Araştırma Makalesi
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Evaluation of Annual Reports by Text Analysis: An Applicaiton in BIST100 Index

Yıl 2024, Sayı: 121, 175 - 188, 16.04.2024

Sedat Çerez Abdullah Kürşat Merter Yavuz Selim Balcıoğlu Gökhan Özer

https://doi.org/10.33203/mfy.1338486

Öz

In this study, the annual reports of 60 non-financial firms in the Borsa İstanbul (BIST) 100 index are examined, and the topics and sentiment tone of the reports are analyzed. Using the Latent Dirichlet Allocation method, it is found that four main topics are differentiated in the annual reports, and approximately 87% of the disclosures in the reports exhibit a positive sentiment and 13% of the disclosures exhibit a negative sentiment, indicating that there is a generally optimistic tone in the reports. This research highlights the potential of text analysis as a tool for extracting meaningful insights from corporate reports and offers a new approach to understanding corporate performance and strategy.

Anahtar Kelimeler

Annual Reports Text Analysis Sentiment Analysis BIST100

Kaynakça

  • Arianpoor, A., & Sahoor, Z. (2022). The impact of business strategy and annual report rea- dability on financial reporting quality. Journal of Asia Business Studies, 17(3), 598-616.
  • Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (745-750)
  • Barth, M. E., Beaver, W. H., & Landsman, W. R. (2001). The relevance of the value relevance literatüre for financial accounting Standard setting: another view. Journal of accounting and economics, 31(1-3), 77-104.
  • Bashri, M. F., & Kusumaningrum, R. (2017). Sentiment analysis using latent dirichlet allocation and topic polarity word cloud visualization.5th International Conference on Information and Communication Technology, 1-5.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Leaming research, 993-1022.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
  • Botosan, C. A. (1997). Disclosure level and the cost of equity Capital. The Accounting Review, 72(3), 323-349.
  • Dempsey, S. J., Harrison, D. M., Luchtenberg, K. F., & Seiler, M. J. (2012). Financial opacity and firm performance: the readability of REIT annual reports. The Journal of Real Estate Finance and Economics, 45, 450-470.
  • Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation. Journal of Accounting and Economics, 64(2- 3), 221-245.
  • Ekinci, E., & Omurca, S. İ. (2017). Ürün özelliklerinin konu modelleme yöntemi ile çıkartıl-ması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 9(1), 51-58.
  • Feuerriegel, S., & Pröllochs, N. (2021). Investor reaction to financial disclosures across topics: An application of latent Dirichlet allocation. Decision Sciences, 52(3), 608-628.
  • Fiandrino, S., & Tonelli, A. (2021). A text-mining analysis on the review of the non-financial reporting directive: bringing value creation for stakeholders into accounting. Sustainability, 1-18.
  • Harymavvan, L, Nasih, M., Ratri, M. C., Soeprajitno, R. R., & Shafie, R. (2020). Sentiment analysis trend on sustainability reporting m ındonesia: evidence from construction ındustry. Journal of Security And Sustainability Issues, 9(3), 1017-1024.
  • Huang, A. H., Lehavy, R., Zang, A. Y., & Zeng, R. (2017). Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach. Management Science, 2833-2855.
  • Lewis, C., & Young, S. (2019). Fad or future? Automated analysis of financial text and its implications for corporate reporting. Accounting and Business Research, 49(5), 587-615.
  • Li, Y., He, J., & Xiao, M. (2019). Risk disclosure in annual reports and corporate investment efficiency. International Review of Economics & Finance, 63, 138-151.
  • Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.
  • Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187-1230.
  • Mucko, P. (2021). Sentiment analysis of CSR disclosures in annual reports of EU companies. Procedia Computer Science, 192, 3351-3359.
  • Pröllochs, N., & Feuerriegel, S. (2020). Business Analytics for strategic management: identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), 1-13.
  • Ranta, M., Ylinen, M., & Jârvenpââ, M. (2022). Machine learning in management accounting research: literatüre review and pathways for the future. European Accounting Review, 1-30.
  • Okere, W., Eluyela, F. D., Bassey, L, & Ajetunmobi, O. (2018). Public sector accounting standards and quality of financial reporting: A case of Ogün state government administration in Nigeria. Business and Management Research Journal, 7(7), 76-81.
  • Qiu, X. Y., Srinivasan, P., & Hu, Y. (2014). Supervised leaming models to predict firm performance with annual reports: An empirical study. Journal of the Association for Information Science and Technology, 65(2), 400-413.
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
  • Yuthas, K., Rogers, R., & Dillard, J. F. (2002). Communicative action and corporate annual reports. Journal of Business Ethics, 141-157.

Yıllık Raporların Metin Analizi İle Değerlendirilmesi: BİST100 Endeksinde Bir Uygulama

Yıl 2024, Sayı: 121, 175 - 188, 16.04.2024

Sedat Çerez Abdullah Kürşat Merter Yavuz Selim Balcıoğlu Gökhan Özer

https://doi.org/10.33203/mfy.1338486

Öz

Bu çalışmada, Borsa İstanbul (BIST) 100 endeksindeki 60 finansal olmayan işletmenin faaliyet raporları incelenerek, raporların içeriğindeki konuların ve duygu tonunun analizi gerçekleştirilmiştir. Gizli Dirichlet Ayırımı yöntemiyle faaliyet raporları içerisinde dört temel konu üzerinde ayrışma sağlandığı ve raporların içerisinde yer alan açıklamaların yaklaşık %87'sinin olumlu, %13'ünün ise olumsuz bir duygu sergilediği tespit edilmekte ve bu da raporlarda genel olarak iyimser bir görünüm içerisinde olduğunu göstermektedir. Bu araştırma, kurumsal raporlardan anlamlı iç görüler elde etmek için bir araç olarak metin analizinin potansiyelinin altını çizmekte ve kurumsal performans ve stratejiyi anlamak için yeni bir yaklaşım sunmaktadır.

Anahtar Kelimeler

Faaliyet Raporları Metin Analizi Duygu Analizi BIST100

Kaynakça

  • Arianpoor, A., & Sahoor, Z. (2022). The impact of business strategy and annual report rea- dability on financial reporting quality. Journal of Asia Business Studies, 17(3), 598-616.
  • Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (745-750)
  • Barth, M. E., Beaver, W. H., & Landsman, W. R. (2001). The relevance of the value relevance literatüre for financial accounting Standard setting: another view. Journal of accounting and economics, 31(1-3), 77-104.
  • Bashri, M. F., & Kusumaningrum, R. (2017). Sentiment analysis using latent dirichlet allocation and topic polarity word cloud visualization.5th International Conference on Information and Communication Technology, 1-5.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Leaming research, 993-1022.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
  • Botosan, C. A. (1997). Disclosure level and the cost of equity Capital. The Accounting Review, 72(3), 323-349.
  • Dempsey, S. J., Harrison, D. M., Luchtenberg, K. F., & Seiler, M. J. (2012). Financial opacity and firm performance: the readability of REIT annual reports. The Journal of Real Estate Finance and Economics, 45, 450-470.
  • Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation. Journal of Accounting and Economics, 64(2- 3), 221-245.
  • Ekinci, E., & Omurca, S. İ. (2017). Ürün özelliklerinin konu modelleme yöntemi ile çıkartıl-ması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 9(1), 51-58.
  • Feuerriegel, S., & Pröllochs, N. (2021). Investor reaction to financial disclosures across topics: An application of latent Dirichlet allocation. Decision Sciences, 52(3), 608-628.
  • Fiandrino, S., & Tonelli, A. (2021). A text-mining analysis on the review of the non-financial reporting directive: bringing value creation for stakeholders into accounting. Sustainability, 1-18.
  • Harymavvan, L, Nasih, M., Ratri, M. C., Soeprajitno, R. R., & Shafie, R. (2020). Sentiment analysis trend on sustainability reporting m ındonesia: evidence from construction ındustry. Journal of Security And Sustainability Issues, 9(3), 1017-1024.
  • Huang, A. H., Lehavy, R., Zang, A. Y., & Zeng, R. (2017). Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach. Management Science, 2833-2855.
  • Lewis, C., & Young, S. (2019). Fad or future? Automated analysis of financial text and its implications for corporate reporting. Accounting and Business Research, 49(5), 587-615.
  • Li, Y., He, J., & Xiao, M. (2019). Risk disclosure in annual reports and corporate investment efficiency. International Review of Economics & Finance, 63, 138-151.
  • Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.
  • Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187-1230.
  • Mucko, P. (2021). Sentiment analysis of CSR disclosures in annual reports of EU companies. Procedia Computer Science, 192, 3351-3359.
  • Pröllochs, N., & Feuerriegel, S. (2020). Business Analytics for strategic management: identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), 1-13.
  • Ranta, M., Ylinen, M., & Jârvenpââ, M. (2022). Machine learning in management accounting research: literatüre review and pathways for the future. European Accounting Review, 1-30.
  • Okere, W., Eluyela, F. D., Bassey, L, & Ajetunmobi, O. (2018). Public sector accounting standards and quality of financial reporting: A case of Ogün state government administration in Nigeria. Business and Management Research Journal, 7(7), 76-81.
  • Qiu, X. Y., Srinivasan, P., & Hu, Y. (2014). Supervised leaming models to predict firm performance with annual reports: An empirical study. Journal of the Association for Information Science and Technology, 65(2), 400-413.
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
  • Yuthas, K., Rogers, R., & Dillard, J. F. (2002). Communicative action and corporate annual reports. Journal of Business Ethics, 141-157.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Finans ve Yatırım (Diğer)
Bölüm Makaleler
Yazarlar

Sedat Çerez 0000-0002-6443-6319

Abdullah Kürşat Merter 0000-0001-6874-1890

Yavuz Selim Balcıoğlu 0000-0001-7138-2972

Gökhan Özer 0000-0002-3255-998X

Erken Görünüm Tarihi 16 Nisan 2024
Yayımlanma Tarihi 16 Nisan 2024
Gönderilme Tarihi 6 Ağustos 2023
Yayımlandığı Sayı Yıl 2024 Sayı: 121

Kaynak Göster

APA Çerez, S., Merter, A. K., Balcıoğlu, Y. S., Özer, G. (2024). Yıllık Raporların Metin Analizi İle Değerlendirilmesi: BİST100 Endeksinde Bir Uygulama. Maliye Ve Finans Yazıları(121), 175-188. https://doi.org/10.33203/mfy.1338486
 Kapak Resmi İndir

Dergi özellikle maliye, finans ve bankacılık alanlarında faaliyet göstermektedir.