AN OPTIMIZED DEEP LEARNING APPROACH TO STOCK PRICE PREDICTION BASED ON INVESTOR SENTIMENT
Keywords:
Stock Price Prediction, Deep Learning, Investor Sentiment, LSTM, NLP, Market Forecasting, Sentiment AnalysisAbstract
Stock price prediction is a crucial yet challenging task due to the inherent volatility of financial markets. Traditional forecasting methods often struggle to incorporate the influence of investor sentiment, which plays a significant role in price fluctuations. This paper proposes an optimized deep learning approach that integrates historical stock data and investor sentiment analysis to enhance prediction accuracy. The proposed model leverages natural language processing (NLP) techniques to extract sentiment from financial news, social media, and investor discussions, which is then combined with technical indicators. A hybrid deep learning framework incorporating Long Short-Term Memory (LSTM) networks and attention mechanisms is utilized to capture both temporal dependencies and sentiment-driven market movements. The model is further optimized using hyperparameter tuning and feature selection techniques to improve robustness. Experimental results on real-world stock market datasets demonstrate that our approach outperforms traditional machine learning models in terms of prediction accuracy, trend detection, and risk minimization. This research highlights the importance of combining quantitative data with qualitative investor sentiment for more precise and adaptive stock price forecasting.
References
M. M. Rounaghi and F. N. Zadeh, ‘‘Investigation of market efficiency and financial stability between S&P 500 and London stock exchange: Monthly and yearly forecasting of time series stock returns using ARMA model,’’ Phys. A, Stat. Mech. Appl., vol. 456, pp. 10–21, Aug. 2016, doi: 10.1016/j.physa.2016.03.006.
G. Bandyopadhyay, ‘‘Gold price forecasting using ARIMA model,’’ J. Adv. Manage. Sci., vol. 4, no. 2, pp. 117–121, 2016, doi: 10.12720/joams.4.2.117-121.
H. Shi, Z. You, and Z. Chen, ‘‘Analysis and prediction of Shanghai composite index by ARIMA model based on wavelet analysis,’’ J. Math. Pract. Theory, vol. 44, no. 23, pp. 66–72, 2014.
H. Herwartz, ‘‘Stock return prediction under GARCH—An empirical assessment,’’ Int. J. Forecasting, vol. 33, no. 3, pp. 569– 580, Jul. 2017, doi: 10.1016/j.ijforecast.2017.01.002.
H. Mohammadi and L. Su, ‘‘International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models,’’ Energy Econ., vol. 32, no. 5, pp. 1001–1008, Sep. 2010, doi: 10.1016/j.eneco.2010.04.009.
A. Hossain and M. Nasser, ‘‘Recurrent support and relevance vector machines based model with application to forecasting volatility of financial returns,’’ J. Intell. Learn. Syst. Appl., vol. 3, no. 4, pp. 230– 241, 2011, doi: 10.4236/jilsa.2011.34026.
J. Chai, J. Du, K. K. Lai, and Y. P. Lee, ‘‘A hybrid least square support vector machine model with parameters optimization for stock forecasting,’’ Math. Problems Eng., vol. 2015, pp. 1–7, Jan. 2015, doi: 10.1155/2015/231394.
A. Murkute and T. Sarode, ‘‘Forecasting market price of stock using artificial neural network,’’ Int. J. Comput. Appl., vol. 124, no. 12, pp. 11–15, Aug. 2015, doi: 10.5120/ijca2015905681.
D. Banjade, ‘‘Forecasting Bitcoin price using artificial neural network,’’ Jan. 2020, doi: 10.2139/ssrn.3515702.
J. Zahedi and M. M. Rounaghi, ‘‘Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran stock exchange,’’ Phys. A, Stat. Mech. Appl., vol. 438, pp. 178–187, Nov. 2015, doi: 10.1016/j.physa.2015.06.033.
A. H. Moghaddam, M. H. Moghaddam, and M. Esfandyari, ‘‘Stock market index prediction using artificial neural network,’’ J. Econ., Finance Administ. Sci., vol. 21, no. 41, pp. 89–93, Dec. 2016, doi: 10.1016/j.jefas.2016.07.002.
H. Liu and Y. Hou, ‘‘Application of Bayesian neural network in prediction of stock time series,’’ Comput. Eng. Appl., vol. 55, no. 12, pp. 225–229, 2019.
A. M. Rather, A. Agarwal, andV. N. Sastry, ‘‘Recurrent neural network and a hybrid model for prediction of stock returns,’’ Expert Syst. Appl., vol. 42, no. 6, pp. 3234–3241, Apr. 2015, doi: 10.1016/j.eswa.2014.12.003.
A. Sherstinsky, ‘‘Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,’’ Phys. D, Nonlinear Phenomena, vol. 404, Mar. 2020, Art. no. 132306, doi: 10.1016/j.physd.2019.132306.
G. Ding and L. Qin, ‘‘Study on the prediction of stock price based on the associated network model of LSTM,’’ Int. J. Mach. Learn. Cybern., vol. 11, no. 6, pp. 1307–1317, Nov. 2019, doi: 10.1007/s13042-019-01041-1.
X. Yan, W. Weihan, and M. Chang, ‘‘Research on financial assets transaction prediction model based on LSTM neural network,’’ Neural Comput. Appl., vol. 33, no. 1, pp. 257–270, May 2020, doi: 10.1007/s00521-020- 04992-7.
M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. Shahab, ‘‘Deep learning for stock market prediction,’’ Entropy, vol. 22, no. 8, p. 840, Jul. 2020, doi: 10.3390/e22080840.
Z. D. Aksehir and E. Kiliç, ‘‘How to handle data imbalance and feature selection problems in CNN-based stock price forecasting,’’ IEEE Access, vol. 10, pp. 31297–31305, 2022, doi: 10.1109/ACCESS.2022. 3160797.
Y. Ji, A. W. Liew, and L. Yang, ‘‘A novel improved particle swarm optimization with long-short term memory hybrid model for stock indices forecast,’’ IEEE Access, vol. 9, pp. 23660–23671, 2021, doi: 10.1109/ACCESS.2021.3056713.
X. Zeng, J. Cai, C. Liang, and C. Yuan, ‘‘A hybrid model integrating long shortterm memory with adaptive genetic algorithm based on individual ranking for stock index prediction,’’ PLoS ONE, vol. 17, no. 8, Aug. 2022, Art. no. e0272637, doi: 10.1371/journal.pone.0272637. [20] E. Mankolli and V. Guliashki, ‘‘A hybrid machine learning method for text analysis to determine job titles similarity,’’ in Proc. 15th Int. Conf. Adv. Technol., Syst. Services Telecommun. (TELSIKS), Oct. 2021, pp. 380–385..
