Enhanced Context-Based Stock Recommendation Integrating News Classification and Technical Indicators
Enhanced Context-Based Stock Recommendation Integrating News Classification and Technical Indicators |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-8 |
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| Year of Publication : 2024 | ||
| Author : Vipul.V.Bag, Shradha Joshi-Bag, Mithun B. Patil, V. D. Gaikwad, Sonali M. Antad, Sandeep P. Abhang |
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| DOI : 10.14445/22315381/IJETT-V72I8P111 | ||
How to Cite?
Vipul.V.Bag, Shradha Joshi-Bag, Mithun B. Patil, V. D. Gaikwad, Sonali M. Antad, Sandeep P. Abhang, "Enhanced Context-Based Stock Recommendation Integrating News Classification and Technical Indicators," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 96-104, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P111
Abstract
This paper presents a method for predicting stock price trends by leveraging online textual news as a contextual factor. Incorporating both historical stock prices and online news data, the cognitive process involves extracting Context Features (CF) from news sources to provide recommendations for traders. Utilizing a Naïve Bayes classification algorithm, news sentiment is efficiently categorized as positive or negative, yielding a News Sentiment Weight (NSW). A News Sentiment Weight (NSW) is then computed for the news, and its impact on a specific stock is assessed to predict the trend. Additionally, the quality of the stock concerning technical is evaluated using different financial technical methods(Indicators), forming the Technical Weight of Stock (TWoS). The combination of NSW and TWoS with weights is utilized to predict the price of the stock trend and offer recommendations to traders. The paper predicts stock trends by combining NSW and TWoS and delivers tailored recommendations to traders. Comparative analysis showcases the superior performance of this contextual approach over traditional recommendation systems, highlighting its efficacy in accurate stock signal prediction.
Keywords
Cognitive Process Stock recommendation, Naïve Bays algorithm, News Sentiment Weight(NSW), Technical Weight of Stock (TWoS).
References
[1] Pooja Saigal, and Vaibhav Khanna, “Multi-Category News Classification Using Support Vector Machine Based Classifiers,” SN Applied Sciences, vol. 2, pp. 1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xingchen Wan et al., “Sentiment Correlation in Financial News Networks and Associated Market Movements,” Scientific Reports, vol. 11, no. 1, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yu Ma et al., “Multi-Source Aggregated Classification for Stock Price Movement Prediction,” Information Fusion, vol. 91, pp. 515-528, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Weiwei Jiang, “Applications of Deep Learning in Stock Market Prediction: Recent Progress, Expert Systems with Applications,” Expert Systems with Applications, vol. 184, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jian Cao, Zhi Li, and Jian Li, “Financial Time Series Forecasting Model Based on CEEMDAN and LSTM,” Physica A: Statistical Mechanics and Its Applications, vol. 519, pp. 127-139, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ankit Thakkar, and Kinjal Chaudhari, “Fusion in Stock Market Prediction: A Decade Survey on the Necessity, Recent Developments, and Potential Future Directions,” Information Fusion, vol. 65, pp. 95-107, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiaodong Li, Pangjing Wu, and Wenpeng Wang, “Incorporating Stock Prices and News Sentiments for Stock Market Prediction: A Case of Hong Kong,” Information Processing & Management, vol. 57, no. 5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Arthur Emanuel de Oliveira Carosia, Guilherme Palermo Coelho, and Ana Estela Antunes da Silva, “Investment Strategies Applied to the Brazilian Stock Market: A Methodology Based on Sentiment Analysis with Deep Learning,” Expert Systems with Applications, vol. 184, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Luca Cagliero, Jacopo Fior, and Paolo Garza, “Shortlisting Machine Learning-Based Stock Trading Recommendations Using Candlestick Pattern Recognition,” Expert Systems with Applications, vol. 216, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Hongping Hu et al., “Predicting the Direction of Stock Markets Using Optimized Neural Networks with Google Trends,” Neurocomputing, vol. 285, pp. 188-195, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jiahao Yang et al., “Enhancing Stock Movement Prediction with Market Index and Curriculum Learning,” Expert Systems with Applications, vol. 213, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yameng Zhang, Yan Song, and Guoliang Wei, “A Feature-Enhanced Long Short-Term Memory Network Combined with Residual-Driven V Support Vector Regression for Financial Market Prediction,” Engineering Applications of Artificial Intelligence, vol. 118, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ali Derakhshan, and Hamid Beigy, “Sentiment Analysis on Stock Social Media for Stock Price Movement Prediction,” Engineering Applications of Artificial Intelligence, vol. 85, pp. 569-578, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Weiling Chen et al., “Leveraging Social Media News to Predict Stock Index Movement Using RNN-Boost,” Data & Knowledge Engineering, vol. 118, pp. 14-24, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jun Hu, and Wendong Zheng, “A Deep Learning Model to Effectively Capture Mutation Information in Multivariate Time Series Prediction,” Knowledge-Based Systems, vol. 203, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Minglan Zhang et al., “A Deep Implicit Memory Gaussian Network For Time Series Forecasting,” Applied Soft Computing, vol. 148, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hyun Jun Park, Youngjun Kim, and Ha Young Kim, “Stock Market Forecasting Using A Multi-Task Approach Integrating Long Short-Term Memory And The Random Forest Framework,” Applied Soft Computing, vol. 114, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Shuang Han et al., “Correlational Graph Attention-Based Long Short-Term Memory Network for Multivariate Time Series Prediction,” Applied Soft Computing, vol. 106, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yusheng Huang et al., “A New Financial Data Forecasting Model Using Genetic Algorithm and Long Short-Term Memory Network,” Neurocomputing, vol. 425, pp. 207-218, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Nan Jing, Zhao Wu, and Hefei Wang, “A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction,” Expert Systems with Applications, vol. 178, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] O. Bustos, and A. Pomares-Quimbaya, “Stock Market Movement Forecast: A Systematic Review,” Expert Systems with Applications, vol. 156, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Rodolfo C. Cavalcante et al., “Computational Intelligence and Financial Markets: A Survey and Future Directions,” Expert Systems with Applications, vol. 55, pp. 194-211, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Yong Hu et al., “Stock Trading Rule Discovery With An Evolutionary Trend Following Model,” Expert Systems with Applications, vol. 42, no. 1, pp. 212-222, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Javad Zahedi, and Mohammad Mahdi Rounaghi, “Application of Artificial Neural Network Models and Principal Component Analysis Method In Predicting Stock Prices on Tehran Stock Exchange,” Physica A: Statistical Mechanics and its Applications, vol. 438, pp. 178-187, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yakup Kara et al., “Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange,” Expert Systems with Applications, vol. 38, no. 5, pp. 5311-5319, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Amin Hedayati Moghaddam, Moein Hedayati Moghaddam, and Morteza Esfandyari, “Stock Market Index Prediction Using Artificial Neural Network,” Journal of Economics, Finance and Administrative Science, vol. 21, no. 41, pp. 89-93, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mingyue Qiu, Yu Song, and Fumio Akagi, “Application of Artificial Neural Network for the Prediction of Stock Market Returns: The Case of the Japanese Stock Market,” Chaos, Solitons & Fractals, vol. 85, pp. 1-7, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[28] E.L. de Faria et al., “Predicting the Brazilian Stock Market through Neural Networks and Adaptive Exponential Smoothing Methods,” Expert Systems with Applications, vol. 36, no. 10, pp. 12506-12509, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Reyes Michaela Denise Gonzales, and Carol Anne Hargreaves, “How Can We Use Artificial Intelligence for Stock Recommendation and Risk Management? A Proposed Decision Support System,” International Journal of Information Management Data Insights, vol. 2, no. 2, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Richard McCreadie et al., Next-Generation Personalized Investment Recommendations, Big Data and Artificial Intelligence in Digital Finance, Springer, Cham, pp. 171-198, 2022.
[CrossRef] [Google Scholar] [Publisher Link]