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Enhancing stock market prediction using three-phase classifier and EM-EPO optimization with news feeds and historical data

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Abstract

Stock price forecasting is a crucial area of research that demands a thorough comprehension of market dynamics and sophisticated analytical methods. Deep neural networks have recently demonstrated considerable potential for enabling academics to create extremely precise models for foretelling financial patterns. Another quickly developing technology is Natural language processing (NLP), which is increasingly used to evaluate financial data, notably news, and social media sentiment, and forecast the course of the market. An innovative deep learning-based stock market prediction model is created in this research paper. The historic stock market data and news feed as the information source. The acquired raw data (news data) are pre-processed using lowercase text conversion, punctuation removal, stop word removal, tokenization of sentences, normalization, and a bag of words technique. Then, from the pre-processed news data, the features such as Parts of Speech (PoS), Term Frequency-Inverse Document Frequency (TF-IDF), and N-gram-based features are extracted. In addition, the Moving Average Convergence/Divergence oscillator (MACD), and Relative Strength Index (RSI) based features are extracted from the historic stock market data. The extracted features from the historic stock market data and news feeds are fused. The optimal features are then chosen from the fused features using a new hybrid optimization model called Exchange Market Emperor Pigeon Optimization (EMEPO). The proposed EMEPO model is a combination of the standard Exchange Market Algorithm (EMA) and Emperor Pigeon Optimization (EPO). A new three-phase classifier is introduced in this research work, for accurate stock price forecasting. The proposed three-phase classifier includes Convolutional neural networks (CNN), Bidirectional long short-term memories (Bi-LSTM), and Autoencoder. The three-phase classifier is trained using the selected EMEPO-based features. The projected outcome from the three-phase classifier portrays the stock market prices. In all three feature selection events, the suggested method produces positive outcomes. In Case 1 (Open/Close), the Mean Absolute Error (MAE) value is 0.051582, suggesting a minimal average difference between the anticipated and actual values. The accuracy of the model in forecasting percentage errors is shown by the Mean Absolute Percentage Error (MAPE), which is 11.28154%. Similar results are obtained by the model in Case 2 (High/Low/Close), with an MAE of 0.056408 and a MAPE of 12.0081%. These results show how well the suggested strategy works for anticipating stock prices with accuracy.

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References

  1. Wang WJ, Tang Y, Xiong J, Zhang YC (2021) Stock market index prediction based on reservoir computing models. Expert Syst Appl 178:115022

    Article  Google Scholar 

  2. Ingle V, Deshmukh S (2021) Ensemble deep learning framework for stock market data prediction (EDLF-DP). Global Transit Proc 2(1):47–66

    Article  Google Scholar 

  3. Akhtar MM, Zamani AS, Khan S, Shatat ASA, Dilshad S, Samdani F (2022) Stock market prediction based on statistical data using machine learning algorithms. J King Saud Univ Sci 34(4):101940

    Article  Google Scholar 

  4. de Almeida RL, Neves RF (2022) Stock market prediction and portfolio composition using a hybrid approach combined with self-adaptive evolutionary algorithm. Expert Syst Appl 204:117478

    Article  Google Scholar 

  5. Chen X, Ma X, Wang H, Li X, Zhang C (2022) A hierarchical attention network for stock prediction based on attentive multi-view news learning. Neurocomputing 504:1–15

    Article  Google Scholar 

  6. Zhao Y, Yang G (2023) Deep Learning-based Integrated Framework for stock price movement prediction. Appl Soft Comput 133:109921

    Article  Google Scholar 

  7. Ma Y, Mao R, Lin Q, Wu P, Cambria E (2023) Multi-source aggregated classification for stock price movement prediction. Inform Fusion 91:515–528

    Article  Google Scholar 

  8. Wang C, Chen Y, Zhang S, Zhang Q (2022) Stock market index prediction using deep Transformer model. Expert Syst Appl 208:118128

    Article  Google Scholar 

  9. Xiong K, Ding X, Du L, Liu T, Qin B (2021) Heterogeneous graph knowledge enhanced stock market prediction. AI Open 2:168–174

    Article  Google Scholar 

  10. Polamuri SR, Srinivas K, Mohan AK (2022) Multi-model generative adversarial network hybrid prediction algorithm (MMGAN-HPA) for stock market prices prediction. J King Saud University-Comput Inform Sci 34(9):7433–7444

    Google Scholar 

  11. Gao R, Cui S, Xiao H, Fan W, Zhang H, Wang Y (2022) Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule. Inf Sci 615:529–556

    Article  Google Scholar 

  12. Wang C, Liang H, Wang B, Cui X, Xu Y (2022) MG-Conv: A spatiotemporal multi-graph convolutional neural network for stock market index trend prediction. Comput Electr Eng 103:108285

    Article  Google Scholar 

  13. Liu X, Guo J, Wang H, Zhang F (2022) Prediction of stock market index based on ISSA-BP neural network. Expert Syst Appl 204:117604

    Article  Google Scholar 

  14. Almalis I, Kouloumpris E, Vlahavas I (2022) Sector-level sentiment analysis with deep learning. Knowl-Based Syst 258:109954

    Article  Google Scholar 

  15. Paramanik RN, Singhal V (2020) Sentiment analysis of Indian stock market volatility. Procedia Comput Sci 176:330–338

    Article  Google Scholar 

  16. Chen J, Wen Y, Nanehkaran YA, Suzauddola MD, Chen W, Zhang D (2023) Machine learning techniques for stock price prediction and graphic signal recognition. Eng Appl Artif Intell 121:106038

    Article  Google Scholar 

  17. Yang J, Zhang W, Zhang X, Zhou J, Zhang P (2023) Enhancing stock movement prediction with market index and curriculum learning. Expert Syst Appl 213:118800

    Article  Google Scholar 

  18. Yan WL (2023) Stock index futures price prediction using feature selection and deep learning. North Am J Econ Finance 64:101867

    Article  Google Scholar 

  19. Liu T, Ma X, Li S, Li X, Zhang C (2022) A stock price prediction method based on meta-learning and variational mode decomposition. Knowl-Based Syst 252:109324

    Article  Google Scholar 

  20. Chaudhari K, Thakkar A (2023) Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction. Expert Systems with Applications, 119527

  21. Teng X, Zhang X, Luo Z (2022) Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction. Neurocomputing 505:92–100

    Article  Google Scholar 

  22. Kanwal A, Lau MF, Ng SP, Sim KY, Chandrasekaran S (2022) BiCuDNNLSTM-1dCNN—A hybrid deep learning-based predictive model for stock price prediction. Expert Syst Appl 202:117123

    Article  Google Scholar 

  23. Zhang D, Lou S (2021) The application research of neural network and BP algorithm in stock price pattern classification and prediction. Futur Gener Comput Syst 115:872–879

    Article  Google Scholar 

  24. Jafari A, Haratizadeh S (2022) GCNET: graph-based prediction of stock price movement using graph convolutional network. Eng Appl Artif Intell 116:105452

    Article  Google Scholar 

  25. Yun KK, Yoon SW, Won D (2021) Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Syst Appl 186:115716

    Article  Google Scholar 

  26. Ahmad A, Akbar S, Khan S, Hayat M, Ali F, Ahmed A, Tahir M (2021) Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks. Chemom Intell Lab Syst 208:104214

    Article  Google Scholar 

  27. Akbar S, Khan S, Ali F, Hayat M, Qasim M, Gul S (2020) iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach. Chemom Intell Lab Syst 204:104103

    Article  Google Scholar 

  28. Akbar S, Hayat M, Tahir M, Khan S, Alarfaj FK (2022) cACP-DeepGram: classification of anticancer peptides via deep neural network and skip-gram-based word embedding model. Artif Intell Med 131:102349

    Article  Google Scholar 

  29. Dataset1 taken from: “https://datahub.io/collections/stock-market-data”, dated 08/03/2023.

  30. Dataset2 taken from: “https://www.kaggle.com/datasets/jainilcoder/netflix-stock-price-prediction”, dated 14/07/2023.

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  1. CSE Faculty of Engineering & Technology, Manav Rachna International Institute of Research and Studies, Faridabad, India

    Shilpa Dixit & Nitasha Soni

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  1. Shilpa Dixit
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  2. Nitasha Soni
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Correspondence to Shilpa Dixit.

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Dixit, S., Soni, N. Enhancing stock market prediction using three-phase classifier and EM-EPO optimization with news feeds and historical data. Multimed Tools Appl 83, 37859–37887 (2024). https://doi.org/10.1007/s11042-023-17184-x

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  • DOI: https://doi.org/10.1007/s11042-023-17184-x

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