债券收益率预测的长短期记忆网络和laglasso：窥视黑匣子内部
Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box
摘要：
固定收益资产管理的现代决策受益于智能系统，其中涉及使用最先进的机器学习模型和适当的方法。我们使用长短期记忆（LSTM）网络进行债券收益率预测的第一项研究，验证其潜力并确定其记忆优势。具体来说，我们使用单变量LSTM模型的10年期债券收益率与三个输入序列和五个预测范围。我们比较那些与多层感知器（MLP），单变量和最相关的功能。为了揭开与LSTM相关的黑箱概念的神秘面纱，我们对该模型进行了第一次内部研究。为此，我们使用序列到序列架构（单变量和多变量），在存储单元中的选定位置计算随时间变化的LSTM信号。然后，我们继续使用外生信息来解释状态信号，为此我们开发了LSTM-LagLasso方法。结果表明，具有额外内存的单变量LSTM模型能够使用宏观经济和市场信息实现与多变量MLP相似的结果。此外，较短的预测范围需要较小的输入序列，反之亦然。在LSTM信号中发现的最显著的特性是随着时间的推移单元的激活/去激活，以及通过产量范围或特征的单元的专业化。这些信号很复杂，但可以用外生变量来解释。此外，通过LSTM-LagLasso识别的一些相关特征在预测模型中并不常用。总之，我们的工作验证了LSTM和债券方法的潜力，为金融从业者提供了额外的工具。
英文摘要：
Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states' signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.
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