Published June 10, 2022 | Version v1
Journal article Open

Forecasting of Stock Market Trends using Machine Learning Techniques

  • 1. Student, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus,Shree Chhatrapati Shivajiraje College of Engineering, Maharashtra, India.
  • 2. Professor, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus,Shree Chhatrapati Shivajiraje College of Engineering, Maharashtra, India.

Description

In this study, we examine existing stock market prediction algorithms before proposing new ones. We approach the topic from three separate angles: fundamental analysis, technical analysis, and machine learning. We discover evidence to support the weak form of the Efficient Market Hypothesis, namely, that the market is efficient. Out of sample, prior prices do not offer valuable information. Data has the potential to anticipate. Any news that is significant to a publicly traded company has an impact on stock movement. We demonstrate the potential of Fundamental Analysis and Machine Learning used to help investors make decisions Machine Learning approaches can help here. Understanding the numerical time analysis Intelligent investors can use machine learning techniques to predict the stock if the series produces close results.

Files

Stock Market Prediction -Formatted Paper.pdf

Files (151.3 kB)

Name Size Download all
md5:f251989a47c77ad8b1b7844e1028b6ea
151.3 kB Preview Download

Additional details

References

  • Karunanayake, N. (2015). OMR sheet evaluation by web camera using template matching approach. International Journal for Research in Emerging Science and Technology, 2(8), 40-44..
  • Wei, P., & Wang, N. (2016, April). Wikipedia and stock return: Wikipedia usage pattern helps to predict the individual stock movement. In Proceedings of the 25th international conference companion on world wide web (pp. 591-594).
  • 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.
  • Malagrino, L. S., Roman, N. T., & Monteiro, A. M. (2018). Forecasting stock market index daily direction: A Bayesian Network approach. Expert Systems with Applications, 105, 11-22.
  • Patel, M. B., & Yalamalle, S. R. (2014). Stock price prediction using artificial neural network. International Journal of Innovative Research in Science, Engineering and Technology, 3(6), 13755-13762.
  • Wang, J., Wang, J., Fang, W., & Niu, H. (2016). Financial time series prediction using elman recurrent random neural networks. Computational intelligence and neuroscience, 2016.