Research Article
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Year 2022, Volume: 11 Issue: 1, 13 - 25, 30.04.2022
https://doi.org/10.54187/jnrs.979836

Abstract

References

  • T. Hendershott, C. M. Jones, A. J. Menkveld, Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), (2011) 1–33.
  • M. P. Taylor, H. Allen, The use of technical analysis in the foreign exchange market, Journal of International Money and Finance, 11(3), (1992) 304–314.
  • R. D. Edwards, W. H. C. Bassetti, J. Magee, Technical analysis of stock trends, CRC press, 2012.
  • O. B. Sezer, A. M. Özbayoğlu, Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach, Applied Soft Computing, 70, (2018) 525–538.
  • Y. Santur, Deep learning-based regression approach for algorithmic stock trading: A case study of the BIST30, Gümüşhane University Journal of Science and Technology, 10(4), (2020) 1195–1211.
  • K. Abouloula, B. E. Habil, S. D. Krit, Money management limits to trade by robot trader for automatic trading, International Journal of Engineering, Science and Mathematics, 7(3), (2018) 195–205.
  • L. Wang, H. An, X. Xia, X. Liu, X. Sun, X. Huang, Generating moving average trading rules on the oil futures market with genetic algorithms, Mathematical Problems in Engineering, 2014, (2014) Article ID: 101808, 1–10.
  • M. A. Canela, I. Alegre, A. Ibarra, Moving average trends, In Quantitative Methods for Management, Springer, Cham, (2019) 111–119.
  • O. B. Sezer, M. U. Güdelek, A. M. Özbayoğlu, Financial time series forecasting with deep learning: A systematic literature review: 2005–2019, Applied Soft Computing, 90, (2020) 106181.
  • A. M. Ozbayoglu, M. U. Gudelek, O. B. Sezer, Deep learning for financial applications: A survey, Applied Soft Computing, 93, (2020) 106384, 1–29.
  • E. León-Castro, E. Avilés-Ochoa, J. M. Merigó, A. M. Gil-Lafuente, Heavy moving averages and their application in econometric forecasting, Cybernetics and Systems, 49(1), (2018) 26–43.
  • S. Siami-Namini, N. Tavakoli, A. S. Namin, A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BILSTM, arXiv preprint arXiv:1911.09512, (2019).
  • R. J. Bauer, J. R. Dahlquist, Technical Markets Indicators: Analysis & Performance (Vol. 64), John Wiley & Sons, 1998.
  • N. Gabdrakhmanova, V. Fedin, B. Matsuta, M. Pilgun, The modeling of forecasting new situations in the dynamics of the economic system on the example of several financial indicators, Procedia Computer Science, 186, (2021) 512–520.
  • L. Lusindah, E. Sumirat, Implementation of Fibonacci Retracements and Exponential Moving Average (EMA) Trading Strategy in Indonesia Stock Exchange, European Journal of Business and Management Research, 6(4), (2021) 402–408.
  • TradingView, Web services for traders, is the most popular technical analysis support platform, (2021), https://www.tradingview.com
  • TradingView pine script 4 user manuel, (2021), https://www.tradingview.com/pine-script-docs/en/v4/index.html

A New moving average approach to predict the direction of stock movements in algorithmic trading

Year 2022, Volume: 11 Issue: 1, 13 - 25, 30.04.2022
https://doi.org/10.54187/jnrs.979836

Abstract

Moving averages and indicators derived from these averages are used to predict the future direction the stocks will move. In manual and algorithmic trading, moving averages play a decisive role in decision making. In this study, a new hybrid approach has been developed that can be used as an alternative to moving averages such as SMA, WMA and EMA used in the literature. In BIST30 stocks in Turkey, the proposed method performs better than widely used indicators such as MACD, Stochastic and RSI, commonly used in the literature.

References

  • T. Hendershott, C. M. Jones, A. J. Menkveld, Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), (2011) 1–33.
  • M. P. Taylor, H. Allen, The use of technical analysis in the foreign exchange market, Journal of International Money and Finance, 11(3), (1992) 304–314.
  • R. D. Edwards, W. H. C. Bassetti, J. Magee, Technical analysis of stock trends, CRC press, 2012.
  • O. B. Sezer, A. M. Özbayoğlu, Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach, Applied Soft Computing, 70, (2018) 525–538.
  • Y. Santur, Deep learning-based regression approach for algorithmic stock trading: A case study of the BIST30, Gümüşhane University Journal of Science and Technology, 10(4), (2020) 1195–1211.
  • K. Abouloula, B. E. Habil, S. D. Krit, Money management limits to trade by robot trader for automatic trading, International Journal of Engineering, Science and Mathematics, 7(3), (2018) 195–205.
  • L. Wang, H. An, X. Xia, X. Liu, X. Sun, X. Huang, Generating moving average trading rules on the oil futures market with genetic algorithms, Mathematical Problems in Engineering, 2014, (2014) Article ID: 101808, 1–10.
  • M. A. Canela, I. Alegre, A. Ibarra, Moving average trends, In Quantitative Methods for Management, Springer, Cham, (2019) 111–119.
  • O. B. Sezer, M. U. Güdelek, A. M. Özbayoğlu, Financial time series forecasting with deep learning: A systematic literature review: 2005–2019, Applied Soft Computing, 90, (2020) 106181.
  • A. M. Ozbayoglu, M. U. Gudelek, O. B. Sezer, Deep learning for financial applications: A survey, Applied Soft Computing, 93, (2020) 106384, 1–29.
  • E. León-Castro, E. Avilés-Ochoa, J. M. Merigó, A. M. Gil-Lafuente, Heavy moving averages and their application in econometric forecasting, Cybernetics and Systems, 49(1), (2018) 26–43.
  • S. Siami-Namini, N. Tavakoli, A. S. Namin, A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BILSTM, arXiv preprint arXiv:1911.09512, (2019).
  • R. J. Bauer, J. R. Dahlquist, Technical Markets Indicators: Analysis & Performance (Vol. 64), John Wiley & Sons, 1998.
  • N. Gabdrakhmanova, V. Fedin, B. Matsuta, M. Pilgun, The modeling of forecasting new situations in the dynamics of the economic system on the example of several financial indicators, Procedia Computer Science, 186, (2021) 512–520.
  • L. Lusindah, E. Sumirat, Implementation of Fibonacci Retracements and Exponential Moving Average (EMA) Trading Strategy in Indonesia Stock Exchange, European Journal of Business and Management Research, 6(4), (2021) 402–408.
  • TradingView, Web services for traders, is the most popular technical analysis support platform, (2021), https://www.tradingview.com
  • TradingView pine script 4 user manuel, (2021), https://www.tradingview.com/pine-script-docs/en/v4/index.html
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Üzeyir Aycel 0000-0003-0847-9418

Yunus Santur 0000-0002-8942-4605

Early Pub Date April 30, 2022
Publication Date April 30, 2022
Published in Issue Year 2022 Volume: 11 Issue: 1

Cite

APA Aycel, Ü., & Santur, Y. (2022). A New moving average approach to predict the direction of stock movements in algorithmic trading. Journal of New Results in Science, 11(1), 13-25. https://doi.org/10.54187/jnrs.979836
AMA Aycel Ü, Santur Y. A New moving average approach to predict the direction of stock movements in algorithmic trading. JNRS. April 2022;11(1):13-25. doi:10.54187/jnrs.979836
Chicago Aycel, Üzeyir, and Yunus Santur. “A New Moving Average Approach to Predict the Direction of Stock Movements in Algorithmic Trading”. Journal of New Results in Science 11, no. 1 (April 2022): 13-25. https://doi.org/10.54187/jnrs.979836.
EndNote Aycel Ü, Santur Y (April 1, 2022) A New moving average approach to predict the direction of stock movements in algorithmic trading. Journal of New Results in Science 11 1 13–25.
IEEE Ü. Aycel and Y. Santur, “A New moving average approach to predict the direction of stock movements in algorithmic trading”, JNRS, vol. 11, no. 1, pp. 13–25, 2022, doi: 10.54187/jnrs.979836.
ISNAD Aycel, Üzeyir - Santur, Yunus. “A New Moving Average Approach to Predict the Direction of Stock Movements in Algorithmic Trading”. Journal of New Results in Science 11/1 (April 2022), 13-25. https://doi.org/10.54187/jnrs.979836.
JAMA Aycel Ü, Santur Y. A New moving average approach to predict the direction of stock movements in algorithmic trading. JNRS. 2022;11:13–25.
MLA Aycel, Üzeyir and Yunus Santur. “A New Moving Average Approach to Predict the Direction of Stock Movements in Algorithmic Trading”. Journal of New Results in Science, vol. 11, no. 1, 2022, pp. 13-25, doi:10.54187/jnrs.979836.
Vancouver Aycel Ü, Santur Y. A New moving average approach to predict the direction of stock movements in algorithmic trading. JNRS. 2022;11(1):13-25.


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