Stock Market Analysis and Forecasting using Machine Learning and Deep Learning Algorithms
Keywords:
Stock Market Analysis, Stock Market Price Forecasting, Machine Learning , Deep Learning , Performance Metrics , RMSE, MAE.Abstract
Nowadays, everyone wants to invest their money where they can get the most profit and benefits with high returns for their amount. In this application, the stock market analysis and forecasting are performed using different machine learning and deep learning algorithms, which helps the person who is investing in the stock market. Users can make the smart decision to get higher profit using machine learning and deep learning algorithms. This application uses LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network) models from deep learning, and RF (Random Forest), ARIMA models from machine learning are used for understanding stock market scenarios. The proposed method provides better performance, which is calculated using MAE (mean absolute error) and RMSE (root mean square error). The proposed system provides investment suggestions based on stock prices, so this system is more safeguard for market risks as well as decisions related to finance that can be taken based on more knowledge with high perfection.
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Nabipour, Mojtaba, Pooyan Nayyeri, Hamed Jabani, and Amir Mosavi. "Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis." Ieee Access 8 (2020): 150199-150212.
Nikou, Mahla, Gholamreza Mansourfar, and Jamshid Bagherzadeh. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms." Intelligent Systems in Accounting, Finance and Management 26, no. 4 (2019): 164-174.
Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat. "Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications." International Journal of Financial Studies 11, no. 3 (2023): 94.
Mukherjee, Somenath, Bikash Sadhukhan, Nairita Sarkar, Debajyoti Roy, and Soumil De. "Stock market prediction using deep learning algorithms." CAAI Transactions on Intelligence Technology 8, no. 1 (2023): 82-94.
Verma, Satya, Satya Prakash Sahu, and Tirath Prasad Sahu. "Stock Market Forecasting with Different Input Indicators using Machine Learning and Deep Learning Techniques: A Review." Engineering Letters 31, no. 1 (2023).
H. Takayasu, “Practical fruits of econophysics,” Proc. 3rd Nikkei Econophys. Symp., 2006.
Y. Zuo and E. Kita, “Stock price forecast using Bayesian network,” Expert Systems with Applications, 2012.
D. Hallac et al., “Toeplitz inverse covariance-based clustering of multivariate time series data,” ACM KDD, 2017.
S.-Y. Shih et al., “Temporal pattern attention for multivariate time series forecasting,” Machine Learning Journal, 2019.
F. Karim et al., “Multivariate LSTM-FCNs for time series classification,” Neural Networks, 2019.
Arora, Naman. "Financial analysis: Stock market prediction using deep learning algorithms." In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India. 2019.
Mehtab, Sidra, and Jaydip Sen. "A time series analysis-based stock price prediction using machine learning and deep learning models." International Journal of Business Forecasting and Marketing Intelligence 6, no. 4 (2020): 272-335.
Singh, Aryendra, Priyanshi Gupta, and Narina Thakur. "An empirical research and comprehensive analysis of stock market prediction using machine learning and deep learning techniques." In IOP conference series: materials science and engineering, vol. 1022, no. 1, p. 012098. IOP Publishing, 2021.
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