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An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

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  • O. B. Sezer
  • M. Ozbayoglu
  • E. Dogdu

Abstract

In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.

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  • O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
    • Handle: RePEc:arx:papers:1712.09592
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    Cited by:

    1. Sangyeon Kim & Myungjoo Kang, 2019. "Financial series prediction using Attention LSTM," Papers 1902.10877, arXiv.org.
    2. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
    3. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    4. Yash Thesia & Vidhey Oza & Priyank Thakkar, 2022. "A dynamic scenario‐driven technique for stock price prediction and trading," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 653-674, April.
    5. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    6. Ma, Chenyao & Yan, Sheng, 2022. "Deep learning in the Chinese stock market: The role of technical indicators," Finance Research Letters, Elsevier, vol. 49(C).
    7. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Jim-Min Lin & Yen-Lin Chen, 2022. "A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application," Mathematics, MDPI, vol. 10(8), pages 1-13, April.
    8. Yong Shi & Bo Li & Wen Long & Wei Dai, 2022. "Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1027-1068, March.
    9. Mostafa Shabani & Martin Magris & George Tzagkarakis & Juho Kanniainen & Alexandros Iosifidis, 2022. "Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots," Papers 2210.14605, arXiv.org, revised Nov 2022.

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