Using AH Premium to Predict Related Stock Index with Support Vector Machine

DOI

10.2991/978-94-6463-042-8_52How to use a DOI?

Keywords

AH premium; Hang Seng China Enterprises Index (HSCEI); Stock index prediction; Support vector machine

Abstract

Since the launch of Shanghai-Hong Kong Stock Connect, AH premium of the A share and H share dual-listed companies maintains at a high level. The extra cost for mainland investors to invest in H shares is an important factor. This paper will analyze this phenomenon and apply support vector machine (SVM) to predict related stock index – Hang Seng China Enterprises Index (HSCEI), in order to examine whether investors can invest these dual-listed companies according to the change of AH premium. The forecasting ability of different kinds of SVMs are compared and the results show that when appropriate parameters are selected, the success rate of prediction can reach nearly 56%. Thus investors can invest with reference to changes of AH premium to some extent.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiyang Chen
PY  - 2022
DA  - 2022/12/29
TI  - Using AH Premium to Predict Related Stock Index with Support Vector Machine
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 355
EP  - 360
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_52
DO  - 10.2991/978-94-6463-042-8_52
ID  - Chen2022
ER  -