Published Paper Details:

In recent times, there has been vast interest amongst researchers and financial analysts in the application of deep learning and optimization approaches to the prediction of trends in the stock market. The financial markets bear inherent complexities and non-linearities such that forecasting into their trends calls for very complex methods. In this paper, we take a deep dive into the state-of-the-art deep learning models and optimization algorithms applied in stock market trend forecasting. We investigate various architectures, including RNNs, LSTMs, CNNs, and hybrid models. In our attempt to improve the prediction accuracy and efficiency of the model, we also investigate several optimization methodologies, including genetic algorithms, particle swarm optimization, and reinforcement learning. This review addresses the merits and demerits of these methodologies, analyzes the practical applications of these approaches, and even identifies prospective areas for further research. Our findings show that the integration of deep learning with sophisticated optimization methods has the potential to demonstrate significant advances in the forecasting capabilities of financial markets.