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A Novel Financial Forecasting Approach Using Deep Learning Framework

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  • Yunus Santur

    (Firat Üniversitesi: Firat Universitesi)

Abstract

Moving averages, which are calculated with statistical approaches, are obtained from the price, but a horizontal market has noise problems and a trending market has lag problems. Since there is an inverse correlation between noise and delay, it is not possible to completely eliminate it with statistical approaches. In the light of the literature, it is common to obtain the classification accuracy or price estimation using regression in studies on financial forecasting. However, a high classification accuracy or a low predicted error cannot guarantee that the portfolio will win. For this reason, a Backtest process that shows the portfolio gain is also needed. This study focused on obtaining moving averages with a deep learning model instead of using statistical approaches. Better results were obtained when the moving averages were obtained with the proposed approach and the statistical approaches used the Backtest for the same periods. Experimental studies have shown that the PF is improved by an average of 9% and the trend forecast accuracy level reaches 82%.

Suggested Citation

  • Yunus Santur, 2023. "A Novel Financial Forecasting Approach Using Deep Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1341-1392, October.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:3:d:10.1007_s10614-023-10403-5
    DOI: 10.1007/s10614-023-10403-5
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
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    3. Brian F Tivnan & David Rushing Dewhurst & Colin M Van Oort & John H Ring IV & Tyler J Gray & Brendan F Tivnan & Matthew T K Koehler & Matthew T McMahon & David M Slater & Jason G Veneman & Christopher, 2020. "Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-24, January.
    4. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
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