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Cited by 1Financial Risk and Volatility ModelingFinancial Markets and Investment StrategiesCOVID-19 Pandemic Impacts

Unpacking leverage and lagged effects: do digitally mature and complex firms outpace their rivals?

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Daruwala, Zaheda, Khan, Faisal, Ullah Jan, Sharif·2025·Cogent Economics & Finance
DOI 10.1080/23322039.2025.2577909
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Abstract

This study explores whether digitally transformed firms outperform non-digital counterparts in terms of stock return volatility asymmetry and leverage behaviour, focusing on firm-specific characteristics such as complexity and maturity within UAE firms. By examining the lagged effects of negative news on these behaviours, the study captures differences in market responses over time. Utilizing a firm-level Exponential Generalized Autoregressive Conditional Heteroskedasticity (E-GARCH) model and stock returns data from 2012 to 2024, the research analyses 661 firms (368 digitalised and 293 non-digitalised), categorised further by firm complexity and maturity. Leverage effects are evaluated across multiple lags, with significant findings emerging particularly at lag intervals four and five, indicating the relevance of historical return patterns and supporting theories of market underreaction and delayed information processing. To ensure robustness, Variance Decomposition Analysis (VDA) and Impulse Response Analysis (IRA) were applied, reinforcing the credibility of the results. Digitally transformed firms demonstrated strategic advantages through the use of digital technologies to enhance efficiency, expand market reach and innovate more rapidly, thereby positioning themselves competitively. The impact of macroeconomic variables, including GDP growth, inflation, and oil price fluctuations, was consistent with existing literature. These findings offer practical insights for investors seeking to optimise portfolio strategies by understanding leverage gaps and time-lagged market reactions between digital and non-digital firms. Moreover, policymakers may apply these insights to design more informed macroeconomic and industrial strategies. The study contributes to the growing literature on digital transformation by linking volatility dynamics with firm maturity and complexity in the UAE context.

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  • Organizations with high resource and capability endowments can amplify the perceived value of an AI system by providing reliable outputs, decreasing false positives, and increasing trust in automated decision-making.

    “Conversely, organizations with high levels of resource and capability endowments can amplify the perceived value of an AI system by providing reliable outputs, decreasing false positives, and increasing trust in the use of automated decision-making (Daruwala et al. 2025).”

    From AI-driven financial fraud detection in Pakistan’s banking sector: bridging strategic intent and operational implementation (2026) · cites this paper as Daruwala et al. 2025

    Section 2.1 says AI can enhance strategic decision-making, and Section 3.7 includes AI in a measure of digital maturity. Neither section addresses whether resource and capability endowments make AI outputs more reliable, reduce false positives, or increase trust in automated decisions. Searches for those terms found no passage supporting the attributed mechanism; page numbers were not available in our copy. (LLM, conf=0.72)

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1 retracted

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Research works printed but not linked: 27

  1. Abubakr, A. A. M., Khan, F., Mohammed, A. A. A., Abdalla, Y. A., Mohammed, A. A. A., & Ahmad, Z. (2024). Impact of AI applications on corporate financial reporting quality: Evidence from UAE corporations. Qubahan Academic Journal, 4(3), 782-792.
  2. Ahmad, N., & Ramzan, M. (2016). Stock market volatility and macroeconomic factor volatility. International Journal of Research in Business Studies and Management, 3(7), 37-44.
  3. Ahmed Khamis, A. S. A., Joseph, A., Asif, M. K., Hock, O. Y., & Mohammad Imtiaz, H. (2020). Influence on internal con- trol through the digitalization of assets: A study on Ministry of Interior, UAE. International Journal of Academic Research in Accounting, Finance and Management Sciences, 10(1), 13-24.
  4. Alali, F., & Haddad, I. (2023). Digital transformation and financial markets: The role of fintech adoption in emerging economies. International Journal of Financial Studies, 11(2), 45-62.
  5. Alqasimi, A., Alshamsi, H., & Khan, M. (2022a). Digital innovation and market volatility: Evidence from the UAE. Journal of Economic Perspectives, 20(3), 101-120.
  6. Alqasimi, A., Khan, M., & Jan, S. (2022b). The impact of digital financial services on investor behaviour: An empirical study of UAE stock markets. Financial Innovation Journal, 9(1), 55-70.
  7. Black, F. (1976). Studies of Stock Price Volatility Changes. Proceedings of the 1976 Meetings of the American Statistical Association. Business and Economical Statistics Section, 177-191.
  8. Deeds, D. L., Mang, P. Y., Frandsen, M. L., & Tilleman, S. G. (2000). The impact of firm-specific capabilities on the amount of capital raised in an initial public offering: Evidence from the biotechnology industry. Journal of Business Venturing, 15(3), 259-283.
  9. Demirer, R., Kutan, A. M., & Chen, C. F. (2016). Do country-level financial structures explain differences in firm-level volatility? Journal of International Financial Markets, Institutions and Money, 45, 84-97.
  10. Fathi, S., Nonegad, M., Zare, H., & Haghighat, A. (2023). A survey of the asymmetric effects of oil price shocks and real exchange rate fluctuation on economic growth and inflation in selected countries of the Islamic conference: EGARCH approach. Journal of Economics and Regional Development, 30(25), 250-290.
  11. Gupta, I., & Garg, V. (2025). Overconfidence and its consequences in financial markets. In Unveiling Investor Biases That Shape Market Dynamics (pp. 195-224). IGI Global Scientific Publishing.
  12. Huang, Y., & Zhang, X. (2022). Digital finance and stock market fluctuations: A cross-country analysis. Journal of Financial Economics, 140(1), 50-72.
  13. Khan, F., Khan, S. U. R., & Khan, H. (2016). Pricing of risk, various volatility dynamics and macroeconomic exposure of firm returns: New evidence on age effect. International Journal of Economics and Financial Issues, 6(2), 551-561.
  14. Khedhiri, S., & Muhammad, M. (2008). Empirical analysis of the UAE stock market volatility. International Research Journal of Finance and Economics, 15, 241-252.
  15. Kilian, L., & Zhou, X. (2019). Oil prices and stock returns in a time-varying world. Journal of Financial Economics, 132(3), 615-636.
  16. Mangani, R. (2008). Modelling return volatility on the JSE securities exchange of South Africa. African Finance Journal, 10(1), 55-71.
  17. Moallemi, M., & Rahjoo, M. (2023). The relationship between risk and return based on the prospect theory: Case study of selected companies in Tehran Stock Exchange. Iranian Economic Review, 27(3), 789-821.
  18. Oskoee, S. A. P., & Shamsavari, A. (2011). Asymmetric effects in emerging stock markets-The case of Iran Stock Market. International Journal of Economics and Finance, 3(6), 16-24.
  19. Padma, Y. (2022). Stock market in UAE. Journal of Positive School Psychology, 10621-10627.
  20. Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133.
  21. Qezelbash, M., Tajdini, S., Jafari, F., Lotfi Ghahroud, M., & Farajnezhad, M. (2023). An analysis of volatility and herd behavior among investors in the S&P500 stock market index, Bitcoin, and gold markets. Journal of Mathematics and Modeling in Finance, 3(2), 77-92.
  22. Roswinna, W., Yulianti, M. L., Suharyono, O., Noviana, A., Kusumawaardani, A., Putri, K. P., & Pertiwi, R. D. (2023). Banking company stock price model. International Journal of Advanced Multidisciplinary, 2(1), 16-23.
  23. su15032077 · DOI
  24. Verma, S., & Mahajan, N. (2012). Stock return, volatility and the global financial meltdown: The behavior of Indian stock market. International Journal of Arts and Commerce, 1(7), 166-178.
  25. Wang, Y., & Wang, Y. (2019). Forecasting stock return volatility: A variance decomposition approach. Journal of Economic Dynamics and Control, 100, 99-112.
  26. Zhao, J., & Kong, L. (2023). Regulatory responses to digital financial transformation: Lessons from emerging markets. Global Finance Journal, 15(2), 33-52.
  27. Zuckerman, A. (2020). Bitcoin insurance? The emerging market for digital asset insurance. The emerging market for digital asset insurance (May 15, 2020). Plus Journal,

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Authors
Daruwala, Z., Khan, F., & Ullah Jan, S.
Affiliations
  • City University Ajman (AE)
  • Department of Management Sciences, FATA University
Corresponding author
Khan, Faisal
ORCID
Venue
Cogent Economics & Finance
Publisher
Informa UK Limited
Published
2025-10-29
Volume
13
Issue
1
ISSN
2332-2039 (ISSN-L)
DOI
10.1080/23322039.2025.2577909
OpenAlex
W4415675092
Cited by
1
References
92
Citations by year
2026: 1
FWCI
0.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Cogent Economics & Finance (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
Keywords
digital transformation, firm complexity, firm maturity, leverage, stock return volatility, asymmetric volatility, negative news, market underreaction, macroeconomic determinants of stock returns, oil price fluctuations

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