Chapter
May 24, 2022

Identifying Key Variables Explaining the Profit of Construction Companies from Financial Statement Data

Publication: Computing in Civil Engineering 2021

ABSTRACT

Profit is one of the most important factors in managing companies. As construction companies run their business on a project basis, their cash flow differs from that of companies in other industrial sectors. This study empirically analyzes the financial factors and explains the profit of construction companies based on the financial statement data for construction companies in South Korea. This paper presents two approaches for extracting financial independent variables that presumably affect the return on equity (ROE) representing the profitability of a company. The first approach involves conducting an in-depth literature review and extracting variables frequently used in prior research studies. The second approach uses the stepwise logistic regression. In the experiment, the dependent variable, ROE, was predicted by a series of neural network analyses. The experimental results show that the model selecting the independent variables through stepwise logistic regression is more predictive than the model that selects independent variables from the literature review. This study adds empirical evidence of financial factors affecting financial performance in the construction industry.

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REFERENCES

Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., and Bilal, M. (2018). “Systematic review of bankruptcy prediction models: Towards a framework for tool selection.” Expert Systems with Applications, 94, 164–184.
Altman, E. I. (1968). “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance, 23(4), 589–609.
Cheng, M., and Hoang, N. (2015). “Evaluating contractor Financial status using a Hybrid Fuzzy Instance Based Classifier : Case Study in the Construction Industry.” IEEE Transactions on Engineering Management, IEEE, 62(2), 184–192.
Feidakis, A., and Rovolis, A. (2007). “Capital structure choice in European Union: Evidence from the construction industry.” Applied Financial Economics, 17(12), 989–1002.
Frank, M. Z., and Goyal, V. K. (2004). “The effect of market conditions on capital structure adjustment.” Finance Research Letters, 1(1), 47–55.
Harris, M., and Raviv, A. (1991). “The Theory of Capital Structure.” The Journal of Finance, 46(1), 297–355.
Heikal, M., Khaddafi, M., and Ummah, A. (2014). “Influence Analysis of Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), Debt To Equity Ratio (DER), and current ratio (CR), Against Corporate Profit Growth In Automotive In Indonesia Stock Exchange.” International Journal of Academic Research in Business and Social Sciences, 4(12), 101–114.
Im, H., and Choi, J. (2015). “Identifying the Key Financial Ratios to Evaluate Korean Construction Firms’ Soundness.” Journal of the architectural institute of korea, 31(7).
Kim, J.-J., Lee, S.-N., and Choi, C.-G. (2001). “Financial status prediction of the enterprise using a financial variable and a market variable.” The Journal of Korea Tax Accounting Research, 9, 220–241.
Kim, M. H. (2002). “Financial strategy for construction companies to maximize corporate value.” Research Report, Construction & Economy Research Institute of Korea, Seoul, 2~13.
Kremer, C., Rizzuto, R., and Case, J. (2019). Managing by the Numbers: A Commonsense Guide to Understanding and Using Your Company’s Financials. Hachette UK.
Kryzanowski, L., and Galler, M. (1995). “Analysis of Small-Business Financial Statements Using Neural Nets.” Journal of Accounting, Auditing & Finance, 10(1), 147–170.
Kwon, G. (2006). “Empirical analysis on the value relevance of accounting earnings and book value according to the firm size and debt ratio.” Journal of Taxation and Accounting, 7(2), 141–163.
Lam, M. (2004). “Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis.” Decision Support Systems, 37(4), 567–581.
Lee, D.-H., Lim, H.-H., and Choi, J. (2014). “A Study of the Key Financial Factors for the Korean Construction Firms.” Conference on Korea Institue of Construction Engineering and Management (KICEM 2014), Korea Institue of Construction Engineering and Management, Seoul, 66–69.
Lee, S., and Kim, J. (2017). “Impact of Financial Soundness of Construction Industry Companies on Business Performance.” Korean Computers and Accounting Review, 15(2), 101–129.
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A. (2013). “Problems in RMSE-based wave model validations.” Ocean Modelling, Elsevier Ltd, 72, 53–58.
Michaelas, N., Chittenden, F., and Poutziouris, P. (1999). “Financial Policy and Capital Structure Choice in U.K. SMEs: Empirical Evidence from Company Panel Data.” Small Business Economics, 12(2), 113–130.
Ndikumana, L. (1999). “Debt service, financing constraints, and fixed investment: Evidence from panel data.” Journal of Post Keynesian Economics, 21(3), 455–478.
Ng, S. T., Wong, J. M. W., and Zhang, J. (2011). “Applying Z-score model to distinguish insolvent construction companies in China.” Habitat International, Elsevier Ltd, 35(4), 599–607.
Oh, J., and Yeo, G. (2019). “An analysis of Financial Factors ’ Characteristic for Global Shipping.” 17(4), 65–73.
Opler, T., Pinkowitz, L., Stulz, R., and Williamson, R. (1999). “The determinants and implications of corporate cash holdings.” Journal of Financial Economics.
Piotroski, J. D. (2000). “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” Journal of Accounting Research, 38, 1.
Shin, K. S., Lee, T. S., and Kim, H. J. (2005). “An application of support vector machines in bankruptcy prediction model.” Expert Systems with Applications, 28(1), 127–135.
Singla, H. K., and Samanta, P. K. (2018). “Determinants of dividend payout of construction companies: a panel data analysis.” Journal of Financial Management of Property and Construction, 24(1), 19–38.
Teo, T. T., Logenthiran, T., Woo, W. L., and Abidi, K. (2017). “Forecasting of photovoltaic power using regularized ensemble Extreme Learning Machine.” IEEE Region 10 Annual International Conference, Proceedings/TENCON, IEEE, 455–458.
Trigueros, J. (2000). “Extracting earning information from financial statements via genetic algorithms.” Conference on Computational Intelligence for Financial Engineering (CIFEr), 580–583.
Tsai, C. F. (2009). “Feature selection in bankruptcy prediction.” Knowledge-Based Systems, Elsevier B.V., 22(2), 120–127.
Wang, S.-C. (2003). “Artificial neural network.” Interdisciplinary computing in java programming, Springer, Boston, MA, 81–100.
de Wet, J. H. V., and du Toit, E. (2007). “Return on equity: A popular, but flawed measure of corporate financial performance.” South African Journal of Business Management, 38(1), 59–69.
Xu, M., and Gao, Y. (2016). “Research about the Impact of Debt Financing on Real Estate Listed Corporations’ Financial Performance.” ICCREM, 97–102.
Zahra, M. M., Essai, M. H., and Ellah, A. R. A. (2014). “Performance Functions Alternatives of Mse for Neural Networks Learning.” (volume 3), pp-967–970.
Zeitun, R., and Tian, G. (2007). “Capital structure and corporate performance: evidence from Jordan.” Australasian Accounting, Business and Finance Journal, 1(4), 40–61.
Zhang, Z. (2016). “Variable selection with stepwise and best subset approaches.” Annals of Translational Medicine, 4(7), 1–6.

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Go to Computing in Civil Engineering 2021
Computing in Civil Engineering 2021
Pages: 843 - 850

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Published online: May 24, 2022

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Wonkyoung Seo [email protected]
1Ph.D. Student, Dept. of Architecture and Architectural Engineering, Yonsei Univ., Seoul, Korea. Email: [email protected]
Byungil Kim, Ph.D. [email protected]
2Associate Professor, Dept. of Civil Engineering, Andong National Univ., Andong, Korea. Email: [email protected]
Seongdeok Bang, Ph.D. [email protected]
3AIv Company, Yongin, Gyeonggi, Korea. Email: [email protected]
Youngcheol Kang, Ph.D., M.ASCE [email protected]
4Assistant Professor, Dept. of Architecture and Architectural Engineering, Yonsei Univ., Seoul, Korea. Email: [email protected]

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