Please use this identifier to cite or link to this item:
|Title:||Sentiment-aware volatility forecasting||Authors:||Xing, Frank Z.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Xing, F. Z., Cambria, E. & Zhang, Y. (2019). Sentiment-aware volatility forecasting. Knowledge-Based Systems, 176, 68-76. https://dx.doi.org/10.1016/j.knosys.2019.03.029||Journal:||Knowledge-Based Systems||Abstract:||Recent advances in the integration of deep recurrent neural networks and statistical inferences have paved new avenues for joint modeling of moments of random variables, which is highly useful for signal processing, time series analysis, and financial forecasting. However, introducing explicit knowledge as exogenous variables has received little attention. In this paper, we propose a novel model termed sentiment-aware volatility forecasting (SAVING), which incorporates market sentiment for stock return fluctuation prediction. Our framework provides an ensemble of symbolic and sub-symbolic AI approaches, that is, including grounded knowledge into a connectionist neural network. The model aims at producing a more accurate estimation of temporal variances of asset returns by better capturing the bi-directional interaction between movements of asset price and market sentiment. The interaction is modeled using Variational Bayes via the data generation and inference operations. We benchmark our model with 9 other popular ones in terms of the likelihood of forecasts given the observed sequence. Experimental results suggest that our model not only outperforms pure statistical models, e.g., GARCH and its variants, Gaussian-process volatility model, but also outperforms the state-of-the-art autoregressive deep neural nets architectures, such as the variational recurrent neural network and the neural stochastic volatility model.||URI:||https://hdl.handle.net/10356/152084||ISSN:||0950-7051||DOI:||10.1016/j.knosys.2019.03.029||Rights:||© 2019 Elsevier B.V. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Dec 28, 2021
Updated on Jan 24, 2022
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.