Abstract: The stock market, as a preferred path for investment, continues to attract a growing number of individuals. Yet, the attraction of potential profits is counterbalanced by the substantial risks associated with stock market investments. In response to this dynamic and complex financial environment, the field of machine learning has been harnessed to construct predictive models that offer insights into future stock price movements. Support Vector Regression (SVR) and Long-Short Term Memory (LSTM), two distinctive and effective machine learning techniques, are used in this research project to explore the field of stock price prediction. The closing values of the stocks of five different companies are forecasted using these models. The Root Mean Squared Error (RMSE), one of the most well-known error metrics, is carefully used to assess the predictive accuracy and performance of SVR and LSTM. The findings of this empirical study show a striking difference between the two methods. In this comparison examination, LSTM is shown to be the better option, demonstrating its outstanding abilities to capture the complex dynamics and nonlinear patterns present in stock price data. The study's conclusions help us comprehend the potential of machine learning for stock market forecasting by highlighting the advantages of LSTM as a stock price prediction tool.
Keywords: Machine Learning, Stock Market, Stock Price Prediction, Artificial Neural Network, Recurrent Neural Network, Long Short-Term Memory, Support Vector Machine.
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DOI:
10.17148/IJARCCE.2024.134132
