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Machine Learning and Deep Learning as Predictive Forecasters of Capital Markets: A Systematic Literature Review


Affiliations
1 Asst. Professor, Dr. D.Y.Patil School of Management, Lohegaon, Pune, India
2 Associate Professor, Dr. D. Y. Patil School of Management, Lohegaon, Pune, India
3 Asst. Professor, Dr. D. Y. Patil School of Management, Lohegaon, Pune, India
 

As the sun dips across the horizon, the stock exchange closes the bell, marking the closure of the day for traders and analyst to think and speculate the next day stock trends. The financial time series are old fashioned though a base line for all prediction methods. However there is a need to understand the prediction accuracy and capital market volatility augmented by fluctuations. The researchers carried out a systematic literature review to unleash the instruments that play the role of predictors in capital market today. It was found after assessment of the high impact research papers that Machine learning and Deep learning are major technological areas that is aiding the capital market predictions and behavioral analysis. with multiple techniques within to support prediction algorithms and clustering techniques for data analysis to support, this study summarizes the most five common set out the complete list of algorithm and techniques used.

Keywords

Capital Market Predictions, Data Science, Deep Learning, , Machine Learning, Prediction Algorithms
Notifications

  • Atkins, A., Niranjan, M., & Gerding, E. (2018). “Financial news predicts stock market volatility better than close price”. The Journal of Finance and Data Science, 4(2), 120–137. https://doi.org/10.1016/j.jfds.2018.02.002
  • Budiharto, W. (2021). “Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)”. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00430-0
  • Chong, E., Han, C., & Park, F. C. (2017). “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies”. Expert Systems with Applications, 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030
  • Esfahanipour, A., & Aghamiri, W. (2010). “Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis”. Expert Systems with Applications, 37(7), 4742–4748. https://doi.org/10.1016/j.eswa.2009.11.020
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011a). “Using artificial neural network models in stock market index prediction”. Expert Systems with Applications, 38(8), 10389–10397. https://doi.org/10.1016/j.eswa.2011.02.068
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011b). “Using artificial neural network models in stock market index prediction”. Expert Systems with Applications, 38(8), 10389–10397. https://doi.org/10.1016/j.eswa.2011.02.068
  • Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). “Literature review: Machine learning techniques applied to financial market prediction”. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012
  • Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016). “Bridging the divide in financial market forecasting: machine learners vs. financial economists”. Expert Systems with Applications, 61, 215–234. https://doi.org/10.1016/j.eswa.2016.05.033
  • Kumar, M., & Thenmozhi, M. (2006, January 24). “Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest”. Papers.ssrn.com. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=876544
  • Moghar, A., & Hamiche, M. (2020). “Stock Market Prediction Using LSTM Recurrent Neural Network”. Procedia Computer Science, 170, 1168–1173. https://doi.org/10.1016/j.procs.2020.03.049
  • Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E., & S., S. (2020). “Deep Learning for Stock Market Prediction”. Entropy, 22(8), 840. https://doi.org/10.3390/e22080840
  • Nabipour, M., Nayyeri, P., Jabani, H., S., S., & Mosavi, A. (2020). “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis”. IEEE Access, 8, 150199–150212. https://doi.org/10.1109/access.2020.3015966
  • Nanda, S. R., Mahanty, B., & Tiwari, M. K. (2010). “Clustering Indian stock market data for portfolio management”. Expert Systems with Applications, 37(12), 8793–8798. https://doi.org/10.1016/j.eswa.2010.06.026
  • Pang, X., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2018). “An innovative neural network approach for stock market prediction”. The Journal of Supercomputing. https://doi.org/10.1007/s11227-017-2228-y
  • Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., & Chouhan, L. (2018). “Stock Market Prediction Using Machine Learning”. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). https://doi.org/10.1109/icsccc.2018.8703332
  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). “Predicting stock market index using fusion of machine learning techniques”. Expert Systems with Applications, 42(4), 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
  • Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). “How to properly use the PRISMA Statement”. Systematic Reviews, 10(1). https://doi.org/10.1186/s13643-021-01671-z
  • SCHWERT, G. W. (1989). “Why Does Stock Market Volatility Change Over Time?” The Journal of Finance, 44(5), 1115–1153. https://doi.org/10.1111/j.1540-6261.1989.tb02647.x
  • Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0131-7
  • Shen, J., & Shafiq, M. O. (2020). “Short-term stock market price trend prediction using a comprehensive deep learning system”. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00333-6
  • Thomas, G. (2016). “How to Do Your Research Project : a Guide for Students”(3rd ed., p. 59). Sage. (Original work published 2013)
  • Ticknor, J. L. (2013). “A Bayesian regularized artificial neural network for stock market forecasting”. Expert Systems with Applications, 40(14), 5501–5506. https://doi.org/10.1016/j.eswa.2013.04.013
  • Vargas, M. R., de Lima, B. S. L. P., & Evsukoff, A. G. (2017). “Deep learning for stock market prediction from financial news articles”. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). https://doi.org/10.1109/civemsa.2017.7995302
  • Viswanathan, M., Ansari, M. T., Berkman, N. D., Chang, S., Hartling, L., McPheeters, M., P Lina Santaguida, Shamliyan, T., Singh, K., Tsertsvadze, A., & Treadwell, J. R. (2012, March
  • “Assessing the Risk of Bias of Individual Studies in Systematic Reviews of Health Care Interventions”. Nih.gov; Agency for Healthcare Research and Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK91433/
  • Xiao, Y., & Watson, M. (2017). “Guidance on Conducting a Systematic Literature Review”. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456x17723971
  • Yoo, P. D., Kim, M. H., & Jan, T. (n.d.). “Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation”. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). https://doi.org/10.1109/cimca.2005.1631572
  • Zhong, X., & Enke, D. (2019). “Predicting the daily return direction of the stock market using hybrid machine learning algorithms”. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0138-0
  • Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). “Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets”. Mathematical Problems in Engineering, 2018, 1–11. https://doi.org/10.1155/2018/4907423

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  • Machine Learning and Deep Learning as Predictive Forecasters of Capital Markets: A Systematic Literature Review

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Authors

Dr. Debashree Jana
Asst. Professor, Dr. D.Y.Patil School of Management, Lohegaon, Pune, India
Dr. Shreekala Bachhav
Associate Professor, Dr. D. Y. Patil School of Management, Lohegaon, Pune, India
Dr. Chetan Eknath Khedkar
Associate Professor, Dr. D. Y. Patil School of Management, Lohegaon, Pune, India
Dr. Ashutosh Eknath Khedkar
Asst. Professor, Dr. D. Y. Patil School of Management, Lohegaon, Pune, India

Abstract


As the sun dips across the horizon, the stock exchange closes the bell, marking the closure of the day for traders and analyst to think and speculate the next day stock trends. The financial time series are old fashioned though a base line for all prediction methods. However there is a need to understand the prediction accuracy and capital market volatility augmented by fluctuations. The researchers carried out a systematic literature review to unleash the instruments that play the role of predictors in capital market today. It was found after assessment of the high impact research papers that Machine learning and Deep learning are major technological areas that is aiding the capital market predictions and behavioral analysis. with multiple techniques within to support prediction algorithms and clustering techniques for data analysis to support, this study summarizes the most five common set out the complete list of algorithm and techniques used.

Keywords


Capital Market Predictions, Data Science, Deep Learning, , Machine Learning, Prediction Algorithms

References





DOI: https://doi.org/10.17697/ibmrd%2F2022%2Fv11i2%2F172611