Stock Price Prediction using Neural Networks
Keywords:
Keywords: Artificial intelligence, Artificial Neural Network, K-Nearest Neighbor, Recurrent Neural Network, and Long-short term memory, Stock Prediction, RMSE, MBEAbstract
Stock market prediction is crucial for capital allocation and economic growth, but it is also challenging due to the uncertainty and complexity of the future. This study compares the performance of four machine learning models - Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN), and Long-short term memory (LSTM) - in predicting the stock price in different geographical locations (S&P500, NYSE, NASDAQ, SSEC, EURONEXT, TSE). Using daily stock price data from November 25th, 2012, to November 25th, 2022, the models are evaluated based on root mean square error (RMSE), mean bias error (MBE), accuracy, and mean absolute percentage error (MAPE) and cross-validating the prediction results. The results show that KNN consistently outperforms the other models in most regions with the lowest error rates and the highest accuracy. The cross-validation results further confirm the superiority of KNN over the other three models.
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