Beyond traditional networks: Stock predictions via enhanced backpropagation

Abstract

Predicting stock prices has long been a challenge due to the inherent volatility and intricacies of stock markets, and addressing these forecasting challenges has broad implications for the financial sector and those involved in market activities. This research holds pivotal importance as it equips traders, investors, and financial institutions with enhanced tools, facilitating better decision-making and optimized strategies. Beyond its academic significance, an accurate model becomes an indispensable asset in the real-world navigation of stock markets, especially in a realm where slight prediction discrepancies can result in substantial financial impacts. This study endeavours to introduce and validate an enhanced Backpropagation neural network with the core objective of elevating stock price prediction accuracy and reliability to a benchmark level. Adopting a meticulously crafted Backpropagation neural network, designed specifically for improved accuracy in stock price forecasting, we employed rigorous evaluation methods, measuring the model's performance against key metrics MAE and MSE. Additionally, visual representations were given to provide a more intuitive understanding of the model's prowess. The results were clear: the enhanced model demonstrably excelled in prediction precision, as evidenced by the favourable MAE and MSE outcomes. Visual narratives further accentuated its adeptness at tracing the complex oscillations inherent to stock market behaviours, underscoring both its academic contribution and potential as a transformative tool in the practical landscape of stock forecasting.