Authors :
Akash Kumar; Garima Panwar; Anant Samrat
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/mrrahe9f
Scribd :
https://tinyurl.com/3fmdtdtm
DOI :
https://doi.org/10.5281/zenodo.14471364
Abstract :
The stock market is a complex and dynamic
system characterized by significant volatility and
uncertainty[1]. Accurate prediction of stock prices is
crucial for investors and financial analysts to make
informed decisions and maximize returns. Traditional
forecasting methods often fall short due to their reliance
on historical data alone and their inability to adapt to
rapid market changes. In recent years, machine learning
(ML) has emerged as a powerful tool for enhancing stock
prediction accuracy by leveraging advanced algorithms
and large datasets. This paper presents a comprehensive
study on the development and evaluation of a stock
prediction system utilizing machine learning techniques.
The system is designed to analyze historical stock price
data and generate forecasts using two prominent ML
models: Linear Regression and Long Short-Term
Memory (LSTM) networks. Linear Regression is
employed as a baseline model due to its simplicity and
interpretability, while LSTM networks are utilized for
their ability to capture complex temporal dependencies in
time series data.
Keywords :
Stock Prediction, Feature Selection, Jellyfish Optimization, Machine Learning, SVM.
References :
- Zhang, G., Qi, M., & Zhou, G. (1998). A neural network approach to stock market prediction. IEEE Transactions on Neural Networks, 9(5), 1212-1223.
- Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
- Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875-889.
- Chen, C., Lin, C., & Lee, Y. (2019). Ensemble learning for stock market prediction: A comparison of multiple algorithms. Journal of Financial Data Science, 1(2), 44-55.
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- Liu, B., Zhang, L., & Wei, J. (2019). Enhancing stock prediction with financial news sentiment analysis. International Journal of Financial Engineering, 6(3), 195-210.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
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- Nguyen, T. T., & Nguyen, T. K. (2019). Hybrid machine learning models for stock price prediction. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 111-117.
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- Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks for stock price prediction and portfolio optimization. European Journal of Operational Research, 259(2), 689-702.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Jiang, J., & Liang, J. (2020). Financial time series forecasting with hybrid deep learning model. Journal of Financial Data Science, 2(4), 22-35.
- Jin, X., Zhang, Z., & Zheng, X. (2018). Stock price prediction using hybrid deep learning model. Proceedings of the IEEE International Conference on Data Mining (ICDM), 431-440.
- Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
- Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-Scale Kernel Machines, 34-75.
The stock market is a complex and dynamic
system characterized by significant volatility and
uncertainty[1]. Accurate prediction of stock prices is
crucial for investors and financial analysts to make
informed decisions and maximize returns. Traditional
forecasting methods often fall short due to their reliance
on historical data alone and their inability to adapt to
rapid market changes. In recent years, machine learning
(ML) has emerged as a powerful tool for enhancing stock
prediction accuracy by leveraging advanced algorithms
and large datasets. This paper presents a comprehensive
study on the development and evaluation of a stock
prediction system utilizing machine learning techniques.
The system is designed to analyze historical stock price
data and generate forecasts using two prominent ML
models: Linear Regression and Long Short-Term
Memory (LSTM) networks. Linear Regression is
employed as a baseline model due to its simplicity and
interpretability, while LSTM networks are utilized for
their ability to capture complex temporal dependencies in
time series data.
Keywords :
Stock Prediction, Feature Selection, Jellyfish Optimization, Machine Learning, SVM.