Indian Stock Market Sell and Buy Indication using Technical Indicators and Enhanced Bidirectional Long Short-Term Memory
Keywords:
Stock Data, Random Forest, Gradient Boosting, XGBoost, DenseNet, CNN-BiLSTM, LSTM, BiLSTM, DANN, Technical Indicators.Abstract
This study introduces an innovative approach to signal generation for sell and buy decisions in the Indian Stock Market, leveraging Novel Technical Indicators and an Enhanced Bidirectional Long Short-Term Memory (BiLSTM) model. We evaluated various machine learning models, including Random Forest, Gradient Boosting, XGBoost, DenseNet, CNN-BiLSTM, LSTM, BiLSTM, and DANN, on their predictive performance using metrics such as MAPE, MAE, and computation time. Our proposed BiLSTM model, optimized with novel technical indicators, demonstrated superior performance with the lowest MAPE and competitive MAE, while maintaining a rapid computation time. These results highlight the efficacy of BiLSTM models in handling the sequential nature of stock data and the advantage of novel technical indicators in capturing intricate market trends. The proposed system holds the potential to revolutionize decision-making processes for traders and investors by providing highly accurate, real-time market predictions. The comparative analysis across diverse machine learning techniques showed that the proposed method significantly surpasses conventional models like Random Forest, Gradient Boosting, and XGBoost in terms of accuracy and efficiency. It achieved a remarkable reduction in Mean Absolute Percentage Error (MAPE) to nearly 0.03%, drastically lower Mean Absolute Error (MAE) at 10.45, and exhibited the fastest execution speed at 0.984 ms, highlighting its substantial advancement over existing approaches.
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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.
Chandola, Deeksha, Akshit Mehta, Shikha Singh, Vinay Anand Tikkiwal, and Himanshu Agrawal. "Forecasting directional movement of stock prices using deep learning." Annals of Data Science 10, no. 5 (2023): 1361-1378.
Alkhatib, Khalid, Huthaifa Khazaleh, Hamzah Ali Alkhazaleh, Anas Ratib Alsoud, and Laith Abualigah. "A new stock price forecasting method using active deep learning approach." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 2 (2022): 96.
Bhandari, Hum Nath, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal, and Rajendra KC Khatri. "Predicting stock market index using LSTM." Machine Learning with Applications 9 (2022): 100320.
Shah, Atharva, Maharshi Gor, Meet Sagar, and Manan Shah. "A stock market trading framework based on deep learning architectures." Multimedia Tools and Applications 81, no. 10 (2022): 14153-14171.
Balakrishnan, S., Deepak Kumar Pathak, Jeidy Panduro-Ramirez, Dharam Buddhi, B. Girimurugan, and Hemant Singh Pokhariya. "Empirical evaluation on stock market forecasting via extreme learning machine." In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 475-479. IEEE, 2023.
Prachyachuwong, Kittisak, and Peerapon Vateekul. "Stock trend prediction using deep learning approach on technical indicator and industrial specific information." Information 12, no. 6 (2021): 250.
Albahli, Saleh, Tahira Nazir, Awais Mehmood, Aun Irtaza, Ali Alkhalifah, and Waleed Albattah. "AEI-DNET: a novel densenet model with an autoencoder for the stock market predictions using stock technical indicators." Electronics 11, no. 4 (2022): 611.
Azevedo, Vitor, Georg Sebastian Kaiser, and Sebastian Mueller. "Stock market anomalies and machine learning across the globe." Journal of Asset Management 24, no. 5 (2023): 419-441.
Khan, Razib Hayat, Jonayet Miah, Md Minhazur Rahman, Md Maruf Hasan, and Muntasir Mamun. "A study of forecasting stocks price by using deep Reinforcement Learning." In 2023 IEEE World AI IoT Congress (AIIoT), pp. 0250-0255. IEEE, 2023.
Mehtab, Sidra, and Jaydip Sen. "Analysis and forecasting of financial time series using CNN and LSTM-based deep learning models." In Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021, pp. 405-423. Springer Singapore, 2022.
Sen, Jaydip, Sidra Mehtab, and Abhishek Dutta. "Stock price prediction using machine learning and LSTM-based deep learning models." Authorea Preprints (2023).
Ghosh, Indranil, and Manas K. Sanyal. "Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI." International Journal of Information Management Data Insights 1, no. 2 (2021): 100039.
Tschora, Léonard, Erwan Pierre, Marc Plantevit, and Céline Robardet. "Electricity price forecasting on the day-ahead market using machine learning." Applied Energy 313 (2022): 118752.
Mehtab, Sidra, Jaydip Sen, and Abhishek Dutta. "Stock price prediction using machine learning and LSTM-based deep learning models." In Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium, SoMMA 2020, Chennai, India, October 14–17, 2020, Revised Selected Papers 2, pp. 88-106. Springer Singapore, 2021.
Nõu, Anders, Darya Lapitskaya, M. Hakan Eratalay, and Rajesh Sharma. "Predicting stock return and volatility with machine learning and econometric models-a comparative case study of the Baltic stock market." International Journal of Computational Economics and Econometrics 13, no. 4 (2023): 446-489.
Avramov, Doron, Si Cheng, and Lior Metzker. "Machine learning vs. economic restrictions: Evidence from stock return predictability." Management Science 69, no. 5 (2023): 2587-2619.
Shen, Jingyi, and M. Omair Shafiq. "Short-term stock market price trend prediction using a comprehensive deep learning system." Journal of big Data 7, no. 1 (2020): 1-33.
Nasiri, Hamid, and Mohammad Mehdi Ebadzadeh. "Multi-step-ahead stock price prediction using recurrent fuzzy neural network and variational mode decomposition." Applied Soft Computing 148 (2023): 110867.
Muthukumaran, Kavitha, and K. Hariharanath. "Deep learning enabled financial crisis prediction model for small-medium sized industries." Intelligent Automation & Soft Computing 35, no. 1 (2023): 101-103.
Zhong, Xiao, and David Enke. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms." Financial Innovation 5, no. 1 (2019): 1-20.
Garai, Sandip, Ranjit Kumar Paul, Debopam Rakshit, Md Yeasin, Walid Emam, Yusra Tashkandy, and Christophe Chesneau. "Wavelets in combination with stochastic and machine learning models to predict agricultural prices." Mathematics 11, no. 13 (2023): 2896.
Tripathi, Bhaskar, and Rakesh Kumar Sharma. "Modeling bitcoin prices using signal processing methods, bayesian optimization, and deep neural networks." Computational Economics 62, no. 4 (2023): 1919-1945.
Sen, Jaydip, Sidra Mehtab, and Gourab Nath. "Stock price prediction using deep learning models." Authorea Preprints (2023).
amalov, Firuz. "Forecasting significant stock price changes using neural networks." Neural Computing and Applications 32 (2020): 17655-17667.
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