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Neural Computing and Applications

Erschienen in:

08.02.2019 | Original Article

A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns

verfasst von: Qifa Xu, Liukai Wang, Cuixia Jiang, Yezheng Liu

Erschienen in: Neural Computing and Applications | Ausgabe 10/2020

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Abstract

From the point view of behavioral finance, market sentiment plays an important role in forecasting stock returns. How to accurately measure the impact of market sentiment is a challenge work. Two issues on nonlinear relationship and mixed-frequency data have to be addressed. To this end, we introduce methods of mixed-frequency data into SVRs and develop a novel (U)MIDAS-SVR model. It can be estimated by solving the Lagrange duality technique of quadratic programming. We then apply the (U)MIDAS-SVR model to predict weekly returns of SHSE and SZSE in China using the mixed-frequency market sentiment as covariates. The empirical results show that the (U)MIDAS-SVR model is promising and MIDAS-SVR is superior to those competing models in terms of MAE and RMSE. In addition, we design seven scenarios by considering different data source combinations and find that the multi-source market sentiment is helpful to improve forecasting performance on stock returns.

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Titel: A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns
verfasst von: Qifa Xu
Liukai Wang
Cuixia Jiang
Yezheng Liu
Publikationsdatum: 08.02.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04063-6

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