Evaluation and improvement of two homogeneous stock trading systems under computational and experimental finance in China: based on IASM model

Zhuwei Li, Baolu Wang and Rong He

International Journal of Computational Economics and Econometrics, 2024, vol. 14, issue 4, 363-388

Abstract: Artificial simulated stock market model is widely used because it can provide repeatable simulation experiment platform for different trading systems to play a role in the financial market. Based on the investor structure, trading behaviours and institutional rules of Chinese stock market, this paper uses intelligent artificial stock market (IASM) model, aims to build a comprehensive evaluation index system of stock market quality, evaluate the effect of the T + N trading system and the price limit system on the quality of Chinese stock market, and then gives analysis results and improvement suggestions. It is found that T + 0 trading system and narrowing the range of limit price fluctuation can significantly improve the quality of Chinese stock market. At the same time, among the combinations of various T + N trading systems and price limit systems, the combination of T + 0 trading system and 5% price limit system of Chinese stock market has the highest comprehensive quality.

Keywords: stock trading system; IASM model; market quality evaluation; system improvement; computational and experimental finance. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=142048 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:14:y:2024:i:4:p:363-388

Access Statistics for this article

More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().