Comparison of Various Deep Learning Algorithms In Evaluating The Prediction of Stock Prices

Registration ID: IJNRD_196600

Published ID: IJNRD2305807

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Suhasi gadge, Pranoti Kale, Aishwarya Kottapalli, Piyusha Patil, Sheetal Patil

Stock Price, Long-Short Term Memory(LSTM), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Multi-Layer Perceptron(MLP), Root Mean Squared Error(RMSE), Mean Absolute Percentage Error(MAPE)

This research paper aims to compare the performance of different deep learning algorithms, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), for the prediction of stock prices. The study utilizes a dataset spanning ten years of historical stock data from RELIANCE.NS, listed on the National Stock Exchange (NSE) in India. The accuracy of each algorithm is evaluated using two popular evaluation metrics, namely Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). These metrics are commonly employed to measure the accuracy and precision of stock price predictions. The research focuses on comparing the performance of the algorithms based on these metrics to identify the most effective approach for stock price prediction. The results indicate that LSTM outperforms all other algorithms in terms of both RMSE and MAPE values. LSTM, a type of RNN architecture with memory cells, demonstrates superior performance due to its ability to capture long-term dependencies and temporal patterns in time series data. The LSTM model shows significant promise in predicting stock prices accurately, making it a valuable tool for investors and financial analysts. This research contributes to the field of stock market prediction by providing empirical evidence on the comparative performance of popular deep learning algorithms. The findings suggest that LSTM is a highly effective approach for stock price prediction, offering a potential advantage in decision-making and trading strategies. Future research may explore additional datasets and further optimize the LSTM model to enhance its performance and applicability in real-world financial scenarios.

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Suhasi gadge, Pranoti Kale, Aishwarya Kottapalli, Piyusha Patil, & Sheetal Patil (May-2023). Comparison of Various Deep Learning Algorithms In Evaluating The Prediction of Stock Prices. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), i49-i56. https://ijnrd.org/papers/IJNRD2305807.pdf

Volume 8 Issue 5, May-2023

Pages : i49-i56

Paper Reg. ID: IJNRD_196600

Published Paper Id: IJNRD2305807

Research Area: Engineering

Author Type: Indian Author

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305807.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305807

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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