Improving Heikin-Ashi Transformation Data Learning in Neural Network Learning Using Volume Weight in Stock Market Data

Authors

  • Nishchal Sharma  Research Scholar, School of Engineering and Technology, Career Point University, Kota, Rajasthan, India
  • Dr. Chaman S. Chauhan  Assistant Professor, Dept. of Computer Science & Application, Govt. College Chamba Himachal Pradesh, India

Keywords:

Heikin Ashi Transformation, Neural Network, Stock Market Analysis, Time series filtering

Abstract

This paper provides a further analysis and improvement of Heikin Ashi Transformation for Neural Network Learning. It has been demonstrated that Heikin Ashi Transformation can improve the learning effect of Neural Network. This paper introduces another improvement using volume-weighted data for learning and its effect for neural network learning process.

References

  1. Adam, K., Marcet, A., Nicolini, J.P., 2016. Stock Market Volatility and Learning. J. Finance 71, 33-82.
  2. Brownstone, D., 1996. Using percentage accuracy to measure neural network predictions in Stock Market movements. Neurocomputing, Financial Applications, Part II 10, 237-250.
  3. Enke, C.G., Nieman, T.A., 1976. Signal-to-noise ratio enhancement by least-squares polynomial smoothing. Anal. Chem. 48, 705A-712A.
  4. Guresen, E., Kayakutlu, G., Daim, T.U., 2011. Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38, 10389-10397.
  5. Haykin, S. (Ed.), 2001. Frontmatter and Index, in: Kalman Filtering and Neural Networks. John Wiley & Sons, Inc., pp. i-xviii.
  6. Hosseinioun, N., 2016. Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm. J. Math. Finance 06, 127-133.
  7. Howe, J.S., 1986. Evidence on Stock Market Overreaction. Financ. Anal. J. 42, 74-77.
  8. Improving community analysis with the Beals smoothing function: Écoscience: Vol 1, No 1).
  9. Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M., 1990. Stock market prediction system with modular neural networks, in: , 1990 IJCNN International Joint Conference on Neural Networks, 1990. Presented at the , 1990 IJCNN International Joint Conference on Neural Networks, 1990, pp. 1-6 vol.1.
  10. Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P., 1985. Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165-177.
  11. Qiu, M., Song, Y., 2016. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLOS ONE 11,
  12. Sahambi, J.S., Tandon, S.N., Bhatt, R.K.P., 1997. Using wavelet transforms for ECG characterization. An on-line digital signal processing system. IEEE Eng. Med. Biol. Mag. 16, 77-83.
  13. Wan, E.A., Merwe, R.V.D., 2000. The unscented Kalman filter for nonlinear estimation, in: Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000. pp. 153-158.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Nishchal Sharma, Dr. Chaman S. Chauhan, " Improving Heikin-Ashi Transformation Data Learning in Neural Network Learning Using Volume Weight in Stock Market Data, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1809-1812, January-February-2018.