IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v28y2021i1p52-71.html
   My bibliography  Save this article

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Author

Listed:
  • Elfadil A. Mohamed
  • Ibrahim Elsiddig Ahmed
  • Riyadh Mehdi
  • Hanan Hussain

Abstract

Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro‐fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014–2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors—return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)—the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

Suggested Citation

  • Elfadil A. Mohamed & Ibrahim Elsiddig Ahmed & Riyadh Mehdi & Hanan Hussain, 2021. "Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 52-71, January.
    • Handle: RePEc:wly:isacfm:v:28:y:2021:i:1:p:52-71
    DOI: 10.1002/isaf.1484
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1484
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.1484?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
    2. Jovic, Srdjan & Smigic Miladinovic, Jasmina & Micic, Radmila & Markovic, Sanja & Rakic, Goran, 2019. "Analysing of exchange rate and gross domestic product (GDP) by adaptive neuro fuzzy inference system (ANFIS)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 333-338.
    3. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2020. "Tehran stock exchange prediction using sentiment analysis of online textual opinions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 22-37, January.
    4. Husain AL‐Omar & Abdullah AL‐Mutairi, 2008. "Bank‐Specific Determinants of Profitability: The case of Kuwait," Journal of Economic and Administrative Sciences, Emerald Group Publishing Limited, vol. 24(2), pages 20-34, December.
    5. Muhammad Zulqarnain Jatoi & Ghulam Shabir & Naqvi Hamad & Nadeem Iqbal & Khan Muhammad, 2014. "A Regressional Impact of Earning per Share on Market Value of Share: A Case Study Cement Industry of Pakistan," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(4), pages 221-227, October.
    6. Myoung Jong Kim & Ingoo Han & Kun Chang Lee, 2004. "Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 43-60, January.
    7. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    8. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    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. Nahida Akter & Ashadun Nobi, 2018. "Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution," JRFM, MDPI, vol. 11(2), pages 1-10, April.
    2. 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.
    3. Meisam Nasrollahi & Jafar Razmi, 2021. "A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty," Operational Research, Springer, vol. 21(1), pages 525-552, March.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    5. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    6. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
    7. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "Tracking economic growth by evolving expectations via genetic programming: A two-step approach," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    9. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    10. Felix Kwame Aveh & Dadson Awunyo-Vitor, 2017. "Firm-specific determinants of stock prices in an emerging capital market: Evidence from Ghana Stock Exchange," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1339385-133, January.
    11. Rashmi Chaudhary & Priti Bakhshi & Hemendra Gupta, 2020. "Volatility in International Stock Markets: An Empirical Study during COVID-19," JRFM, MDPI, vol. 13(9), pages 1-17, September.
    12. S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
    13. Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.
    14. Shah, Mumtaz Hussain & Khan, Sajjad, 2017. "Factors Effecting Commercial Banks Profitability in Pakistan," MPRA Paper 82016, University Library of Munich, Germany.
    15. Manel Hamdi & Walid Chkili, 2019. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers 13, Economic Research Forum, revised 21 Aug 2019.
    16. Sujin Pyo & Jaewook Lee & Mincheol Cha & Huisu Jang, 2017. "Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    17. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
    18. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    19. Irma Setyawati & Sugeng Suroso & Tulus Suryanto & Dini Siti Nurjannah, 2017. "Does Financial Performance of Islamic Banking is better? Panel Data Estimation," European Research Studies Journal, European Research Studies Journal, vol. 0(2A), pages 592-606.
    20. Arief Putranto & Aldrin Herwany & Erman Sumirat, 2012. "The Determinants Of Commercial Bank Profitability In Indonesia," Working Papers in Business, Management and Finance 201202, Department of Management and Business, Padjadjaran University, revised Dec 2012.

    More about this item

    Statistics

    Access and download statistics

    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:wly:isacfm:v:28:y:2021:i:1:p:52-71. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.