Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, , 369 - 390, 31.10.2021
https://doi.org/10.24289/ijsser.990376

Öz

Kaynakça

  • Aggarwal, R. K., & Wu, G. (2003, March). Stock market manipulation-theory and evidence. In AFA 2004 San Diego Meetings.
  • Angenieux, G. (1964). Les Ratios et L’Expansiyon de L’Entreprise. Paris: Dunod.
  • Atalay, M., & Çelik, E. (2017). Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamalari-Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172.
  • Barnes, P. (2016). Stock market efficiency, insider dealing and market abuse. CRC Press.
  • Berman, K., Knight, J., & Case, J. (2008). Financial intelligence for entrepreneurs: What you really need to know about the numbers. Harvard Business Press.
  • Bhagat, S. and B. Black (2001): Board independence and long-term firm performance, Journal of Corporation Law 27, 231–273. Boudriga, & Ghachem, (2016). Does US stock market react differently to rating announcements during crisis period? The case of the 2008 worldwide financial crisis. American Journal of Finance and Accounting, 4(3-4), 193-214.
  • Brigham, E. F., & Houston, J. F. (2012). Fundamentals of financial management. Cengage Learning.
  • Brownlee, J. (2019, September 13). Introduction to Python deep learning with Keras. Retrieved from https://machinelearningmastery.com/introduction-python-deep-learning-library-keras/ , Access Date: 25.06.2020.
  • Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512.
  • Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018). Which artificial intelligence algorithm better predicts the Chinese stock market. IEEE Access, 6, 48625-48633.
  • Çelik, S. (2018). Büyük Veri. Gece Kitaplığı. Ankara. ISBN: 978-605-288-811-7.
  • Dase, R. K., & Pawar, D. D. (2010). Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), 14-17.
  • Drake, P. P., & Fabozzi, F. J. (2012). Financial ratio analysis. Encyclopedia of Financial Models.
  • Eiteman, A., & Stonehill, D. (1986). Multinational Business Finance. California: Addison Wesley Publishing.
  • Giacomino, D. E., & Mielke, D. E. (1993). Cash Flows: Another Approach to Ratio Analysis. Journal of Accountancy, 55-57.
  • Gibbons, J. D. (1976). Nonparametric Methods for Quantitative Analysis. New York: Holt, Rinehart and Winston.
  • Gordan, R. (2000). Macroeconomics. Addison-Wesley.
  • Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia computer science, 132, 1351-1362.
  • Howell, C. D. (1992). Statistical Methods For Psychology. California: Duxbury Press.
  • Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
  • Khan, Z. H., Alin, T. S., & Hussain, M. A. (2011). Price prediction of share market using artificial neural network (ANN). International Journal of Computer Applications, 22(2), 42-47.
  • Kızıl, A., Fidan, M., Kızıl, C., & Keskin, İ. (2013). Türkiye Muhasebe ve Finansal Raporlama Standartları. İstanbul: Der Yayınları.
  • Krantz, M. (2016). Fundamental analysis for dummies. John Wiley & Sons.
  • Machado, M. A. V., & de Medeiros, O. R. (2013). Does the liquidity effect exist in the brazilian stock market?. Available at SSRN 2217941.
  • Mansfield, E. (1998). Essential Microeconomics. Norton & Company.
  • Næs, T., Kvaal, K., Isaksson, T., & Miller, C. (1993). Artificial neural networks in multivariate calibration. Journal of Near Infrared Spectroscopy, 1(1), 1-11.
  • Needles, B. E., Powers, M., & Crosson, S. V. (2013). Financial and managerial accounting. Nelson Education.
  • Nikolai, L. A., Bazley, J. D., & Jones, J. P. (2009). Intermediate Accounting (Book Only). Cengage Learning.
  • Patel, M. B., & Yalamalle, S. R. (2014). Stock price prediction using artificial neural network. International Journal of Innovative Research in Science, Engineering and Technology, 3(6), 13755-13762.
  • Pathmind (2020). A beginner's guide to neural networks and deep learning. Retrieved from https://wiki.pathmind.com/neural-network
  • Peterson & Fabozzi, (1999). Analysis of financial statements (Vol. 54). John Wiley & Sons.
  • Peterson, P. (2006). Financial Management and Analysis. New York: McGraw-Hill Inc.
  • Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals, 85, 1-7.
  • Rouse, M. (2019, August 29). What is an artificial neural network (ANN)?. Retrieved from https://searchenterpriseai.techtarget.com/definition/neural-network.
  • Samuelson, P. A., & William D, N. (1995). Economics. McGr Angenieux, G. (1964). Les Ratios et L’Expansiyon de L’Entreprise. Paris: Dunod.
  • Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569-18584.
  • Sexton, R. L. (2015). Exploring economics. Cengage Learning.
  • Shim, J. K., & Siegel, J. G. (1988). Handbook of Financial Analysis,Forecasting & Modeling. New Jersey: Prentice-Hall.
  • Spiegel, M. R. (1972). Theory and Problems of Statistics. New York: McGraw-Hill Book Company.
  • Taner B., Akaya G. C. (2004): Sermaye Piyasası, Faaliyet Alanı ve Menkul Kıymetler
  • Thomas, R., & Gup, B. E. (2010). The valuation handbook: valuation techniques from today's top practitioners (Vol. 480). John Wiley & Sons.
  • Tracy, A. (2012). Ratio analysis fundamentals: how 17 financial ratios can allow you to analyse any business on the planet. RatioAnalysis. net.
  • Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017, June). Deep learning for stock market prediction from financial news articles. In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for
  • Measurement Systems and Applications (CIVEMSA) (pp. 60-65). IEEE.
  • Willems, K. (2019). (Tutorial) KERAS tutorial: DEEP learning in PYTHON. Retrieved from https://www.datacamp.com/community/tutorials/deep-learning-python.
  • Yetis, Y., Kaplan, H., & Jamshidi, M. (2014, August). Stock market prediction by using artificial neural network. In 2014 World Automation Congress (WAC) (pp. 718-722). IEEE.
  • Zhang, Y., & Wu, L. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert systems with applications, 36(5), 8849-8854.

Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example

Yıl 2021, , 369 - 390, 31.10.2021
https://doi.org/10.24289/ijsser.990376

Öz

One of the main investment instruments that attract foreign and domestic investors is the stocks of companies. The Istanbul Stock Exchange, which has been serving more than 300 companies with a volume of billions of dollars since 1985, offers various opportunities to international investors. Of course, investors have to apply to many analyzes of companies and companies in order to evaluate these opportunities correctly and to make a profit. Therefore, it is of great importance for millions of investors around the world that firm and market data are highly transparent, consistent and predictable. Because otherwise, markets that cannot show stability in developing countries cause the market values of the company to rise and fall excessively with the opportunities and crises that constantly occur. This situation leads to unnoticeable manipulations in firm and market values and investors to be mistaken due to the inability to analyze them correctly. The purpose of this research is to estimate the market values of 2019 Borsa Istanbul firms by using the Artificial Neural Networks (ANN) method with the data of Forbes 2000 companies in 2019. As a result of the analysis, it was revealed that the companies in Borsa Istanbul have higher or lower market values than they should be, and the results are also supported by the comparisons of ratio analysis.

Kaynakça

  • Aggarwal, R. K., & Wu, G. (2003, March). Stock market manipulation-theory and evidence. In AFA 2004 San Diego Meetings.
  • Angenieux, G. (1964). Les Ratios et L’Expansiyon de L’Entreprise. Paris: Dunod.
  • Atalay, M., & Çelik, E. (2017). Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamalari-Artificial Intelligence and Machine Learning Applications in Big Data Analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172.
  • Barnes, P. (2016). Stock market efficiency, insider dealing and market abuse. CRC Press.
  • Berman, K., Knight, J., & Case, J. (2008). Financial intelligence for entrepreneurs: What you really need to know about the numbers. Harvard Business Press.
  • Bhagat, S. and B. Black (2001): Board independence and long-term firm performance, Journal of Corporation Law 27, 231–273. Boudriga, & Ghachem, (2016). Does US stock market react differently to rating announcements during crisis period? The case of the 2008 worldwide financial crisis. American Journal of Finance and Accounting, 4(3-4), 193-214.
  • Brigham, E. F., & Houston, J. F. (2012). Fundamentals of financial management. Cengage Learning.
  • Brownlee, J. (2019, September 13). Introduction to Python deep learning with Keras. Retrieved from https://machinelearningmastery.com/introduction-python-deep-learning-library-keras/ , Access Date: 25.06.2020.
  • Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512.
  • Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018). Which artificial intelligence algorithm better predicts the Chinese stock market. IEEE Access, 6, 48625-48633.
  • Çelik, S. (2018). Büyük Veri. Gece Kitaplığı. Ankara. ISBN: 978-605-288-811-7.
  • Dase, R. K., & Pawar, D. D. (2010). Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), 14-17.
  • Drake, P. P., & Fabozzi, F. J. (2012). Financial ratio analysis. Encyclopedia of Financial Models.
  • Eiteman, A., & Stonehill, D. (1986). Multinational Business Finance. California: Addison Wesley Publishing.
  • Giacomino, D. E., & Mielke, D. E. (1993). Cash Flows: Another Approach to Ratio Analysis. Journal of Accountancy, 55-57.
  • Gibbons, J. D. (1976). Nonparametric Methods for Quantitative Analysis. New York: Holt, Rinehart and Winston.
  • Gordan, R. (2000). Macroeconomics. Addison-Wesley.
  • Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia computer science, 132, 1351-1362.
  • Howell, C. D. (1992). Statistical Methods For Psychology. California: Duxbury Press.
  • Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
  • Khan, Z. H., Alin, T. S., & Hussain, M. A. (2011). Price prediction of share market using artificial neural network (ANN). International Journal of Computer Applications, 22(2), 42-47.
  • Kızıl, A., Fidan, M., Kızıl, C., & Keskin, İ. (2013). Türkiye Muhasebe ve Finansal Raporlama Standartları. İstanbul: Der Yayınları.
  • Krantz, M. (2016). Fundamental analysis for dummies. John Wiley & Sons.
  • Machado, M. A. V., & de Medeiros, O. R. (2013). Does the liquidity effect exist in the brazilian stock market?. Available at SSRN 2217941.
  • Mansfield, E. (1998). Essential Microeconomics. Norton & Company.
  • Næs, T., Kvaal, K., Isaksson, T., & Miller, C. (1993). Artificial neural networks in multivariate calibration. Journal of Near Infrared Spectroscopy, 1(1), 1-11.
  • Needles, B. E., Powers, M., & Crosson, S. V. (2013). Financial and managerial accounting. Nelson Education.
  • Nikolai, L. A., Bazley, J. D., & Jones, J. P. (2009). Intermediate Accounting (Book Only). Cengage Learning.
  • Patel, M. B., & Yalamalle, S. R. (2014). Stock price prediction using artificial neural network. International Journal of Innovative Research in Science, Engineering and Technology, 3(6), 13755-13762.
  • Pathmind (2020). A beginner's guide to neural networks and deep learning. Retrieved from https://wiki.pathmind.com/neural-network
  • Peterson & Fabozzi, (1999). Analysis of financial statements (Vol. 54). John Wiley & Sons.
  • Peterson, P. (2006). Financial Management and Analysis. New York: McGraw-Hill Inc.
  • Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals, 85, 1-7.
  • Rouse, M. (2019, August 29). What is an artificial neural network (ANN)?. Retrieved from https://searchenterpriseai.techtarget.com/definition/neural-network.
  • Samuelson, P. A., & William D, N. (1995). Economics. McGr Angenieux, G. (1964). Les Ratios et L’Expansiyon de L’Entreprise. Paris: Dunod.
  • Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569-18584.
  • Sexton, R. L. (2015). Exploring economics. Cengage Learning.
  • Shim, J. K., & Siegel, J. G. (1988). Handbook of Financial Analysis,Forecasting & Modeling. New Jersey: Prentice-Hall.
  • Spiegel, M. R. (1972). Theory and Problems of Statistics. New York: McGraw-Hill Book Company.
  • Taner B., Akaya G. C. (2004): Sermaye Piyasası, Faaliyet Alanı ve Menkul Kıymetler
  • Thomas, R., & Gup, B. E. (2010). The valuation handbook: valuation techniques from today's top practitioners (Vol. 480). John Wiley & Sons.
  • Tracy, A. (2012). Ratio analysis fundamentals: how 17 financial ratios can allow you to analyse any business on the planet. RatioAnalysis. net.
  • Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017, June). Deep learning for stock market prediction from financial news articles. In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for
  • Measurement Systems and Applications (CIVEMSA) (pp. 60-65). IEEE.
  • Willems, K. (2019). (Tutorial) KERAS tutorial: DEEP learning in PYTHON. Retrieved from https://www.datacamp.com/community/tutorials/deep-learning-python.
  • Yetis, Y., Kaplan, H., & Jamshidi, M. (2014, August). Stock market prediction by using artificial neural network. In 2014 World Automation Congress (WAC) (pp. 718-722). IEEE.
  • Zhang, Y., & Wu, L. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert systems with applications, 36(5), 8849-8854.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makaleleri
Yazarlar

Çağrı Köroğlu Bu kişi benim 0000-0003-4073-1847

Cahit İncioğlu 0000-0003-2449-5346

Zeynep Aydın 0000-0002-3358-0000

Yayımlanma Tarihi 31 Ekim 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Köroğlu, Ç., İncioğlu, C., & Aydın, Z. (2021). Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example. International Journal of Social Sciences and Education Research, 7(4), 369-390. https://doi.org/10.24289/ijsser.990376
AMA Köroğlu Ç, İncioğlu C, Aydın Z. Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example. International Journal of Social Sciences and Education Research. Ekim 2021;7(4):369-390. doi:10.24289/ijsser.990376
Chicago Köroğlu, Çağrı, Cahit İncioğlu, ve Zeynep Aydın. “Comparative Analysis of Estimated Market Values of Companies: Forbes 2000 and BIST 388 Company Example”. International Journal of Social Sciences and Education Research 7, sy. 4 (Ekim 2021): 369-90. https://doi.org/10.24289/ijsser.990376.
EndNote Köroğlu Ç, İncioğlu C, Aydın Z (01 Ekim 2021) Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example. International Journal of Social Sciences and Education Research 7 4 369–390.
IEEE Ç. Köroğlu, C. İncioğlu, ve Z. Aydın, “Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example”, International Journal of Social Sciences and Education Research, c. 7, sy. 4, ss. 369–390, 2021, doi: 10.24289/ijsser.990376.
ISNAD Köroğlu, Çağrı vd. “Comparative Analysis of Estimated Market Values of Companies: Forbes 2000 and BIST 388 Company Example”. International Journal of Social Sciences and Education Research 7/4 (Ekim 2021), 369-390. https://doi.org/10.24289/ijsser.990376.
JAMA Köroğlu Ç, İncioğlu C, Aydın Z. Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example. International Journal of Social Sciences and Education Research. 2021;7:369–390.
MLA Köroğlu, Çağrı vd. “Comparative Analysis of Estimated Market Values of Companies: Forbes 2000 and BIST 388 Company Example”. International Journal of Social Sciences and Education Research, c. 7, sy. 4, 2021, ss. 369-90, doi:10.24289/ijsser.990376.
Vancouver Köroğlu Ç, İncioğlu C, Aydın Z. Comparative analysis of estimated market values of companies: Forbes 2000 and BIST 388 company example. International Journal of Social Sciences and Education Research. 2021;7(4):369-90.

88x31.png

Bu eser Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır.