Stock Price Prediction: Impact of Volatility on Model Accuracy

This research paper focuses on predicting stock prices using neural networks, and evaluating the impact of volatility on model accuracy. Two stocks, one non-volatile and one volatile, were selected to assess the effect of volatility on prediction precision using three types of neural networks: RNN, LSTM, and feedforward. The datasets used in this study include daily stock price information obtained from Yahoo Finance for the period from September 2020 to February 2023. Additionally, news articles were extracted to perform sentiment analysis. The NLTK sentiment library was utilized to classify sentiments as positive, negative, or neutral, and the results were averaged on a daily basis. The integration of these datasets aims to provide a comprehensive understanding of the factors influencing stock price behavior. The paper discusses the methodology used to train and evaluate neural network models based on the combined datasets. This research contributes to the field of stock price prediction and highlights the importance of considering volatility in achieving accurate predictions.