Stock Price Prediction Model Based on Convolutional Neural Networks
DOI:
https://doi.org/10.5281/zenodo.12780145ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a01Keywords:
Convolutional Neural Networks, Stock Price Prediction, Financial Markets, Deep Learning, Technical indicators, Prediction Accuracy, Feature Extraction, Model Performance, Machine Learning, Time Series AnalysisAbstract
Stock price prediction is a challenging task due to the volatility and non-linear nature of financial markets. Traditional models often fail to capture complex patterns in the data. This paper presents a Convolutional Neural Network (CNN) based approach to predict stock prices. The model leverages historical stock data and various technical indicators. Unlike conventional methods that rely on hand-crafted features, our CNN model automatically learns and extracts relevant features from raw data, enhancing prediction accuracy. We compare the performance of our CNN model with other traditional models, including linear regression and support vector machines (SVM). Experimental results, using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), show that the CNN model outperforms the traditional models in terms of prediction accuracy. Additionally, we demonstrate the robustness of the CNN model through various validation techniques and highlight its potential for practical applications in financial markets.
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