Neural Forecasting of the Italian Sovereign Bond Market with Economic News
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Abstract (arXiv)
In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on Long-Short Term Memory cells outperforms classical machine learning techniques and provides a forecasting performance that is over and above that obtained by using conventional determinants of interest rates alone.
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References · 11
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2015LSTM: A Search Space Odysseycited by 315
- 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 121
- 2019GluonTS: Probabilistic Time Series Models in Pythoncited by 45
- 2019Bayesian Optimization for Policy Search via Online-Offline Experimentationcited by 20
- 2017Decision support from financial disclosures with deep neural networks and transfer learningcited by 18
- 2020Macroeconomic forecasting through news, emotions and narrativecited by 2
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