Ahmed A El-Masry

Personal Details

First Name:	Ahmed
Middle Name:	A
Last Name:	El-Masry
Suffix:
RePEc Short-ID:	pel361
	[This author has chosen not to make the email address public]
Terminal Degree:	2006 Alliance Manchester Business School; University of Manchester (from RePEc Genealogy)

Affiliation

Centre for Financial and Corporate Integrity
Coventry University

Coventry, United Kingdom
https://www.coventry.ac.uk/research/areas-of-research/centre-for-financial-and-corporate-integrity/
RePEc:edi:cfcovuk (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Ahmed A. El-Masry, 2004. "The Exchange Rate Exposure of UK Nonfinancial Companies: Industry-Level Analysis," International Finance 0401001, University Library of Munich, Germany.

   Cited by:

   1. Hyde, Stuart J, 2007. "The response of industry stock returns to market, exchange rate and interest rate risks," MPRA Paper 9679, University Library of Munich, Germany.

Articles

1. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.

   Cited by:

   1. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
   2. Shokufe Afzali & Mohamad Mohamadi-Baghmolaei & Sohrab Zendehboudi, 2021. "Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process," Energies, MDPI, vol. 14(21), pages 1-28, November.
   3. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
   4. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
   5. Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
   6. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
   7. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
   8. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
   9. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
   10. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
   11. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
   12. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
   13. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
   14. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
   15. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
   16. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
   17. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
   18. Ahmed S. Baig & Benjamin M. Blau & R. Jared DeLisle, 2022. "Does mutual fund ownership reduce stock price clustering? Evidence from active and index funds," Review of Quantitative Finance and Accounting, Springer, vol. 58(2), pages 615-647, February.
   19. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
   20. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
   21. Roman Matkovskyy & Taoufik Bouraoui, 2019. "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Post-Print hal-02155402, HAL.
       • Roman Matkovskyy & Taoufik Bouraoui, 2019. "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 433-446, June.
   22. Arash Sioofy Khoojine & Mahboubeh Shadabfar & Yousef Edrisi Tabriz, 2022. "A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
   23. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
   24. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
   25. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
   26. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.
   27. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
   28. Abdullah Sultan Al Shammre & Benaissa Chidmi, 2023. "Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models," Energies, MDPI, vol. 16(11), pages 1-24, May.
   29. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
   30. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
   31. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.

2. El-Masry, Ahmed A. & de Mingo-López, Diego Víctor & Matallín-Sáez, Juan Carlos & Tortosa-Ausina, Emili, 2016. "Environmental conditions, fund characteristics, and Islamic orientation: An analysis of mutual fund performance for the MENA region," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 174-197.

   Cited by:

   1. Balli, Faruk & Chowdhury, Md Iftekhar Hasan & de Bruin, Anne, 2022. "Transition to Islamic equities: Systematic risk and Shari'ah compliance," Global Finance Journal, Elsevier, vol. 51(C).
   2. Khan, Abdullah & Rizvi, Syed Aun R. & Ali, Mohsin & Haroon, Omair, 2021. "A survey of Islamic finance research – Influences and influencers," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
   3. Abid, Ilyes & Benlemlih, Mohammed & El Ouadghiri, Imane & Peillex, Jonathan & Urom, Christian, 2023. "Fossil fuel divestment and energy prices: Implications for economic agents," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 1-16.
   4. Mirza, Nawazish & Abbas Rizvi, Syed Kumail & Saba, Irum & Naqvi, Bushra & Yarovaya, Larisa, 2022. "The resilience of Islamic equity funds during COVID-19: Evidence from risk adjusted performance, investment styles and volatility timing," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 276-295.
   5. Delle Foglie, Andrea & Panetta, Ida Claudia, 2020. "Islamic stock market versus conventional: Are islamic investing a ‘Safe Haven’ for investors? A systematic literature review," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).

3. Mojisola Olugbode & Ahmed El-Masry & John Pointon, 2014. "Exchange Rate and Interest Rate Exposure of UK Industries Using First-order Autoregressive Exponential GARCH-in-mean (EGARCH-M) Approach," Manchester School, University of Manchester, vol. 82(4), pages 409-464, July.

   Cited by:

   1. G l ah Gen er elik, 2020. "Volatility Modelling for Tourism Sector Stocks in Borsa Istanbul," International Journal of Economics and Financial Issues, Econjournals, vol. 10(3), pages 158-165.
   2. Laura Ferrando & Román Ferrer & Francisco Jareño, 2017. "Interest Rate Sensitivity of Spanish Industries: A Quantile Regression Approach," Manchester School, University of Manchester, vol. 85(2), pages 212-242, March.
   3. Jiranyakul, Komain, 2016. "Are Thai Equity Index Returns Sensitive to Interest and Exchange Rate Risks?," MPRA Paper 71602, University Library of Munich, Germany.
   4. Ferrer, Román & Bolós, Vicente J. & Benítez, Rafael, 2016. "Interest rate changes and stock returns: A European multi-country study with wavelets," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 1-12.

4. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2013. "Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques," International Journal of Information Management, Elsevier, vol. 33(4), pages 627-641.

   Cited by:

   1. Azra Shamim & Awais Ali Khan & Muhammad Ahsan Qureshi & Hamaad Rafique & Adnan Akhunzada, 2021. "Ride or Not to Ride: Does the Customer Deviate toward Ridesharing?," IJERPH, MDPI, vol. 18(19), pages 1-18, October.

5. Abdou, Hussein A. & Pointon, John & El-Masry, Ahmed & Olugbode, Moji & Lister, Roger J., 2012. "A variable impact neural network analysis of dividend policies and share prices of transportation and related companies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 796-813.

   Cited by:

   1. Wanke, Peter & Azad, M.D. Abul Kalam & Barros, C.P., 2016. "Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach," Research in International Business and Finance, Elsevier, vol. 36(C), pages 485-498.
   2. Hussein A. Abdou & Nouran N. Ellelly & Ahmed A. Elamer & Khaled Hussainey & Hassan Yazdifar, 2021. "Corporate governance and earnings management nexus: Evidence from the UK and Egypt using neural networks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6281-6311, October.
   3. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.

6. Ahmed A. El-Masry & Mojisola Olugbode & John Pointon, 2010. "The exposure of shipping firms’ stock returns to financial risks and oil prices: a global perspective," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(5), pages 453-473, April.

   Cited by:

   1. Ki-Hong Choi & Seong-Min Yoon, 2020. "Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
   2. Pouliasis, Panos K. & Papapostolou, Nikos C. & Kyriakou, Ioannis & Visvikis, Ilias D., 2018. "Shipping equity risk behavior and portfolio management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 178-200.
   3. Alexandros M. Goulielmos, 2015. "The Multi-faceted Character of Risk in Maritime Freight Markets (Panamax) 1996-2012," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 65(1-2), pages 67-86, January-M.
   4. Sunil K. Mohanty & Roar Aadland & Sjur Westgaard & Stein Frydenberg & Hilde Lillienskiold & Cecilie Kristensen, 2021. "Modelling Stock Returns and Risk Management in the Shipping Industry," JRFM, MDPI, vol. 14(4), pages 1-25, April.
   5. Maitra, Debasish & Chandra, Saurabh & Dash, Saumya Ranjan, 2020. "Liner shipping industry and oil price volatility: Dynamic connectedness and portfolio diversification," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
   6. Nektarios A. Michail & Konstantinos D. Melas, 2019. "A cointegrating stock trading strategy: application to listed tanker shipping companies," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-10, December.
   7. Kian-Guan Lim & Michelle Lim, 2020. "Financial performance of shipping firms that increase LNG carriers and the support of eco-innovation," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-25, December.
   8. Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
   9. Bai, Xiwen & Cheng, Liangqi & Iris, Çağatay, 2022. "Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
   10. Sercan Erol, 2017. "Calculation of the freight revenues in Turkey-focused maritime transportation," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(7), pages 815-824, October.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ARA: MENA - Middle East and North Africa (1) 2015-03-22
2. NEP-CFN: Corporate Finance (1) 2004-01-12
3. NEP-EEC: European Economics (1) 2004-01-12
4. NEP-IFN: International Finance (1) 2004-01-12

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