Research Article
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Year 2022, Volume: 11 Issue: 3, 222 - 232, 31.12.2022
https://doi.org/10.54187/jnrs.1185912

Abstract

Supporting Institution

TÜBİTAK

Project Number

121E733

References

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  • E. Özhan, E. Uzun, The Analysis of Big Financial Data Through Artificial Intelligence Methods, Impact of Artificial Intelligence on Governance, Economics and Finance, Springer Nature Singapore, 1, (2021) 51-79.
  • U. Erkan, A precise and stable machine learning algorithm: eigenvalue classification (EigenClass), Neural Computing and Applications, 33(10), (2021) 5381-5392.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical data classification via distance-based similarity measures of fuzzy parameterized fuzzy soft matrices, IEEE Access, 9, (2021) 88583-88601.
  • Ü. Aycel, Y. Santur, A new algorithmic trading approach based on ensemble learning and candlestick pattern recognition in financial assets, Turkish Journal of Science and Technology, 17(2), (2022) 167-184.

A web scraping-based approach for fundamental analysis platform in financial assets

Year 2022, Volume: 11 Issue: 3, 222 - 232, 31.12.2022
https://doi.org/10.54187/jnrs.1185912

Abstract

There are two main benefits of using fundamental analysis for investors and portfolio managers. First, investing in a company with good ratios has lower risks. The second reason is that it is possible to evaluate share prices with internal valuation methods based on ratios. These price valuations can be more meaningful when combined with technical analysis data. Many data terminals provide processes such as fundamental analysis data and price valuation on a paid and licensed basis. However, the balance sheet data of publicly traded markets are publicly available and can be obtained and interpreted by web scraping methods. This study presents an approach in which basic analysis and price evaluation are made with balance sheets and ratios using open-source tools and web scraping.

Project Number

121E733

References

  • A. Altan, S. Karasu, The effect of kernel values in support vector machine to forecasting performance of financial time series, The Journal of Cognitive Systems, 4(1), (2019) 17-21.
  • O. B. Sezer, M. U. Güdelek, A. M. Özbayoğlu, Financial time series forecasting with deep learning: A systematic literature review: 2005–2019, Applied Soft Computing, 90, (2020) 106181.
  • C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong, Interpretable machine learning: Fundamental principles and 10 grand challenges, Statistics Surveys, 16, (2022) 1-85.
  • I. K. Nti, A. F. Adekoya, B. A. Weyori, A systematic review of fundamental and technical analysis of stock market predictions, Artificial Intelligence Review, 53, (2020) 3007-3057.
  • Y. Huang, Y. Gao, Y. Gan, M. Ye, A new financial data forecasting model using genetic algorithm and long short-term memory network, Neurocomputing, 425, (2021) 207-218.
  • Tradingview, Tradingview platform, retrieved from http://www.tradingview.com. Accessed on August 13, 2022.
  • Y. Santur, Candlestick chart based trading system using ensemble learning for financial assets, Sigma Journal of Engineering and Natural Sciences, 40(2), (2022) 370-379.
  • J. Roeder, M. Palmer, J. Muntermann, Data-driven decision-making in credit risk management: The information value of analyst reports, Decision Support Systems, 158, (2022) Article ID: 113770.
  • Fundamental Analysis Ratios, retrieved from https://www.wallstreetmojo.com/ratio-analysis. Accessed on August 13,2022.
  • N. R. Mustika, N. Novrina, Automated Black Box Testing using Selenium Python, International Journal of Computer Science and Software Engineering, 7(9), (2018) 201-204.
  • KAP, Public Disclosure Platform, retrieved from https://www.kap.org.tr/en. Accessed on August 13, 2022.
  • Yahoo Finance. Finance, retrieved from yahoo.com. Accessed on August 13, 2022.
  • Ü. Aycel, Y. Santur, A new moving average approach to predict the direction of stock movements in algorithmic trading, Journal of New Results in Science, 11(1), (2022) 13-25.
  • M. Etemad, A. Soares, P. Mudroch, S. A. Bailey, S. Matwin, Developing an advanced information system to support ballast water management, Management of Biological Invasions, 13(1), (2022) 68-80.
  • Y. Santur, Deep learning-based regression approach for algorithmic stock trading: A case study of the Bist30, Gümüşhane University Journal of Science and Technology, 10(4), (2020) 1195-1211.
  • E. Özhan, E. Uzun, The Analysis of Big Financial Data Through Artificial Intelligence Methods, Impact of Artificial Intelligence on Governance, Economics and Finance, Springer Nature Singapore, 1, (2021) 51-79.
  • U. Erkan, A precise and stable machine learning algorithm: eigenvalue classification (EigenClass), Neural Computing and Applications, 33(10), (2021) 5381-5392.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical data classification via distance-based similarity measures of fuzzy parameterized fuzzy soft matrices, IEEE Access, 9, (2021) 88583-88601.
  • Ü. Aycel, Y. Santur, A new algorithmic trading approach based on ensemble learning and candlestick pattern recognition in financial assets, Turkish Journal of Science and Technology, 17(2), (2022) 167-184.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Yunus Santur 0000-0002-8942-4605

Mustafa Ulaş 0000-0002-0096-9693

Murat Karabatak 0000-0002-6719-7421

Project Number 121E733
Publication Date December 31, 2022
Published in Issue Year 2022 Volume: 11 Issue: 3

Cite

APA Santur, Y., Ulaş, M., & Karabatak, M. (2022). A web scraping-based approach for fundamental analysis platform in financial assets. Journal of New Results in Science, 11(3), 222-232. https://doi.org/10.54187/jnrs.1185912
AMA Santur Y, Ulaş M, Karabatak M. A web scraping-based approach for fundamental analysis platform in financial assets. JNRS. December 2022;11(3):222-232. doi:10.54187/jnrs.1185912
Chicago Santur, Yunus, Mustafa Ulaş, and Murat Karabatak. “A Web Scraping-Based Approach for Fundamental Analysis Platform in Financial Assets”. Journal of New Results in Science 11, no. 3 (December 2022): 222-32. https://doi.org/10.54187/jnrs.1185912.
EndNote Santur Y, Ulaş M, Karabatak M (December 1, 2022) A web scraping-based approach for fundamental analysis platform in financial assets. Journal of New Results in Science 11 3 222–232.
IEEE Y. Santur, M. Ulaş, and M. Karabatak, “A web scraping-based approach for fundamental analysis platform in financial assets”, JNRS, vol. 11, no. 3, pp. 222–232, 2022, doi: 10.54187/jnrs.1185912.
ISNAD Santur, Yunus et al. “A Web Scraping-Based Approach for Fundamental Analysis Platform in Financial Assets”. Journal of New Results in Science 11/3 (December 2022), 222-232. https://doi.org/10.54187/jnrs.1185912.
JAMA Santur Y, Ulaş M, Karabatak M. A web scraping-based approach for fundamental analysis platform in financial assets. JNRS. 2022;11:222–232.
MLA Santur, Yunus et al. “A Web Scraping-Based Approach for Fundamental Analysis Platform in Financial Assets”. Journal of New Results in Science, vol. 11, no. 3, 2022, pp. 222-3, doi:10.54187/jnrs.1185912.
Vancouver Santur Y, Ulaş M, Karabatak M. A web scraping-based approach for fundamental analysis platform in financial assets. JNRS. 2022;11(3):222-3.


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