Evaluation of LSTM Networks for Stock Price Forecasting: A Benchmark Against traditional econometric models (ARIMA and PROPHET)

This thesis explores the effectiveness of the use of Long Short-Term Memory (LSTM) neural networks for prediction of daily stock closing prices and compares their performance with several traditional time series models, such as AutoRegressive Integrated Moving Average (ARIMA), its extended versions and Facebook PROPHET. The analysis is based on historical stock data from a selected group of companies listed in the S&P 500 index, collected from publicly available Yahoo Finance databases. All models are trained using a consistent preprocessing and forecasting pipeline with the objective of predicting the next day closing price. Specially selected accuracy metrics are used to evaluate the size of prediction errors and how well the models spot trends and movements. The results indicate that while traditional models can model linear relationships and seasonal changes, they tend to underperform with the messy behavior and dynamics seen in financial markets. In contrast, the LSTM model can pick up on complicated time patterns straight from the data, without need to make assumptions like stationarity. This study highlights the growing relevance of machine learning in financial areas, especially forecasting. It offers a comparative analysis that reflects the strengths and limitations of traditional, hybrid and modern approaches. The results received may be useful for researchers and practitioners, who can leverage the presented findings and model designs in their own time series forecasting applications, including closing stock price prediction.