Optimized Machine Learning Models for Enhanced Stock Market Predictions: A Case Study on the SSE Index

Several stock exchanges located all over the world make up the stock market, also known as the financial market. The objective of investment in the financial market is to increase profits. Because sellers want to sell their shares at the highest price and buyers want to buy them at the lowest possible price, it is difficult to predict future market dynamics in this complex market. Predictions about the stock market have long been made using traditional methods that examine both technical and fundamental factors. Traditional prediction tools are unreliable, which has led to the rise of novel artificial intelligence-based strategies. This study aims to introduce a machine learning-based model for Shanghai Stock Exchange Index (SSE) index prediction. In this research, three optimizers—the Genetic algorithm, the Artificial Bee Colony, and the Aquila optimizer—were chosen to modify the parameters of the chosen model to assess how well Adaptive Boosting performed in stock price prediction. The study's output feature was close price forecasting of the SSE index, and the input features included open, high, low, and volume prices which were collected from January 2015 to the end of June 2023. The regression coefficient of 0.9948 indicates that the combination of this adaptive boosting and the Aquila optimizer yielded very accurate and effective results. Ultimately, the findings suggested that the hybrid model could not only significantly outperform the comparative models examined in this study, but also provide useful and practical recommendations for relevant investors, businesses, and policymakers.