Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 11 Sayı: 1, 13 - 25, 30.04.2022
https://doi.org/10.54187/jnrs.979836

Öz

Kaynakça

  • 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

Yıl 2022, Cilt: 11 Sayı: 1, 13 - 25, 30.04.2022
https://doi.org/10.54187/jnrs.979836

Öz

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.

Kaynakça

  • 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
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Articles
Yazarlar

Üzeyir Aycel 0000-0003-0847-9418

Yunus Santur 0000-0002-8942-4605

Erken Görünüm Tarihi 30 Nisan 2022
Yayımlanma Tarihi 30 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 11 Sayı: 1

Kaynak Göster

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. Nisan 2022;11(1):13-25. doi:10.54187/jnrs.979836
Chicago Aycel, Üzeyir, ve Yunus Santur. “A New Moving Average Approach to Predict the Direction of Stock Movements in Algorithmic Trading”. Journal of New Results in Science 11, sy. 1 (Nisan 2022): 13-25. https://doi.org/10.54187/jnrs.979836.
EndNote Aycel Ü, Santur Y (01 Nisan 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 ve Y. Santur, “A New moving average approach to predict the direction of stock movements in algorithmic trading”, JNRS, c. 11, sy. 1, ss. 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 (Nisan 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 ve Yunus Santur. “A New Moving Average Approach to Predict the Direction of Stock Movements in Algorithmic Trading”. Journal of New Results in Science, c. 11, sy. 1, 2022, ss. 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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