The Memory Advantage of Long Short-Term Memory Networks for Bond Yield Forecasting
The Memory Advantage of Long Short-Term Memory Networks for Bond Yield Forecasting
复制标题
DOI：
10.2139/ssrn.3415219
复制
发表时间：
2019-07
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
债券市场在金融行业中的重要性源于其规模及其与其他资产类别和整体经济的直接相关性。在本文中，我们首次使用深度学习长短期记忆 (LSTM) 网络进行债券收益率预测研究，验证了 LSTM 网络在该方面的潜力，并确定了 LSTM 相对于标准前馈神经网络（特别是多层感知器 (MLP)）的记忆优势。具体来说，我们使用具有不同输入序列（6、21 和 61 个时间步长）的单变量 LSTM 对 10 年期欧元政府债券收益率进行建模，考虑从第二天到未来 20 天的五个预测范围。我们将这些 LSTM 模型与 MLP 进行比较，无论是单变量还是每个预测范围都使用最相关的特征。结果表明，具有额外内存的单变量 LSTM 模型能够使用来自市场和经济的信息实现与多元 MLP 类似的结果。此外，在相同条件下（即 5 个时间步长的小输入序列）直接比较模型，可以得到与 LSTM 相似或更好且标准差较低的结果。此外，对于 LSTM，较短的预测范围需要较小的输入序列，反之亦然。总之，在资产管理行业的决策支持系统中使用 LSTM 的结果令人鼓舞，结合了宏观经济/市场信息并将输入序列长度调整到所考虑的预测范围。
The importance of bond markets in the financial industry stems from its dimension, its direct relevance for other asset classes and for the overall economy. In this paper, we conduct the first study of bond yield forecasting using deep learning long short-term memory (LSTM) networks, validating the potential of LSTMs networks for that purpose, and identifying the LSTM's memory advantage over standard feedforward neural networks, in particular, the multilayer perceptron (MLP). Specifically, we model the 10-year Euro government bond yield using univariate LSTMs with different input sequences (6, 21 and 61 time steps), considering five forecasting horizons, from next day to 20 days ahead. We compare those LSTM models with MLPs, both univariate as well as using the most relevant features for each forecasting horizon. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using information from markets and the economy. Moreover, the direct comparison of models in identical conditions, i.e. small input sequence of 5 time steps, leads to results with LSTMs that are similar or better with lower standard deviations. Furthermore, with the LSTMs, shorter forecasting horizons require smaller input sequences and vice-versa. In summary, the results are encouraging for the use of LSTMs in decision support systems for the asset management industry, incorporating macroeconomic / market information and adjusting the input sequence length to the forecasting horizon considered.