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The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

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  • Monira Essa Aloud

Abstract

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.

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  • Monira Essa Aloud, 2020. "The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(2), pages 43-54, April.
    • Handle: RePEc:wly:isacfm:v:27:y:2020:i:2:p:43-54
    DOI: 10.1002/isaf.1466
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    1. Kim, Kyungsik & Yoon, Seong-Min & Kim, Yup, 2004. "Herd behaviors in the stock and foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 341(C), pages 526-532.
    2. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    3. Christian L. Dunis & Jason Laws & Andreas Karathanasopoulos, 2013. "GP algorithm versus hybrid and mixed neural networks," The European Journal of Finance, Taylor & Francis Journals, vol. 19(3), pages 180-205, March.
    4. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    7. Serge Hayward, 2006. "Genetically Optimised Artificial Neural Network for Financial Time Series Data Mining," Computing in Economics and Finance 2006 417, Society for Computational Economics.
    8. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    9. Georgios Vasilakis & Konstantinos Theofilatos & Efstratios Georgopoulos & Andreas Karathanasopoulos & Spiros Likothanassis, 2013. "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 415-431, December.
    10. Iryna Veryzhenko & Lise Arena & Etienne Harb & Nathalie Oriol, 2017. "Time to Slow Down for High‐Frequency Trading? Lessons from Artificial Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 73-79, April.
    11. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    12. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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