AI-Driven Market Forecasting in Emerging Economies: A Systematic Review of GANs and Web Scraping Techniques for the Pakistan Stock Exchange

Abstract

Evaluated on PSX-100 index data (2018–2023), LSTM models and ARIMA have produced less accurate and dependable results compared to hybrid model, achieving a 4.80% MAPE (22.4% improvement over ARIMA) and 77.6% directional accuracy. Model interpret- ability was enhanced using SHAP (Shapley Additive Explanations), providing actionable insights for investors. Ethical web scraping ensured compliance with data governance standards. The framework’s adaptability to other emerging markets (e.g., Bangladesh, Nigeria) is demonstrated, alongside policy recommendations for AI adoption in Pakistan’s financial sector. Predicting stock market trends in emerging economies has always been difficult due to unstable conditions and limited data availability. Although artificial intelligence (AI) is rapidly transforming financial forecasting worldwide, its use in the Pakistan Stock Exchange (PSX) is still quite rare. This research attempts to fill that gap by developing a new approach that blends traditional Generative Adversarial Network (GAN)-based technical analysis with modern AI tools and real-time data scraping. Instead of relying solely on old or static market indicators, the proposed model collects fresh PSX data like prices, volumes, and investor sentiment from multiple online sources and uses a deep learning technique (LSTM) to process it alongside GAN's geometric patterns. The results of the evaluation of the hybrid forecasting approach using PSX-100 index data from 2018 to 2023 were encouraging and the findings which we got was so amazing It provided more accurate and reliable forecast trends and fewer prediction errors than popular prediction techniques like ARIMA and simple GAN models. This models clarity is one of its best features; analysts and investors may easily understand the logic and reasoning behind its results by using a SHAP-based interpretation. Crucially, the information was obtained through the use of ethical web scraping methods, promising that the procedure sticked to responsible data standards. This article bridges the gap between traditional analysis and explainable AI, delivering a robust, rules - based yet adaptive forecasting tool for Pakistan Stock Exchange Traders.