Research Article
BibTex RIS Cite

Makroekonomik faktörlerle finansal oranlar arasındaki bağlantı: Gelişmekte olan ülkelerde şirket karlılığının bütünsel analizi

Year 2023, Volume: 22 Issue: 48 - İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Aralık/Güz 2023, Cilt 22, Sayı 48, 1101 - 1123, 30.12.2023
https://doi.org/10.46928/iticusbe.1347449

Abstract

Çalışmanın Amacı; Bu çalışmanın temel amacı, şirket karlılığına etki eden makroekonomik faktörler, ilgili endeksler (dışsal değişkenler olarak), finansal oran göstergeleri (şirket içi değişkenler olarak) arasındaki örüntüleri bütüncül bir yaklaşımla ortaya çıkarmaktır. Bu araştırma aynı zamanda BİST’e (Istanbul Menkul Kıymetler Borsası) kayıtlı gıda, kimya ve metal eşya sektörlerinde faaliyet gösteren borsaya kote üretim şirketlerinin kar elde edebilmesi için bu değişkenlerin alacağı eşik değerleri ve marjlarını netleştirmeyi amaçlamaktadır. Araştırma kapsamına Haziran 2007-Aralık 2022 döneminde net kar marjı sıfırdan büyük olan şirketler alınmıştır.
Metodoloji: Çalışmada KNIME (The Konstanz Information Miner) Veri Analiz Platformu üzerinde denetimli makine öğrenmesi algoritmaları kullanılmıştır. Rastgele Orman ve Gradyan Artırma algoritmalarıyla başarılı bir model sunulmuştur.
Bulgular: Rastgele Orman Algoritmasıyla şirketlerin kar/zarar etme olasılığını tahmin etmeye yönelik on belirgin kural elde edilmiştir.
Uygulamaya yönelik sonuçlar: Bu çalışmadan elde edilen bulgular, karar vericilerle doğrudan bağlantılı olacak şekilde, kar elde etmek için değişkenlerin değerlerinin farklı kombinasyonlarını sunmaktadır. Ayrıca, finansal göstergelerin eşik değerlerinin elde edilmesiyle iç ve dış faktörlere ilişkin bilgilerimizin derinleştirilmesi ve gelişmekte olan ülke piyasalarının daha iyi anlaşılması beklenmektedir.
Özgünlük: Önceki çalışmalar çoğunlukla iki veya üç makro değişkenin seçilen şirket finansal oranlarıyla ilişkisi üzerine yoğunlaşmıştır. Ayrıca, gelişmekte olan ülke piyasaları üzerine yapılan çalışmalar oldukça azdır ve bunların yok denecek kadar azında makine öğrenimi algoritmaları kullanılmıştır. Bu çalışma, bütüncül bir yaklaşımla değişkenlerin şirket karı üzerinde ne yönde rol oynadığını göstermeyi amaçlamaktadır. Kâr elde etmek için bağımsız değişkenlerin değerlerinin farklı kombinasyonları, ülkeye özgü piyasa koşulları altında eşik değerleri ile birlikte değerlendirilecektir.

References

  • Almansour, A. Y., Alzoubi, H. M., Almansour, B. Y., & Almansour, Y. M. (2021). The effect of inflation on performance: an empirical investigation on the banking sector in Jordan. The Journal of Asian Finance, Economics and Business, 8(6), 97-102.
  • Alomari, M. W., & Azzam, I. A. (2017). Effect of the micro and macro factors on the performance of the listed Jordanian insurance companies. International Journal of Business and Social Science, 8(2), 66-73.
  • Arilyn, E. J. (2020, January). The effect of financial ratios and macroeconomic variables to financial distress of agriculture industry listed in the Indonesia Stock Exchange from 2013 to 2018. In 17th International Symposium on Management (INSYMA 2020) (pp. 347-349). Atlantis Press.
  • Aydin, G., & Silahtaroglu, G. (2021). Insights into mobile health application market via a content analysis of marketplace data with machine learning. PloS one, 16(1), e0244302.
  • Beckmann, J., Berger, T., & Czudaj, R. (2019). Gold price dynamics and the role of uncertainty. Quantitative Finance, 19(4), 663-681.
  • Bjørnbet, M. M., Skaar, C., Fet, A. M., & Schulte, K. Ø. (2021). Circular economy in manufacturing companies: A review of case study literature. Journal of Cleaner Production, 294, 126268.
  • Brunnermeier, M. K., Correi̇a, S. A., Luck, S. ve Verner Zımmermann, E. &. T. (2023). The debt-inflation channel of the German hyperinflation. Nati̇onal Bureau of Economi̇c Research, (31298). https://www.nber.org/papers/w31298
  • Cheong, C., & Hoang, H. V. (2021). Macroeconomic factors or firm-specific factors? An examination of the impact on corporate profitability before, during and after the global financial crisis. Cogent Economics & Finance, 9(1), 1959703.
  • Çöllü, D. A., Akgün, L., & Eyduran, E. (2020). Financial failure prediction with decision tree algorithms: textile, wearing apparel and leather sector application. International Journal of Economics and Innovation, 6(2), 225-246.
  • Dewı, V. I., Soeı, C. T. L. & F. O. S. (2019). The ımpact of macroeconomic factors on firms\' profitability (evidence from fast moving consumer good firms listed on Indonesian Stock Exchange). Academy Of Accounti̇ng and Fi̇nanci̇al Studi̇es Journal, 23(1), 327.
  • Doruk, Ö. T. (2023). Macroeconomic determinants of firm performance: Evidence from Turkey. The Singapore Economic Review, 68(01), 177-196.
  • Egbunike, C. F., & Okerekeoti, C. U. (2018). Macroeconomic factors, firm characteristics, and financial performance: A study of selected quoted manufacturing firms in Nigeria. Asian Journal of Accounting Research, 3(2), 142-168.
  • Ekren, N., Erdoğan, M. F. ve Bi̇̇ldi̇̇k, K. H. (2020). Makroekonomi̇k performansın ki̇şi̇ başına düşen göstergelerle alternati̇f anali̇zi̇. İ̇stanbul Ti̇caret Üni̇versi̇tesi̇ Sosyal Bi̇li̇mler Dergi̇si̇, Bahar 2020/1(37), 493-514. https://dergipark.org.tr/en/download/article-file/1123786
  • Ferrati, F., & Muffatto, M. (2021). Entrepreneurial finance: emerging approaches using machine learning and big data. Foundations and Trends® in Entrepreneurship, 17(3), 232-329.
  • Friedman, J. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5), 1189-1232. https://www.jstor.org/stable/2699986
  • Giustiziero, G., Kretschmer, T., Somaya, D., & Wu, B. (2023). Hyperspecialization and hyperscaling: A resource‐based theory of the digital firm. Strategic Management Journal, 44(6), 1391-1424.
  • Gustriansyah, R., Alie, J., & Suhandi, N. (2023). Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods. Quality & Quantity, 57(2), 1725-1737.
  • Issah, M., & Antwi, S. (2017). Role of macroeconomic variables on firms’ performance: Evidence from the UK. Cogent Economics & Finance, 5(1), 1405581.
  • Kırıkkaleli, D., & Güngör, H. (2021). Co-movement of commodity price indexes and energy price indice: a wavelet coherence approach. Financial Innovation, 7(1), 1-18.
  • Kogan, L., & Tian, M. H. (2012). Firm characteristics and empirical factor models: a data-mining experiment. FRB International Finance discussion paper, (1070).
  • Lee, J., Chang, J. R., Kao, L. J., & Lee, C. F. (2023). Financial Analysis, Planning, and Forecasting. In Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning (pp. 433-455). Cham: Springer International Publishing.
  • Madushanka, K. H., & Jathurika, M. (2018). The impact of liquidity ratios on profitability. International Research Journal of Advanced Engineering and Science, 3(4), 157-161.
  • Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet], 9(1), 381-386.
  • Ningsih, S., & Sari, S. P. (2019). Analysis of the effect of liquidity ratios, solvability ratios and profitability ratios on firm value in go public companies in the automotive and component sectors. International Journal of Economics, Business and Accounting Research (IJEBAR), 3(04).
  • Oral, E., & Tuncel, S. Ö. (2006). Real Sector Confidence Index from the Business Tendency Survey of CBRT. Central Bank of the Republic of Turkey.
  • Ozpolat, A. (2020). Causal link between consumer prices index and producer prices index: An Evidence from central and Eastern European Countries (CEECs). Adam Academy Journal of Social Sciences, 10(2), 319-332.
  • Pervan, M., Pervan, I., & Ćurak, M. (2019). Determinants of firm profitability in the Croatian manufacturing industry: evidence from dynamic panel analysis. Economic research-Ekonomska istraživanja, 32(1), 968-981.
  • Rehman, K. U, Shaikh, A. H., AliRaza, and Soomro, Y. A. (2021). Macroeconomics ındicators & financial performance of firms: A study of the sugar industry in Pakistan. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(5), 12A5J, 1-11.
  • Sahoo, P., & Bishnoi, A. (2023). Drivers of corporate investment in India: The role of firm-specific factors and macroeconomic policy. Economic Modelling, 125, 106330.
  • Shrivastava, A., Jaın, J. & Chauhan, D. (2023). Literature review on tools & applications of data mining. International Journal of Computer Sciences and Engineering, 11(4), 46-54.
  • Silahtaroğlu, G., & Alayoglu, N. (2016). Using or not using business intelligence and big data for strategic management: an empirical study based on interviews with executives in various sectors. Procedia-Social and Behavioral Sciences, 235, 208-215.
  • Widagdo, B., Jihadi, M., Bachitar, Y., Safitri, O. ve Singh, S. (2020). Financial ratio, macro economy, and investment risk on sharia stock return. The Journal of Asian Finance, Economics and Business, 7(12), 919-926.
  • Yuliastari, M., Najmudin, ve Dewi, M. K. (2022). The influence of financial ratios and macroeconomic indicators in predicting financial distress (empirical study in the consumer goods sector companies. International Sustainable Competitiveness Advantage, 207-217.
  • Zulfiqar, Z., & Din, N. U. (2015). Inflation, Interest rate and firms’ performance: the evidence from textile industry of Pakistan. International Journal of Arts and Commerce, 4(2), 111-115.

EXPLORING THE NEXUS OF MACROECONOMIC FACTORS AND FINANCIAL RATIOS: A HOLISTIC ANALYSIS OF COMPANY PROFITABILITY IN DEVELOPING MARKETS

Year 2023, Volume: 22 Issue: 48 - İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Aralık/Güz 2023, Cilt 22, Sayı 48, 1101 - 1123, 30.12.2023
https://doi.org/10.46928/iticusbe.1347449

Abstract

Purpose of the Study; The primary objective of this document is to find out the patterns among macroeconomic factors, related indexes (as external variables), financial ratio indicators (as internal drivers) that had impact on company’s profit with a holistic approach. This research also aims to clarify the threshold values and the margin of these variables to achieve profit for the listed manufacturing companies which are registered to BIST (Istanbul Stock Exchange) and operating in food, chemistry and metalware sectors. The companies which have net profit margin greater than zero are taken into the pool of investigation for the period from June 2007 to December 2022.
Methodology; The study utilized supervised machine learning algorithms on KNIME Analytics Platform (The Konstanz Information Miner). A successful model has been achieved by using Random Forest Learner and Gradient Boosted Trees Learner Algorithms.
Findings; Ten prominent rules have been extracted by Random Forest algorithm to predict profit/loss probability of companies.
Practical implications; The findings derived from this study have direct relevance for decision makers by formulating the values of variables in different combinations to earn profit. Besides, the threshold values of the financial indicators which deepens our knowledge of the internal and external factors is expected to provide a better insight on the markets of developing countries.
Originality/Value; Previous studies are mostly concentrated on the relationship of two or three macro variables with the chosen financial ratios of the companies. Besides a few studies were conducted on the markets of developing countries and if not none of them, very few of them have employed machine learning algorithms. This study aims to show what direction the variables play a role on company’s profit with a holistic approach. The diverse combination of the values of independent variables to generate profit will be evaluated with their threshold values under the country specific conditions of the markets.

References

  • Almansour, A. Y., Alzoubi, H. M., Almansour, B. Y., & Almansour, Y. M. (2021). The effect of inflation on performance: an empirical investigation on the banking sector in Jordan. The Journal of Asian Finance, Economics and Business, 8(6), 97-102.
  • Alomari, M. W., & Azzam, I. A. (2017). Effect of the micro and macro factors on the performance of the listed Jordanian insurance companies. International Journal of Business and Social Science, 8(2), 66-73.
  • Arilyn, E. J. (2020, January). The effect of financial ratios and macroeconomic variables to financial distress of agriculture industry listed in the Indonesia Stock Exchange from 2013 to 2018. In 17th International Symposium on Management (INSYMA 2020) (pp. 347-349). Atlantis Press.
  • Aydin, G., & Silahtaroglu, G. (2021). Insights into mobile health application market via a content analysis of marketplace data with machine learning. PloS one, 16(1), e0244302.
  • Beckmann, J., Berger, T., & Czudaj, R. (2019). Gold price dynamics and the role of uncertainty. Quantitative Finance, 19(4), 663-681.
  • Bjørnbet, M. M., Skaar, C., Fet, A. M., & Schulte, K. Ø. (2021). Circular economy in manufacturing companies: A review of case study literature. Journal of Cleaner Production, 294, 126268.
  • Brunnermeier, M. K., Correi̇a, S. A., Luck, S. ve Verner Zımmermann, E. &. T. (2023). The debt-inflation channel of the German hyperinflation. Nati̇onal Bureau of Economi̇c Research, (31298). https://www.nber.org/papers/w31298
  • Cheong, C., & Hoang, H. V. (2021). Macroeconomic factors or firm-specific factors? An examination of the impact on corporate profitability before, during and after the global financial crisis. Cogent Economics & Finance, 9(1), 1959703.
  • Çöllü, D. A., Akgün, L., & Eyduran, E. (2020). Financial failure prediction with decision tree algorithms: textile, wearing apparel and leather sector application. International Journal of Economics and Innovation, 6(2), 225-246.
  • Dewı, V. I., Soeı, C. T. L. & F. O. S. (2019). The ımpact of macroeconomic factors on firms\' profitability (evidence from fast moving consumer good firms listed on Indonesian Stock Exchange). Academy Of Accounti̇ng and Fi̇nanci̇al Studi̇es Journal, 23(1), 327.
  • Doruk, Ö. T. (2023). Macroeconomic determinants of firm performance: Evidence from Turkey. The Singapore Economic Review, 68(01), 177-196.
  • Egbunike, C. F., & Okerekeoti, C. U. (2018). Macroeconomic factors, firm characteristics, and financial performance: A study of selected quoted manufacturing firms in Nigeria. Asian Journal of Accounting Research, 3(2), 142-168.
  • Ekren, N., Erdoğan, M. F. ve Bi̇̇ldi̇̇k, K. H. (2020). Makroekonomi̇k performansın ki̇şi̇ başına düşen göstergelerle alternati̇f anali̇zi̇. İ̇stanbul Ti̇caret Üni̇versi̇tesi̇ Sosyal Bi̇li̇mler Dergi̇si̇, Bahar 2020/1(37), 493-514. https://dergipark.org.tr/en/download/article-file/1123786
  • Ferrati, F., & Muffatto, M. (2021). Entrepreneurial finance: emerging approaches using machine learning and big data. Foundations and Trends® in Entrepreneurship, 17(3), 232-329.
  • Friedman, J. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5), 1189-1232. https://www.jstor.org/stable/2699986
  • Giustiziero, G., Kretschmer, T., Somaya, D., & Wu, B. (2023). Hyperspecialization and hyperscaling: A resource‐based theory of the digital firm. Strategic Management Journal, 44(6), 1391-1424.
  • Gustriansyah, R., Alie, J., & Suhandi, N. (2023). Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods. Quality & Quantity, 57(2), 1725-1737.
  • Issah, M., & Antwi, S. (2017). Role of macroeconomic variables on firms’ performance: Evidence from the UK. Cogent Economics & Finance, 5(1), 1405581.
  • Kırıkkaleli, D., & Güngör, H. (2021). Co-movement of commodity price indexes and energy price indice: a wavelet coherence approach. Financial Innovation, 7(1), 1-18.
  • Kogan, L., & Tian, M. H. (2012). Firm characteristics and empirical factor models: a data-mining experiment. FRB International Finance discussion paper, (1070).
  • Lee, J., Chang, J. R., Kao, L. J., & Lee, C. F. (2023). Financial Analysis, Planning, and Forecasting. In Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning (pp. 433-455). Cham: Springer International Publishing.
  • Madushanka, K. H., & Jathurika, M. (2018). The impact of liquidity ratios on profitability. International Research Journal of Advanced Engineering and Science, 3(4), 157-161.
  • Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet], 9(1), 381-386.
  • Ningsih, S., & Sari, S. P. (2019). Analysis of the effect of liquidity ratios, solvability ratios and profitability ratios on firm value in go public companies in the automotive and component sectors. International Journal of Economics, Business and Accounting Research (IJEBAR), 3(04).
  • Oral, E., & Tuncel, S. Ö. (2006). Real Sector Confidence Index from the Business Tendency Survey of CBRT. Central Bank of the Republic of Turkey.
  • Ozpolat, A. (2020). Causal link between consumer prices index and producer prices index: An Evidence from central and Eastern European Countries (CEECs). Adam Academy Journal of Social Sciences, 10(2), 319-332.
  • Pervan, M., Pervan, I., & Ćurak, M. (2019). Determinants of firm profitability in the Croatian manufacturing industry: evidence from dynamic panel analysis. Economic research-Ekonomska istraživanja, 32(1), 968-981.
  • Rehman, K. U, Shaikh, A. H., AliRaza, and Soomro, Y. A. (2021). Macroeconomics ındicators & financial performance of firms: A study of the sugar industry in Pakistan. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(5), 12A5J, 1-11.
  • Sahoo, P., & Bishnoi, A. (2023). Drivers of corporate investment in India: The role of firm-specific factors and macroeconomic policy. Economic Modelling, 125, 106330.
  • Shrivastava, A., Jaın, J. & Chauhan, D. (2023). Literature review on tools & applications of data mining. International Journal of Computer Sciences and Engineering, 11(4), 46-54.
  • Silahtaroğlu, G., & Alayoglu, N. (2016). Using or not using business intelligence and big data for strategic management: an empirical study based on interviews with executives in various sectors. Procedia-Social and Behavioral Sciences, 235, 208-215.
  • Widagdo, B., Jihadi, M., Bachitar, Y., Safitri, O. ve Singh, S. (2020). Financial ratio, macro economy, and investment risk on sharia stock return. The Journal of Asian Finance, Economics and Business, 7(12), 919-926.
  • Yuliastari, M., Najmudin, ve Dewi, M. K. (2022). The influence of financial ratios and macroeconomic indicators in predicting financial distress (empirical study in the consumer goods sector companies. International Sustainable Competitiveness Advantage, 207-217.
  • Zulfiqar, Z., & Din, N. U. (2015). Inflation, Interest rate and firms’ performance: the evidence from textile industry of Pakistan. International Journal of Arts and Commerce, 4(2), 111-115.
There are 34 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Article
Authors

Dilek Yomralıoğlu 0000-0003-2114-935X

Gökhan Silahtaroğlu 0000-0001-8863-8348

Publication Date December 30, 2023
Submission Date August 21, 2023
Acceptance Date December 25, 2023
Published in Issue Year 2023 Volume: 22 Issue: 48 - İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Aralık/Güz 2023, Cilt 22, Sayı 48

Cite

APA Yomralıoğlu, D., & Silahtaroğlu, G. (2023). EXPLORING THE NEXUS OF MACROECONOMIC FACTORS AND FINANCIAL RATIOS: A HOLISTIC ANALYSIS OF COMPANY PROFITABILITY IN DEVELOPING MARKETS. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 22(48), 1101-1123. https://doi.org/10.46928/iticusbe.1347449