IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v13y2024i1p6-d1558024.html
   My bibliography  Save this article

Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach

Author

Listed:
  • Saima Latif

    (Department of Management Sciences, COMSATS University, Park Road, Islamabad 45550, Pakistan)

  • Faheem Aslam

    (Department of Management Sciences, COMSATS University, Park Road, Islamabad 45550, Pakistan
    School of Business Administration (SBA), Al Akhawayn University, Ifrane 53003, Morocco
    VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal)

  • Paulo Ferreira

    (VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal
    Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal
    CEFAGE-UE, IIFA, University of Évora, Largo dos 2 Colegiais, 7000-809 Évora, Portugal)

  • Sohail Iqbal

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

Abstract

Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour.

Suggested Citation

  • Saima Latif & Faheem Aslam & Paulo Ferreira & Sohail Iqbal, 2024. "Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach," Economies, MDPI, vol. 13(1), pages 1-28, December.
  • Handle: RePEc:gam:jecomi:v:13:y:2024:i:1:p:6-:d:1558024
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/13/1/6/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/13/1/6/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    2. Aye, Goodness C. & Balcilar, Mehmet & Demirer, Riza & Gupta, Rangan, 2018. "Firm-level political risk and asymmetric volatility," The Journal of Economic Asymmetries, Elsevier, vol. 18(C), pages 1-1.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    4. Vera Ivanyuk, 2022. "Methodology for Constructing an Experimental Investment Strategy Formed in Crisis Conditions," Economies, MDPI, vol. 10(12), pages 1-19, December.
    5. Chiang, Thomas C., 2019. "Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets," Finance Research Letters, Elsevier, vol. 29(C), pages 41-49.
    6. Leo Krippner, 2015. "A comment on Wu and Xia (2015), and the case for two-factor Shadow Short Rates," CAMA Working Papers 2015-48, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    8. Claus, Edda & Claus, Iris & Krippner, Leo, 2018. "Asset market responses to conventional and unconventional monetary policy shocks in the United States," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 270-282.
    9. Turan G. Bali & Stephen J. Brown & Yi Tang, 2022. "Disagreement in economic forecasts and equity returns: risk or mispricing?," China Finance Review International, Emerald Group Publishing Limited, vol. 13(3), pages 309-341, August.
    10. Black, Fischer, 1995. "Interest Rates as Options," Journal of Finance, American Finance Association, vol. 50(5), pages 1371-1376, December.
    11. Edda Claus & Iris Claus & Leo Krippner, 2014. "Asset markets and monetary policy shocks at the zero lower bound," Reserve Bank of New Zealand Discussion Paper Series DP2014/03, Reserve Bank of New Zealand.
    12. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
    13. Ma, Chenyao & Yan, Sheng, 2022. "Deep learning in the Chinese stock market: The role of technical indicators," Finance Research Letters, Elsevier, vol. 49(C).
    14. Fadekemi Chidinma Adeloye & Olayinka Olawoyin & Chinaemerem Daniel, 2024. "Economic Policy Uncertainty and Financial Markets in the United State," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 998-1016, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Costola, Michele & Lorusso, Marco, 2022. "Spillovers among energy commodities and the Russian stock market," Journal of Commodity Markets, Elsevier, vol. 28(C).
    2. Das, Debojyoti & Kannadhasan, M. & Bhattacharyya, Malay, 2019. "Do the emerging stock markets react to international economic policy uncertainty, geopolitical risk and financial stress alike?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 1-19.
    3. Hong, Yun & Zhang, Rushan & Zhang, Feipeng, 2024. "Time-varying causality impact of economic policy uncertainty on stock market returns: Global evidence from developed and emerging countries," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Costantini, Mauro & Sousa, Ricardo M., 2022. "What uncertainty does to euro area sovereign bond markets: Flight to safety and flight to quality," Journal of International Money and Finance, Elsevier, vol. 122(C).
    5. repec:wsr:wpaper:y:2019:i:189 is not listed on IDEAS
    6. Xiao, Jihong & Jiang, Jiajie & Zhang, Yaojie, 2024. "Policy uncertainty, investor sentiment, and good and bad volatilities in the stock market: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    7. Salah A. Nusair & Jamal A. Al-Khasawneh, 2023. "Changes in oil price and economic policy uncertainty and the G7 stock returns: evidence from asymmetric quantile regression analysis," Economic Change and Restructuring, Springer, vol. 56(3), pages 1849-1893, June.
    8. Kannadhasan, M. & Das, Debojyoti, 2020. "Do Asian emerging stock markets react to international economic policy uncertainty and geopolitical risk alike? A quantile regression approach," Finance Research Letters, Elsevier, vol. 34(C).
    9. Lealand Morin & Ying Shang, 2021. "Federal Reserve policy after the zero lower bound: an indirect inference approach," Empirical Economics, Springer, vol. 60(4), pages 2105-2124, April.
    10. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    11. Wen, Fenghua & Shui, Aojie & Cheng, Yuxiang & Gong, Xu, 2022. "Monetary policy uncertainty and stock returns in G7 and BRICS countries: A quantile-on-quantile approach," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 457-482.
    12. Tan, Xueping & Zhong, Yiran & Vivian, Andrew & Geng, Yong & Wang, Ziyi & Zhao, Difei, 2024. "Towards an era of multi-source uncertainty: A systematic and bibliometric analysis," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    13. Yonghong Jiang & Gengyu Tian & Yiqi Wu & Bin Mo, 2022. "Impacts of geopolitical risks and economic policy uncertainty on Chinese tourism‐listed company stock," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 320-333, January.
    14. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    15. Alessandro Paolo Rigamonti & Giulio Greco & Mariarita Pierotti & Alessandro Capocchi, 2024. "Macroeconomic uncertainty and earnings management: evidence from commodity firms," Review of Quantitative Finance and Accounting, Springer, vol. 62(4), pages 1615-1649, May.
    16. Zhibing Li & Jia Liu & Jie Liu & Xiaoyu Liu & Yinglun Zhu, 2024. "The causal effect of political risk on the stock market: Evidence from a natural experiment," Australian Economic Papers, Wiley Blackwell, vol. 63(1), pages 145-162, March.
    17. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    18. Philip Barrett & Mariia Bondar & Sophia Chen & Mali Chivakul & Deniz Igan, 2024. "Pricing protest: the response of financial markets to social unrest," Review of Finance, European Finance Association, vol. 28(4), pages 1419-1450.
    19. Jia, Lijun & Xu, Ruoyu & Wu, Jian & Song, Malin & Chen, Xueli, 2023. "Impacts of geopolitical risk and economic policy uncertainty on metal futures price volatility: Evidence from China," Resources Policy, Elsevier, vol. 87(PB).
    20. Nonejad, Nima, 2022. "An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample," Finance Research Letters, Elsevier, vol. 47(PB).
    21. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2021. "Investigating the asymmetric impact of oil prices on GCC stock markets," Economic Modelling, Elsevier, vol. 102(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecomi:v:13:y:2024:i:1:p:6-:d:1558024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.