Abstract:
The article focuses on enhancing stock market price prediction through
artificial neural networks and machine learning. It underscores the
significance of improving forecast accuracy by incorporating historical stock
prices, macroeconomic indicators, news events, and technical indicators.
Exploring deep learning principles, it delves into convolutional neural
networks (CNN), recurrent neural networks (RNN), including long shortterm memory (LSTM), and gated recurrent unit (GRU) modifications. This
financial time series processing study covers data preprocessing, creating
training/test sets, and selecting evaluation metrics. Results suggest promising
applications for the developed forecasting models in stock markets, stressing
the importance of considering various factors for precise forecasts in
dynamic financial environments. Historical reserve data serves as the model
foundation. Integration of macroeconomic, news, and technical indicators
offers a holistic approach, aiding trend and anomaly identification for
enhanced forecasts. The article recommends suitable deep learning
architectures, highlighting LSTM and GRU's effectiveness in adapting to
intricate data dependencies. Experimental outcomes showcase these
architectures' benefits in predicting stock market prices, offering valuable
insights for finance and asset management professionals in financial analysis
and machine learning realms.
