Predicting the Stock Market using Machine Learning

How can we make stock market predictions more reliable in a world where volatility rules? In this research-driven presentation, Dr. Partha Majumdar explores the use of machine learning, particularly deep learning architectures like GRU, LSTM, RNN, and ANN, for forecasting stock market trends.

Drawing from advanced time series techniques, the presentation demonstrates how converting non-stationary financial data into a stationary format significantly improves the accuracy and stability of predictive models. Based on extensive experimentation, this study shows how Gated Recurrent Units (GRUs) consistently outperform other models when trained on stationarised data.

📊 Key Highlights:
• Why stock market data is inherently non-stationary
• How differencing transforms time series data for prediction
• Model-wise comparison using R² scores and MSE metrics
• Real-world implications for institutional and retail investors
• Challenges and ethical considerations in financial AI

This video is essential viewing for data scientists, finance professionals, researchers, and anyone interested in the intersection of AI and financial markets. It offers a transparent, scalable, and reproducible framework for stock market prediction using open-source tools and public datasets.

📘 Based on doctoral research by Dr. Partha Majumdar
🎓 Research grounded in contemporary literature (2023–2025)
🔬 Next steps: Transformers, CNN-GRU hybrids, and sentiment-aware forecasting

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Видео Predicting the Stock Market using Machine Learning канала Partha Majumdar