致不同的相关特征，从而加强了定制模型的重要性。在两种多任务学习方法中，没有明显的区别，可以证明，并确定了几个解释因素。总体而言，这一结果对于开发更好的固定收益市场预测系统非常令人鼓舞。
The yield curve is the centrepiece in bond markets, a massive asset class with an overall size of USD100 trillion that remains relatively under-investigated using machine learning. This paper is the first comprehensive study using artificial neural networks in the context of yield curve forecasting. Specifically, two models were used for forecasting the European yield curve: multivariate linear regression and multilayer perceptron (MLP), at five forecasting horizons, from next day to 20 days ahead. Five variants of the MLP were analysed with different sets of features: target to predict (univariate); the most relevant features; all generated features; and the former two incorporating synthetic data generated by the linear regression model. Additionally, two different techniques of multitask learning were employed: simultaneous modelling and transformation into multiple single task learning. The results show that considering all forecasting horizons, the MLP using the most relevant features achieved the best results and the addition of synthetic data tends to improve accuracy. Furthermore, different targets and forecasting horizons resulted in different relevant features, reinforcing the importance of custom-built models. In the two multitask learning methodologies no clear differentiation could be demonstrated, and several explaining factors are identified. Overall, the outcome is very encouraging for the development of better forecasting systems for fixed income markets.