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Revenue forecast models using hybrid intelligent methods Cover

References

  1. Chen X., Cho T., Dou Y., Lev B., Predicting future earnings changes using machine learning and detailed financial data, Journal of Accounting Research, 60(2), 467–515, 2022.
  2. Chung I.H., Williams D.W., Do M.R., For better or worse? Revenue forecasting with machine learning approaches, Public Performance and Management Review, 45(5), 1133–1154, 2022.
  3. Kureljusic M., Reisch L., Revenue forecasting for European capital market-oriented firms: a comparative prediction study between financial analysts and machine learning models, Corporate Ownership and Control, 19(2), 159–178, 2022.
  4. Lin Y., Li B., Moiser T.M., Griffel L.M., Mahalik M.R., Kwon J., Alam S.M.S., Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques, Journal of Energy Storage, 50, 104123, 2022.
  5. Mousa G.A., Elamir E.A.H., Hussainey K., Using machine learning methods to predict financial performance: Does disclosure tone matter?, International Journal of Disclosure and Governance, 19, 93–112, 2022.
  6. Wasserbacher H., Spindler M., Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls, arXiv:2107.04851, 2021.
  7. Ndikum P., Machine learning algorithms for financial asset price forecasting, arXiv:2004.01504, 2020.
  8. Allen J., Giacoman K., Analytical approaches to macroeconomic forecasting: a study of profits through machine learning and deep learning, https://digital.wpi.edu/pdfviewer/nc580q60t, Accessed: January 1, 2020.
  9. Piispanen N., Sundqvist R., Vuotila R., Matilainen V., Emerging Technology Adoption and Use, Chapter: Applications of Deep Learning in Finance, ISBN: 978-952-03-1572-6, 1–181, 2020.
  10. Huang C.H., Hsieh S.H., Predicting BIM labor cost with random forest and simple linear regression, Automation in Construction, 118(103280), 1–16, 2020.
  11. Papadimitriou A., Patel U., Kim L., Bang G., Nematzadeh A., Liu X., A multi-faceted approach to large scale financial forecasting, Proceedings of the First ACM International Conference on AI in Finance, 15–16 October 2020, New York, USA, 5, 1–8, 2020.
  12. Weytjens H., Lohmann E., Kleinsteuber M., Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet, Electronic Commerce Research, 21, 371–391, 2021.
  13. Rayhan M., Sultana S., Majid A., Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network, B.Sc. Thesis, Brac University, Dhaka/Bangladesh, 1–58, 2019.
  14. Lorenz T., Homburg C., Determinants of analysts’ revenue forecast accuracy, Review of Quantitative Finance and Accounting, 51, 389–431, 2018.
  15. Ding Y., Song X., Zen Y., Forecasting financial condition of Chinese listed companies based on support vector machine, Expert Systems with Applications, 34(4), 3081–3089, 2008.
  16. Cheng Z., Zou C., Dong J., Outlier detection using isolation forest and local outlier factor, Proceedings of the Conference on Research in Adaptive and Convergent Systems, 24–27 September 2019, Chongqing, China, 161–168, 2019.
  17. Ding C., Peng H., Minimum redundancy feature selection from microarray gene expression data, Journal of Bioinformatics and Computational Biology, 03(02), 185–205, 2005.
  18. Kumar K.M., Reddy A.R.M., A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method, Pattern Recognition, 58, 39–48, 2016.
  19. Xu R., Improvements to random forest methodology, Ph.D. Thesis, Iowa State University, Lowa/USA, 1–87, 2013.
Language: English
Page range: 117 - 124
Submitted on: Jun 14, 2023
Accepted on: Jul 22, 2023
Published on: Oct 31, 2023
Published by: Harran University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Gizem Topaloğlu, Tolga Ahmet Kalaycı, Kaan Pekel, Mehmet Fatih Akay, published by Harran University
This work is licensed under the Creative Commons Attribution 4.0 License.