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

作者：Xu, Qifa; Wang, Liukai; Jiang, Cuixia^*; Liu, Yezheng

来源：Neural Computing & Applications, 2020, 32(10): 5875-5888.

DOI：10.1007/s00521-019-04063-6

• Mixed-frequency data
• Support vector regression
• (U)MIDAS-SVR
• Market sentiment

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.

• 出版日期2020-5
• 单位合肥工业大学