A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns
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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.
KeywordsMixed-frequency data Support vector regression (U)MIDAS-SVR Market sentiment
The authors are grateful to the Editor-in-Chief, the Associate Editor, and two anonymous referees for their helpful comments and constructive guidance. This work was supported by the National Natural Science Foundation of PR China (71671056, 91846201).
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Conflict of interest
The authors declare that they have no conflict of interest.