Title: Projected future stock valuations with the newly developed hybrid model: a case study on the Shanghai Stock Exchange Index

Authors: Lei Wang; Mingzhu Xie

Addresses: School of Accounting and Finance, Anhui Xinhua University, Hefei, Anhui, China ' School of Accounting and Finance, Anhui Xinhua University, Hefei, Anhui, China

Abstract: The share market provides the means of buying and selling shares of publicly traded companies. Share price forecasting is very challenging due to various interdependent variables, which are political, social and economic. The prime function of investments in financial markets is return maximisation. As technology advances rapidly, there are many opportunities for researchers, investors and analysts in financial markets. Artificial intelligence and emerging technologies provide investors with more precise options. This study suggests an innovative approach that combines the CatBoost model, Wavelet decomposition and an Artificial Bee Colony to address the intricacies of market forecasting. Using Shanghai Stock Exchange (SSE) Index data for 2015-2023, the suggested alternate model performed better with the best R² of 0.9972 during testing. The proposed methodology demonstrated high performance, low error rates and high efficiency. Results indicate that this approach surpasses existing methods in forecast accuracy and better manages the trading market's volatility.

Keywords: trading market forecast; index price; CatBoost model; artificial bee colony optimisation.

DOI: 10.1504/IJIPT.2024.146272

International Journal of Internet Protocol Technology, 2024 Vol.17 No.4, pp.167 - 179

Received: 17 Feb 2025
Accepted: 21 Mar 2025
Published online: 14 May 2025 *

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