Investigation of Micro and Macro Economic Factors Affecting Corporate Financial Performance: A Fuzzy Dimensional Approach

Subject Areas : Journal of Investment Knowledge

Ebrahim Alizadeh ¹ , HamidReza Vakilifard ² , mohsen hamidian ³

1 - Department of Accounting, Kish International Unit, Islamic Azad University, Kish Island, Iran
2 - Associate Professor, Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Asistant Professor of Accounting, Islamic Azad University Tehran South, Iran

Received: 2020-11-10 Accepted : 2021-01-04 Published : 2022-03-21

Keywords: microeconomic factors, macroeconomic factors, corporate financial performance,

Abstract :

Financial indicators are good benchmarks for policymakers who want to assess the current state of the economy and predict the future, especially for creditors and the central bank, and there are several reasons to justify this. The data on which the financial indices are calculated is essentially defined by looking at the future and possibly taking into account market expectations of the macro data. Financial indicators may also directly affect the future state of the economy or be influenced by macroeconomic and micro indicators. This study attempts to conduct a systematic and comprehensive study to identify all the measures that may be likely to affect profitability and other key indicators of financial performance and provide a complete database of these measures. For this purpose, a combination of knowledge domain and content analysis methods has been used to select effective metrics. Finally, the most effective factors are determined through interviews with experts and the fuzzy DEMATEL method.

References:

• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.


• Altman, E. I., Zhang, L., & Yen, J. (2007). Corporate financial distress diagnosis in China. New York University Salomon Center, Working Paper.


• Angelopoulou, E., Balfoussia, H., Gibson, H., 2013. Building a Financial Conditions Index for the Euro Area and Selected Euro Area Countries: What Does it Tell us About the Crisis? ECB WP no. 1541.


• Chen, G., M. Firth, D.N. Gao and O.M. Rui (2006) "Ownership structure, corporate governance, and fraud: Evidence from China", Journal of Corporate Finance , vol. 12, Issue 3, pp. 424-448.


• Chun, , Keles, S. , 2010. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J. R. Stat. Soc. B 72, 3–25.


• Dimitras, A., Kyriakou, M. & Latridis G., (2015), “Financial crisis, GDP variation and earning managment in Europe”, Research in International Business and Finance, Vol 34, 338-335.


• Divsalar, M., Javid, M. R., Gandomi, A. H., Soofi, J. B., & Mahmood, M. V. (2011). Hybrid genetic programming-based search algorithms for enterprise bankruptcy prediction. Applied Artificial Intelligence, 25(8), 669-692.


• Du Jardin, P. (2010). Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy. Neurocomputing, 73(10-12), 2047-2060.


• Drezner, Z., Marcoulides, G. A., & Hoven Stohs, M. (2018). Financial applications of a tabu search variable selection model. Journal of Applied Mathematics and Decision Sciences, 5(4), 215-234.


• Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: which factors are reliably important?. Financial management, 38(1), 1-37.


• Giraitis, , Kapetanios, G., Price, S., 2013. Adaptive forecasting in the presence of recent and ongoing structural change. J. Econom. 177, 153–170.


• Gepp, A., Kumar, K., & Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of forecasting, 29(6), 536-555.


• Guichard, S., Haugh, D., Turner, D., 2009. Quantifying the Effect of Financial Conditions in the Euro Area, Japan, United Kingdom and the United States. OECD Economics Working Papers No. 677.


• Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.


• Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K., Watson, M., 2010. Financial Conditions Indexes: A Fresh Look After the Financial Crisis. Working Paper.


• Helbling, , Huidron, R. , Kose, M.A. , Otrok, C. , 2011. Do credit shocks matter? A global perspecive. Eur. Econ. Rev. 55, 340–353.


• Kapetanios, G., Price, S., & Young, G. (2018). A UK financial conditions index using targeted data reduction: forecasting and structural identification. Econometrics and Statistics.


• Lin, C. J. and W. W. Wu (2008). “A Causal Analytical Method for Group Decision-Making under Fuzzy Environment”, Expert Systems with Applications, Vol. 34, No. 1, pp. 205- 213.


• Millard, S., Nicolae, A., 2014. The Effect of the Financial Crisis on TFP Growth: A General Equilibrium Approach. Bank of England Working Paper 502. Mueller, P., 2009. Credit Spreads and Real Activity EFA 2008 Athens Meetings Paper.


• Pantea, M., Gligor, D., & Anis, C. (2014). Economic determinants of Romanian firms’ financial performance. Procedia-Social and Behavioral Sciences, 124, 272-281.


• Paries, M. D., Maurin, L., Moccero, D., 2014. Financial Conditions Index and Credit Supply Shocks for the Euro Area. ECB Working Paper Series 1644. Pearson, K. , On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572.


• Rajan, G.R. and R. Zingales (1995) "What do we know about capital structure? Some evidence from international data", Journal of Finance , pp. 1421-1460.


• Raju, P. S. & Lonial, S. C. (2002). The Impact of Service Quality and Marketing on Financial Performance in the Hospital Industry. Journal of Retailing and Consumer Services, 9, 335-348.


• Ramli, N. A., Latan, H., & Solovida, G. T. (2019). Determinants of capital structure and firm financial performance—A PLS-SEM approach: Evidence from Malaysia and Indonesia. The Quarterly Review of Economics and Finance, 71, 148-160.


• Richardson, F. m. and Davidson, L. F. (2016) ‘An exploration into bankruptcy Discriminant model sensitivity’ , Journal of Business Finance & Accounting, 10(2) (Summer): 195-207


• Rossi, B. , Advances in forecasting under instability. In: Elliott, G., Timmermann, A. (Eds.), Handbook of Economic Forecasting. Elsevier, pp. 1203–1324.


• Santos, J. B., & Brito, L. A. L. (2012). Toward a subjective measurement model for firm performance. BAR-Brazilian Administration Review, 9(SPE), 95-117.


• Shu, Yan, Broadstock, David C. and Xu, Bing. (2013), “The Heterogeneous Impact of Macroeconomic Information on Firms’ Earnings Forecasts”. The British Accounting Review. Vol.45, PP.311–325.


• Stock, J. H., and Watson, M. W. (2002),”Forecasting Using Principal Components From a Large Number of Predictors”, Journal of the American Statistical Association, 97, PP.1167–1179.


• Tsai, C. F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120-127.


• Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies, European Journal of Operational Research, 123 (1), 105–124.


• Zhou, L., Lu, D., & Fujita, H. (2017). The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowledge-Based Systems, 85, 52-61.