Predictability of China’s Stock Market Returns Based on Combination of Distribution Forecasting Models
-
- Yao Yanyun
- Department of Applied Statistics, Shaoxing University
-
- Zheng Xiutian
- Department of Economics, Hangzhou Normal University Qianjiang College
-
- Wang Huimin
- Department of Applied Statistics, Shaoxing University
この論文をさがす
説明
<p>No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is “qualified” in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China’s stock market.</p>
収録刊行物
-
- Journal of Advanced Computational Intelligence and Intelligent Informatics
-
Journal of Advanced Computational Intelligence and Intelligent Informatics 24 (4), 477-487, 2020-07-20
富士技術出版株式会社
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390566775153925504
-
- NII論文ID
- 130007878712
-
- NII書誌ID
- AA12042502
-
- ISSN
- 18838014
- 13430130
-
- NDL書誌ID
- 030518021
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可