CC BY
34DOI 10.47576/2712-7516_2021_3_1_53 УДК 336.62
EVALUATION AND PREDICTION MODELLING ON FINANCIAL AND TAX RISKS OF A MULTINATIONAL COMPANY
Grigorieva Elena Mihailovna,
Сandidate of Economic Sciences, Associate Professor, Deputy Dean for Research,
Associate Professor of the Finance and Credit Department, RUDN University,
Moscow, Russia, e-mail: [email protected]
Lasloom Nasser Mohammed,
postgraduate student of the Finance and Credit Department, RUDN University
Moscow, Russia, e-mail: [email protected]
This study sought to And out the management practices employed by multinational companies in controlling the financial and tax risks they are exposed to. It also assessed the management practices used by multinational companies in the recent financial crisis. This study is relevant since multinational companies located in developing countries operate under different business environments. Hence, multinational companies in developing countries should control financial risk by using management practices conducive for the environment they operate in. Chevron Corporation was used as a case study. Data were obtained from its annual financial statement published on their website covering the period of year 2009-2018. Based on the result of the Step-wise regression analysis we identified the significant predictors in conditions of the economic environment to estimate the corporate prosperity and profitability measure of Chevron Corp.
Keywords: Financial risk; Tax risk of multinational companies; forecasting of financial indicators.
УДК 336.62
ОЦЕНКА И ПРОГНОЗ МОДЕЛИРОВАНИЯ ФИНАНСОВЫХ И НАЛОГОВЫХ РИСКОВ МНОГОНАЦИОНАЛЬНОЙ КОМПАНИИ
Григорьева Елена Михайловна,
кандидат экономических наук, доцент, заместитель декана по научной работе, доцент кафедры финансов и кредита, Российский университет дружбы народов, г. Москва, Россия, e-mail: [email protected]
Насер Мохаммед Ласлум,
аспирант кафедры финансов и кредита, Российский университет дружбы народов, г. Москва, Россия, e-mail: [email protected]
В исследовании была предпринята попытка выявить методы управления, применяемые транснациональными компаниями для контроля финансовых и налоговых рисков, которым они подвергаются. Дана оценка методов управления, использованных транснациональными компаниями во время недавнего финансового кризиса. Это исследование актуально, поскольку транснациональные компании, расположенные в развивающихся странах, работают в разных бизнес-средах. Они должны контролировать финансовые риски, используя методы управления, благоприятные для среды, в которой они работают. В качестве примера использовалась корпорация Chevron. На основе результатов пошагового регрессионного анализа были определены важные предикторы в условиях экономической среды для оценки корпоративного процветания и показателя прибыльности.
Ключевые слова: финансовый риск; налоговый риск транснациональных компаний; прогнозирование финансовых показателей.
Introduction company to manage risk. They are exposed to
Hedging policies are used by multinational different types of risk such as country exchange
rate, and political risks etc. The transformation of a firm to multinational occurs when it begins planning, production, marketing and the financing of its activities on a global basis. Future projections are the basis by which Firms hedge their risks. Hedging policies is srategic for the survival of multinational companies. The aim of Firms in international market is to increase revenue which is achieved by increased production and sales as well as penetration into new markets. Companies use hedging to minimize their investment risk and other risk that are related to the future.
Multinational companies can be classified based on their ownership management and their structural and strategic components with their basic goal to produce goods and services and make profits globally; they enter potential markets to achieve their goal. Products of Multinational corporations are often sell domiciled in the various countries currencies. The breakdown of the Bretton Woods system of fixed exchange led to dramatic increase in exchange rates volatility[39], the vulnerability of multinational corporations to exchange risk is also is also occasioned as short term movements in exchange rates are often not followed by offsetting differences in prices in the corresponding countries see Shapiro [37].
Financial risk thus implies the possibility of losses that corporate organizations may be exposed to especially when it may not have adequate amount of cash to meet its financial obligations. It can also been seen as the uncertainty of returns and the possibility for financial emanating from financial transactions of institutions or organizations. Risk shows there is a possibility that there won't be a return on an investment. It is thus important to consider some events in business transactions which may involves some kind of risks and requires steps to be taken to resolve them. In additional to financial contracts can be used by cooperation to manage risk exposure.
Literature Review
A number of finance scholars and practitioners have discussed how firms could utilize financial instruments to hedge financial price risk see among others [22; 37]. Market imperfections, as seen in taxes, agency problems, asymmetric information, dead-weight costs related to financial distress, may provide leverage for corporations to hedge exchange risk [15].
Financial hedging helps alleviate the agency problem associated with the firm's outstanding
debt and moves equity owners to closer to the first best operating policy. Mello, Parsons, and Triantis analyze a model in which exchange rate movements are the only source of risk. In such a setting, production
Flexibility enhances firm's value but may not confer a hedging benefit [35]. There are good number of studies on Financial risk measurement; its connection with imprecise probabilities has been mostly overlooked. Risk measures can be viewed as instances of upper (or lower) previsions, thus letting us apply the theory of inaccurate provisions to them [46]. A detailed view to risk measurement in financial management is can be referenced [12]. Apart from well known risk initiatives, not excluding value at risk or coherent and turned in risk measures, there are also others techniques that researchers employs to assess financial risks. Su and Furman [41] uses a form of multivariate Pareto distribution to assess financial risks. Spatial financial time series models were also initiated by [8].
Kessler in his study gave an implementation systematic approach strategy for risk, the approach is to have the risk management focus and to manage and mitigate the risk around-loss causes [9]. Attention on the usefulness of innovative soft-computing techniques usage can help to identify correctly the default of a company by adequate financial credit risk projection. Chai and Xia, [10] put call attention to that to survive and develop in a significantly viable market, business entities should be in control of possible financial risks and acknowledge their future financial development (using prediction models).
Following the first prediction model made by (Fitzpatrick), a number of studies have emerged and many predictors have been identified to foretell the future condition of the business entities, e.g., (Beaver model , Altman model, Springate model) , etc. The accuracy of bankruptcy forecast in different industries is of a great interest to business partners either as investors or creditors, as this can lead to reduction of creditors' risk. The methodical analysis of bankruptcy prediction models are expressed [2]. The analysis show that there exist two groups of fashionable and capable tools within the bankruptcy prediction models research area, i.e., statistical tools (multiple discriminant analysis and logistic regression) and artificial intelligence tools as expressed in decision trees, neural networks, etc.
In this study, we test the use of a quite simple classifier, linear regression approach for modeling the relationship between a scalar dependent variable and more explanatory variables (financial indicators) as it performs reasonably well in bankruptcy prediction, as proved by Jones et al. Regression analysis is notably used for bankruptcy forecast, this assertion is supported by the study in [43] or studies done in Romania and Lithuania, which suggest regression models for bankruptcy prediction. In Asia, Europe and America A practical framework of regression was used to make predictive bankruptcy models, the results confirmed the superiority of the global model compared to regional models. Ben Jabeur, [6] claims that regression model shows the opportunity to consider all the indicators in predicting financial distress. Hwang and Chu [23] highlighted a new procedure to estimate the loss that can arise using logistic regression. Li and Miu [24], shows a prediction model with dynamic loading on accounting ratio-based and market-based information using a regression approach.
Few researchers have, however, considered the relevance of taxation as part of the change in the Risk-management ideology. Tax remains a 'resultant' determined 'simply' by applying the applicable tax rate to the taxable incomes determined in accordance with the respective jurisdiction's tax rules (International Accounting Standards Board (IASB), 2012). In the context of hectic financial markets Witherell [24], swift alterations in domestic and international tax laws and an enlarged proclivity for tax authorities to engage in aggressive tax administration and collection policies [44] this viewpoint is no longer suitable [17]. Tax risk-management (TRM) can have substantial propositions for the capacity of organizations to generate commonly responsible and sustainable returns in the short, medium and long term, making it a highly pertinent part of the corporate governance paradigm [27].
Comprehensive system of risk identification and management are now in prominence as mechanism that practically address tax-related risks (consider Institute Of Directors (IOD), 2009; Integrated Reporting Committee of South Africa (IRC), 2011; Loots, 2012).Thus the need to take consider the company's overall commercial strategy, the potential impact on stakeholders and the need for improved simplicity and accountability (Organisation for Economic Cooperation and Development (OECD), 2006). Prior
corporate governance research has, however, largely ignored the importance of effective TRM [13].
Materials and Methodology The objective of this study is to examine the formation of the management strategies of financial and tax risks for multinational companies. Chevron Corporation is an American multinational energy company of standard oil it is active in more than 180 countries. Fourteen (14) financial and accounting variables were utilized as components. Eleven years data from 2009 to were collected from the Chevron Corporation online report and presentation data bank, and www.annualreports.com. A stepwise regression model will be used as a statistical tool to formulate a model to determine the risk involve and on how to manage it. The method of multiple linear regression models was used to create the model. Multiple linear regression models have the dependent variable as a linear combination of independent variables and an intercept: yi = f?0+T,f=ífiJxlj+eí (1)
Where:
y¡ = dependent variable x ■ -
lJ = independent variable(s) fiofij = unknown parameter of the model £3 = error term
Parameters Pj are considered as unknown numerical constant, pQ is an absolute number. The parameter fij explains the variations in the value of the dependent variable.^, if the j-th independent variable changes of one unit, provided that the value of other independent variables stays unchanged.
Testing the significance of the model create. Write the equation of the regression model. The descriptive statistics (Mean, variance and standard deviation) are summarized in table 1 below.
Table 1 - Descriptive Statistics (Source: Author)
Variables Mean Std. Deviation
GDP Per Capital 4.73834 .04б001
Inflation Rate 1.573 1.0041
Total Liability 4.9817б .0б25б3
Total Equity 5.133бб .075759
Total Debt 4.41552 .345394
Interest Expense 2.14491 .4920б2
EBIT 4.21545 .434704
se
Total operating cost 5.19843 .079607
Income Tax Expense 3.55320 .889906
Total Asset 5.36712 .068677
Net income 3.97463 .524472
Total expenditure 4.42814 .126614
Total revenue 5.25076 .113819
Sales 5.25996 .136672
Furthermore we test the hypothesis of mutual dependence between the individual independent variables and the dependent variables on the significance leave of 0.05, which is compared to the p-value of the test of significance of Pearson correlation coefficient. Even as there exist a mutual dependency between the independent variables and the dependent variable the p-value statistics still shows that some of the independent variables are not statistically significant to explain the dependent variable i.e. there is no significant relationship between them hence they were not included in the model. We decided to include these variables in the modeling corporate prosperity estimation considering the independent variables Income tax expense, Net income, Sales, Total debt, Total operating cost, Total revenue and GDP Per capital having their p-value lower than the level of significance (0.05) hence the null hypothesis was rejected and we conclude that there is a dependence between the individual independent variables and the dependent variable.
The significance should be below the significance level P-value <0.05 to consider all predictors significant for the model. All independent variables are below the determined significance level hence we concluded that Total revenue, Net income and Income Tax expense are all statistically significance to predict the dependent variable (EBIT). The model diagnostic check for multi-collinearity was display in Table 2 below.
Table 2. Collinearity measured by VIF (Dependent Variable: EBIT, Source: Author)
Model
Collinearity Statistics
Tolerance
VIF
(Constant)
Total revenue 3 M Net income .182 7.262
.143 6.984
Income Tax Expense .286 3.497
The variance inflation factor (VIF), calculated in Table 2 above, measures the impact of collinearity among the variables in a regression model. It is always greater than or equal to one. There is of formal VIF value for determining presence of multi-collinearity; however, values that exceed 10 are often regarded as indicating multi-collinearity. Based on the result in the model it can be concluded that there is no multi-collinearity symptom, as all values are between 1 and 10.
Furthermore the model significance was using the F-Test (Anova) presented in the Table 3 below.
Table 3 - ANOVA (Dependent Variable: EBIT, Source: Author)
Model Sum of Squares df Mean Square F Sig.
Regression 1.869 3 .623 206.061 0.000
3 Residual .021 7 .003
Total 1.890 10
The result value of the calculated F statistics is again compared to the significance level 0.05, and as it is below the determined level, we can conclude that the mode is statistical significant.
The final notation of the model of the prosperity quantification, based on the corporate profitability, in condition of the chevron financial data is:
Corporate prosperity (EBIT) — - 2.312 + 0.749,otn, _ + 0. 542ff„
+ 0-124,„
(2)
Summary of Findings
Findings from the study show that there is linear relationship between the two macro-economic indicators and the corporate prosperity & profitability of Chevron Corp. The GDP Per capital exhibit a medium and negative linearity with the prosperity & profitability of the Chevron Corp with a correlation coefficients of -0.548 and thus implies that an increase in the corporate prosperity & profitability of Chevron will results to a decrease in the GDP Per capital of United State of America and vice versa. A medium but a positive relationship exists between the profitability ratio and inflation rate of United State of America with a correlation coefficient of 0.402 causing an increase in the inflation rate of the United State of America will also increase the prosperity & profitability measure of Chevron Corp and vice versa.
The multi-national industry model of the corporate prosperity and profitability shows that
the value of the intercept (Constant) Is the limit value, which means that if all financial ratios are zero and the company has the value of the corporate prosperity equal or less than the constant value (-2.312), the future prosperity of the company is bad, it is consider to be non-prosperous. In that case, the company has to consider their efficiency and profitability in the future or take measures to eliminate or prevent the financial risks. If the value of the corporate prosperity is higher than the constant value the company is considered profitable in the future. The value 0.749. t. implies that a unit
total revenue 1
measured increase/decrease in Total revenue the corporate prosperity will increase/decrease by 0.749$. The value 0.542 t. means if
J net income
the value of Net income increase/decrease by one measured unit, the corporate prosperity increase/decrease of 0.542$ and lastly the value 0.124[Mome tas expeil£e means if the value of Income tax expense increase/decrease by one measured unit, the corporate prosperity increase/ decrease of 0.124$.
Conclusion
The main aim of the paper was to strategize the management of financial and tax risks related to the unhealthy financial situation of a multi-national company. The results gained in the model are not just extra important for companies themselves but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the unfavorable financial scenario. Based on the results of the multiple linear regression analysis we can identify the financial predictors, which play a crucial role in the process of the prosperity quantification measured by EBIT and financial risks identification.
The financial prediction modeling helps to predict the financial distress of companies. The importance of the area is underline by the fact that the information about the future profitability eliminates potential financial and tax risks and enables to evaluate the financial health of the company base on some selected external and internal factors. Realizing the multiple regression analysis we identify the statistically significant determinants that affect the future financial development of the company and thus we form a regression model to estimate the financial risk, the corporate prosperity and profitability. As the statistically significant predictors were determined five financial indicators which includes Total revenue, Total debt, Net income, Sales and Total operating cost and in other to examine some external factors that might affect the model adequacy, two micro-economic indicators which includes GDP per capital and Inflation rate of the United State of American were also gotten from their database online.
These factors are significant enough to manage the financial risks and to affect the profitability and prosperity of the company and can be later used in the model to predict the default of Chevron Corporation. The multi-national model of the financial and tax risk management perceived the value of the intercept as the limit value, which means that if the company has the value of the corporate prosperity equal or less than intercept value, there is thread of financial problems in the future. Moreover, the multi-national company considers their cooperation with their strategic plan in the future or take measures to eliminate or prevent the financial risks involve.
Reference _
1. Alaminos, D.; del Castillo, A.; Angel, F.M. (2006). A global model for bankruptcy prediction. Plus one 2016, 11.
2. Alaka, H.A.; Oyedele, L.O.; Owolabi, H.A.; Alaka, H.A.; Oyedele, L.O. Owolabi, H.A.; Kumar, V.; Ajayi, S.O.;Akinade, O.O.; Bilal, M.(2008). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Syst. Appl. 2018, 94, 164-184
3. Altman, E.I. (1968). Financial Ratios. Discriminant Analysis and Prediction of Corporate Bankruptcy. J. Financ. 1968, 23, 589-609.
4. Audrino, F.; Barone-Adesi, G. (2005). Functional gradient descent for financial time series with an application to the measurement of market risk. J. Bank. Financ. 2005,29, 959-977.
5. Bali, T.G. (2007). A generalized extreme value approach to financial risk management. J. Money Credit Bank. 2007, 39, 1613-1649.
6. Ben Javeur, S.(2017) Bankruptcy prediction using Partial Least Squares Logistic Regression. J. Retail. Consume. Serv. 2017,36, 197-202
7. Beaver, W.H.(1966). Financial Ratios as Predictors of Failure. Empirical Research in Accounting: Selected Studies. J. Account. Res. 1966, 4, 71-111.
8. Blasques, F.; Koopman, S.J.; Lucas, A.; Schaumburg, J. (2016). Spillover dynamics for systemic risk measurement using spatial financial time series models. J. Econom. 2016,195, 211-223.
9. Campos, R.; Riuz, F.J.; Agell, N.; Angulo, C.(2004). Financial credit risk measurement prediction using innovative soft-computing techniques. In Computational Finance and its Applications; Costantino, M., Brebbia, C.A., Eds. WIT Press: Southampton, UK, 2004; Volume 38, pp. 57-66.
10. Chai, Y.; Xia, E.(2008). Financial risk measurement and prevention. In Proceedings of the International Conference on Risk and Reliability Management, Beijing, China, 10-12 November 2008; Li, J.L., Ed.; Universe Academic Press: Toronto, ON, Canada, 2008.
11. Chobot, M. (1993). Approaches to risk measurement in strategic and financial management 1.Ekon.Cas.1993, 41,751-758.
12. Chobot, M. (1994). Approaches to risk measurement in strategic and financial management 2. Ekon.Cas:1994, 42,40-54.
13. Demidenko, E. & McNutt, P. (2010). The ethics of enterprise risk management as a key component of corporate governance. International Journal of Social Economics, 37(10), pp. 802-815.
14. DeMarzo, Peter M. and Duffie, Darrell (1995). Corporate incentives for hedging and hedge accounting, Review of Financial Studies 8, 743-771.
15. Dufey, Gunter and Srinivasulu, S. N. (1983) The case for corporate management of foreign exchange risk, Financial Management 54-62.
16. Drakos, A.; Kouretas, G.P. (2015).Bank ownership, financial segments and The measurement of systematic risk: An application of CoVaR.Int. Rev. Econ. Finance. 2015,40, 127-140.
17. Elgood, T., Paroissien, I. & Quimby, L. (2004). Tax risk management guide. PWC Publication. [Online] Available: http://www.pwc.co.za/en_ZA/za/assets/pdf/pwc-tax-risk-management-guide.pdf. (Accessed 7 August 2013).
18. Eiteman, David K., Stonehill, Arthur I., and Moffet, Michael H. (1995) Multinational Business Finance, Seventh Edition, Addison Wesley Publishing Company.
19. Fitzpatrik, P.J. A (1931). Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies. Certif. Public Account. 1931, 598-605.
20. Froot, Kenneth A., Scharstein, David S., and Stein, Jeremy C. (1993) Risk management: coordinating corporate investment and financing policies, Journal of Finance 48, 1629-1658.
21. Guo, G.; You, W.; Qian, G.; Shao, W.(2015). Parallel maximum likelihood estimator for multiple linear regression model. J. Comput. Appl. Math. 2015,273, 251-263.
22. Giddy, Ian H. (1983) The foreign exchange option as a hedging tool, Midland Corporate Finance Journal (3), 32-42.
23. Hwang, R.C.; Chu, C.K.(2018). A logistic regression point of view toward loss given default distribution estimation. Quant. Finance. 2018,18, 419-435.
24. Kessler, A.M. (2007). A systematic approach to operational risk measurement in financial institutions. J. Oper. Risk 2007, 2, 27-68.
25. Krusinskas, R.; Lakstutiene, A.; Stankeviciene, J. (2014). The research of reliability of bankruptcy prediction models in Lithuanian companies. Transform. Bus. Econ. 2014,13, 102-123.
26. Peres, C.; Antao, M. (2016). The use of multivariate discriminant analysis to predict corporate bankruptcy: A review. AESTIMATIO IEB Int. J. Financ. 2016, 14, 108-131.
27. Leitch, M. (2003). Risk management history and regulations (UK). Managed Luck website. [Online] Available: http://www.managedluck.co.uk/regulations/index.shtml. (Accessed 28 August 2013).
28. Lewent, Judy C. and Kearney, A. John (1990) Identifying, measuring and hedging currency risk at Merck, Journal of Applied Corporate Finance (4), 19-28.
29. International Accounting Standards Board (IASB). (2012). International Accounting Standard 12: Current and Deferred Tax (IAS 12). SAICA Handbook. South Africa: Lexis Nexis.
30. Integrated Reporting Committee of South Africa (IRC). (2011). Framework for Integrated Reporting and the Integrated Report. [Online] Available: http://www.sustainabilitysa.org/IntegratedReporting/ ReferenceSourcesonIntegra tedReporting.aspx (Accessed 2 March 2013).
31. Institute of Directors (IOD). (2009). King Report on Corporate Governance for South Africa 2009. South Africa: Lexis Nexis.
32. Jones, S.; Johnstone, D.; Wilson, R. Predicting corporate Bankruptcy: An evaluation of Alternative Statistical Frameworks. J. Bus. Financ. Account.2017,44, 3-34.
33. Lee, S.; Choi, W.S.(2013). A multi-industry bankruptcy prediction model using back-propagation neutral network and multivariate discriminant analysis. Expert Syst. Appl. 2013, 40, 2941-2946.
34. Li, M.Y.L.; Miu, P.(2010). A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach. J. Empir. Finance. 2010,17, 818-833.
35. Mello, Antonio S., Parsons, John E., and Triantis, Alexander (1995).An integrated model of multinational flexibility and financial hedging, International Economic Review 39,27-51
36. Springate, G.L.V. (1978). Predicting the Possibiity of Failure in A Canadian Firm; Unpublished M.B.A. Research Project; Simon Fraser University: Burnaby, BC, Canada, 1978.
37. Shapiro, Alan C. (1992) Multinational Financial Management, Fourth Edition, Allyn and Bacon.
38. Shapiro, Alan C. and Titman, Sheridan (1985) An integrated approach to corporate risk management, Midland Corporate Finance Journal 3 (2), 41-56.
39. Smith, Clifford W., Jr., Smithson, Charles W., and Wilford, D. Sykes (1990) Managing Financial Risk, Harper Collins Publishers.
40. Smith, Clifford W., Jr. and Stulz, René: (1985), the determinants of firms' hedging policies, Journal of Financial and Quantitative Analysis 20, 391-403.
41. Su, J.; Furman, E. A. (2017). Form of multivariate Pareto distribution with applications to financial risk measurement. Astin Bull. 2017, 47, 331-357.
42. Stulz, René (1994) Optimal hedging policies, Journal of Financial and Quantitative Analysis 19, 127-140.
43. Tudorache, I.C. (2017). Bankruptcy prediction models for Romanian restaurants. In Proceedings of the 16th International Conference on Informatics in Economy: Education, Research and Business Technologies, Bucharest, Romania, 4-7 May 2017; Boja, C., Doinea, P., Pocatilu, P., Eds.; Bucharest Univ Economic Studies:Bucharest, Romania, 2017; pp. 519-525.
44. Weinberger, M., Nolan, D. & Thomas, R. (2012). 2011-12 Tax risk and controversy survey-Ernst & Young - Global, 4th edition. United Kingdom: Ernst & Young.
45. Witherell, B. (2012). Real Clear Markets - A Tumultuous Year for the Financial Markets. Real Clear Markets.
46. Vicig, P.(2008). Financial risk measurement with imprecise probabilities. Int. J. Approx. Reason.2008, 49, 159-174.
47. Yang, X.; Chatpatanasiri, R.; Sattayatham, P. Dynamic Risk Measurement of Financial Time Series with Heavy-Tailed: A new hybrid Approach. Thai J. Math. 2016, 14, 265-281.
48. http://www1.realclearmarkets.com/2012/01/05/a_tumultuous_year_for_the_financial_markets_122063.html. (Accessed 31 July 2013).
49. Organization for economic co-operation and development (OECD). (2006).
50. Good Corporate Governance: The Tax Dimension. [Online] Available: http://www.oecd.org/site/ctpfta/37207911. pdf. (Acce. 2013).
