Improving the accuracy of stock Return Predictions in the Iraqi Stock exchange Index Using Artificial Intelligence Techniques
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This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) models to predict stock returns in the emerging Iraq Stock Exchange (ISX), a market characterized by high volatility and limited prior research. The research conducts a comprehensive empirical comparison between traditional statistical models (ARIMA) and advanced AI algorithms, including XGBoost, Linear Regression, Random Forest, SVR, LSTM, and Transformer.
Using daily index data from 2018 to 2025, the models were evaluated based on statistical metrics like RMSE,MSE, MAE, and R². The results demonstrate a clear superiority of advanced models over traditional ones. The Transformer model achieved the best performance, exhibiting the highest predictive accuracy and lowest error, attributed to its self-attention mechanism that effectively captures complex temporal dependencies in the data.
The findings confirm the research hypothesis that advanced ML models can significantly outperform traditional statistical methods in the challenging context of the Iraqi market. The study concludes by recommending the formal adoption of these AI tools, particularly the Transformer model, to enhance investment decision-making, improve risk management, and foster greater efficiency and investor confidence in the ISX. This research provides a valuable framework for integrating AI-driven financial analysis in emerging markets.
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