Instructions For this assignment, you will write an essay in which you compare and contrast the following strategic measurement tools used by business and human resource professionals as presented in

Instructions For this assignment, you will write an essay in which you compare and contrast the following strategic measurement tools used by business and human resource professionals as presented in
International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 57 IS ECONOMIC VALUE ADDED SUPERIOR TO EARNINGS AND CASH FLOWS IN EXPLAINING MARKET VALUE ADDED? AN EMPIRICAL STUDY Ahmad N. Obaidat Tafila Technical University ABSTRACT This study investigates if Economic Value Added (EVA) is superior to Net Operating Profit after Tax (NOPAT) and Net Cash Flow (NCF) in explaining the change in the Market Value Added (MVA) of the non-financial firms listed on Amman Stock Exchange (ASE) for the year 2016. The results indicated that NCF has the strongest power in explaining the change in MVA, followed by EVA. The results also indicated that the NOPAT does not add any additional significant explanatory power to NCF and EVA in explaining the change in MVA. Finally, this study recommends the use of EVA as an enhancement tool to the existing traditional accounting performance measures, not as a substitute to them. Keywords: Economic value added, earnings, cash flow, market value added INTRODUCTION Due to the changes in the business environment, business community is looking for more powerful performance measures that overcome the problems associated with the traditional accounting measures, in terms of emphasizing shareholders value maximization, which has become the obsession for managers and capital providers. The traditional accounting measures have been criticized because they failed to represent faithfully: the factors that drive shareholder value (Chari, 2009), profitability (Al-Mamun & Abu-Mansor, 2012), and the financial situation of the firms (Bluszcz, Kijewska & Sojda, 2015). In recent years, firms have been focusing on creating value for their shareholders (Sirbu, 2012), and the value maximization has become a well accepted objective among them (Bhasin, 2013), so the attention should be paid to the elements of shareholders value creation (Ganea, 2015). In this regard, new performance measures have been introduced (Abdeen & Haight, 2002; Chari, 2009), such as Economic Value Added (EVA), Market Value Added (MVA), Cash Flow Return on Investment (CFROI), Total Shareholder Return (TSR), Shareholder Value Added (SVA), and Value Added Management (VAM). EVA, which is considered one of the most popular value indicator measure, is a trade mark developed in the early 1990s by Stern Stewart & Co., a New York based consulting firm that claimed that EVA is superior to traditional accounting measures in measuring shareholder value. The idea behind EVA is that the firm adds value to shareholders if it earns more than the cost of capital employed, where the cost of capital includes the total costs of borrowed capital and equity capital. In other words, EVA is the profit earned by the firm minus the cost of capital. If EVA is positive, the firm is creating value for its shareholders, and if it is negative, the firm is destroying value. So, EVA differs in that it considers the cost of all capital employed, and not just the cost of borrowed capital as the traditional accounting measures do. Many researchers considered EVA helpful in: indicating how successful a firm is in creating value for shareholders (Epstein & Young, 1998), making financial decision, (Goldberg, 58 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 1999), analyzing capital budget and securities (Kramer & Peters, 2001), valuing investments (Sparling & Turve, 2003), enhancing business environment (Tsuji, 2006), measuring performance (Sharma & Kumar, 2012), identifying investment opportunities (Bhasin, 2013) and determining remuneration policy (Ganea, 2015). Some other researchers doubted that EVA is a new discovery. Kyriazis and Anastassis (2007), Bhasin (2013), and Nagarajan (2015) argued that a similar concept had been contemplated by economists such as Alfred Marshall for many years before that, particularly in 1890, who spoke about the economic profit, in terms of the real profit that firms make when it covers the cost of its invested capital. Also the Finnish academics and financial press discussed this concept in the early 1970s. In the same context, Goldberg (1999), Keys, Azamhuzjaev & Mackey (2001), De Wet (2005), and Nagarajan (2015) asserted that EVA is a relatively recent variant of residual income, an older financial measure that did not get wide publicity and was abandoned by firms years ago, whereas the difference between EVA and residual income lies in the various adjustments in the financial statements suggested by Stern Stewart & Co. These adjustments aimed to remove the distortions in the accounting profit caused by accounting rules (Machuga, Pfeiffer, & Verma, 2002), and because Stern Stewart & Co. suggested more than 160 adjustments, many researchers criticized EVA. Goldberg (1999) argued that the cost of EVA may exceed its benefits. Keys, Azamhuzjaev & Mackey (2001) argued that EVA calculation is very complex and too difficult to understand, especially the calculation of the cost of capital. Bhasin (2013) argued that the complex calculation of EVA may lead to calculation errors that lead to misleading results. Regardless of these criticisms for EVA, Large well known firms including Coca-Cola, Polaroid, Sprint Corporation, AT&T, CSX, DuPont, Eli Lilly, Quaker Oats, Briggs & Stratton, and Toys ‘R Us have utilized EVA in investment decisions, capital reallocation, and the performance evaluation (Kramer & Pushner, 1997; Tortella & Brusco, 2003; Al-Mamun & Abu- Mansor, 2012). Recently, Stancu et al. (2017) have asserted that “EVA is the most widely used indicator by firms and financial advisors to measure the company’s performance”. While EVA is considered an internal single-period measure of firm performance, MVA is a more forward looking measure (Kramer & Pushner, 1997). MVA is considered a cumulative measure of the value created by the firm (Kramer & Peters, 2001), which is the difference between the market value and the book value of capital (Sparling & Turve, 2003), and from investors’ point of view, MVA is the best external measure of a firm’s performance (De Wet, 2005). There is a general belief that, in order to maximize shareholders value, firms should maximize MVA, and the best way to do so is by maximizing EVA (De Wet & Hall, 2004) because MVA is the sum of all future EVAs (Poornima, Narayan, & Reddy, 2015). In this regard, Khan, Aleemi, and Qureshi (2016) argued that the debate is still on about the superiorit y of EVA over the traditional accounting measures. Whereas the previous empirical studies revealed controversial results regarding which is superior in explaining MVA; is it EVA or the traditional accounting measures? (Altaf, 2016). This study investigates if Economic Value Added (EVA) is superior to Net Operating Profit after Tax (NOPAT) and Net Cash Flow (NCF) in explaining the change in the Market Value Added (MVA) of the non-financial firms listed on Amman Stock Exchange (ASE) for the year 2016. The results indicated that NCF has the strongest power in explaining the change in MVA, followed by EVA. The results also indicated that the NOPAT does not add additional significant explanatory power to NCF and EVA in explaining the change in MVA. International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 59 LITERATURE REVIEW After the introduction of EVA as a performance measure used in assessing firm’s ability to create value for shareholders, studies have revealed controversial results regarding the superiority of EVA over the traditional accounting measures. The most related previous studies supporting the superiority of EVA are presented next, followed by the most related previous studies that do not support the superiority of EVA. Machuga, Pfeiffer, and Verma (2002) examined the effectiveness of EVA and EPS in predicting future earnings. The results indicate that EVA contains more incremental information than EPS in predicting future earnings. Prakash et al. (2003) investigated the impact of adoption of EVA on the key financial ratios as a proxy of firm’s performance. The results indicated that most of the financial ratios were significantly improved after the adoption of EVA. Worthington and West (2004) examined whether EVA is more associated with stock returns than traditional accounting measures such as earnings and net cash flow for 110 Australian companies over the period 1992 – 1998. Results indicated that EVA is more closely related to stock returns than traditional accounting measures. Kim, Jae-Hyeon, and Yun (2004) investigated the significance of EVA, after controlling firm stage (contraction period vs. expansion period) and sector (manufacturing vs. non- manufacturing). In addition, they investigated if stock prices follow EVA or the traditional accounting measure such as EPS. The results indicated a positive correlation between EVA and MVA during the contraction period and indicated that the market response to EVA and traditional accounting measure differed between manufacturing and nonmanufacturing sectors. Sharma and Kumar (2012) examined if EVA can be used as a tool of performance measures and if it is considered better than the traditional accounting measures in Indian market. The results indicated that investors should use EVA along with the traditional accounting measures for decision making. Al-Sheikh (2012) measured the relationship between EVA and the market value of stock prices. The results indicated that there is a relationship between the EVA and the market price per share and this relationship is stronger than the traditional accounting measures. Abu-Wadi and Saqfalhait (2016) analyzed the effect of traditional accounting measures represented by the rate of return on equity and EVA on MVA for Jordanian commercial banks. The results indicated that the both indicators have a significant positive effect on MVA with superiority for EVA. Contrary to the previous studies; Kramer and Pushner (1997) tested the relationship between EVA and MVA. The results did not support the arguments that EVA is the best measure of corporate success in adding value to shareholder. Turve and Lake (2000) examined the relationship between EVA and the stock market performance in the Canadian food processing sector. The results provided little support that high-EVA firms lead to high shareholders value. Kramer and Peters (2001) tested if EVA could be considered as a proxy for MVA across 53 industries. The results indicated that there is no marginal benefit from using EVA as a proxy for MVA instead of the traditional accounting measure like net operating profit after tax. Sparling and Turve (2003) assessed the strength of the relationship between EVA as a tool for valuing investments and shareholder return. The results indicated a weak correlation between them. De Wet (2005) analyzed the relationship between some traditional accounting measures (e.g., cash flow from operations and earning per share) and EVA with MVA as a proxy for shareholder value in South Africa market. The results did not indicate the superiority of 60 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 EVA, and indicated that the stronger relationships exist between MVA and cash flow from operations. Kyriazis and Anastassis (2007) investigated the explanatory power of EVA and traditional accounting measures with respect to stock returns and firms’ market va lue in Athens Stock Exchange. The results indicated that EVA does not have a stronger correlation with firms’ MVA than the traditional accounting measures. Visaltanachoti, Luo, and Yi Yi (2008) compared EVA with traditional accounting measures (e.g., cash flow from operations, earnings before income tax, and residual income) in terms of its relationship with sector returns. The results indicated that the association between sector returns and the traditional accounting measures is higher than that with EVA. Bhasin (2013) analyzed the effectiveness of EVA over the traditional accounting measures. The results indicated that there is no strong evidence to support the superiority of EVA over the traditional accounting measures in its association with MVA. Khan, Aleemi and Qureshi (2016) examined if EVA have superiority over the traditional accounting measures (e.g., return on equity, return on assets, operating cash flows and earning per share) in explaining stock price of non-financial firms listed on Karachi Stock Exchange for the period 2009-2012. The results indicated that the traditional accounting measures outperformed EVA in explaining the behavior of stock prices. Altaf (2016) tested if EVA is a better indicator than traditional accounting measures in explaining MVA for manufacturing and service firms in India. The results indicated that the operating income has the strongest relationship with MVA in both sectors, while EVA showed a weak positive relationship with MVA. HYPOTHESES The aim of this study is to investigate if Economic Value Added (EVA) is superior to Net Operating Profit after Tax (NOPAT) and Net Cash Flows (NCF) in explaining the change in Market Value Added (MVA) of the non-financial firms listed on Amman Stock Exchange (ASE) for the year 2016. Based on the stated aim, the following null hypotheses were developed and tested: H 1: Net Operating Profit after Tax (NOPAT) is superior to Economic Value Added (EVA) in explaining the change in Market Value Added (MVA). H 2: Net Cash Flow (NCF) is superior to Economic Value Added (EVA) in explaining the change in Market Value Added (MVA). CALCULATION OF EVA, WACC AND MVA EVA CALCULATION Abdeen & Haight (2002) stated that “In its simplest terms, EVA measures how much economic value in dollars the company is creating, taking into account the cost of debt and equity capital”. In other words, EVA represents the return that the firm must generate to satisfy the capital providers (Chari, 2009). (1) Capital of CostTax Afterofit Operating Net EVA   Pr International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 61 As mentioned previously, to calculate EVA, Stern Stewart & Co. suggested more than 160 adjustments to eliminate distortions in firm NOPAT and capital in an attempt to refine the accounting income to be closer to economic income. The most common adjustments include (Abate, Grant, & Stewart, 2004; Alihodžić, 2013): capitalized R&D, goodwill amortization, LIFO reserve, operating lease, pension expense, provisions for doubtful receivables, one-off cost of restructuring. Sirbu (2012) argued that only five to seven key adjustments are made in practice. Nagarajan (2015) justified this, when he concluded that EVA calculation will be more complicated if all of these adjustments are made. Accordingly, the final version of EVA equation could be represented as follows: (2) Where: : Adjusted net operating profit after tax, : Adjusted invested capital, : Weighted average cost of capital. The firm is creating value (wealth) for its shareholders if EVA is positive, and is destroying value if EVA is negative. WACC CALCULATION The WACC represents an average rate of return that a firm must pay to its shareholders and creditors (Alihodžić, 2013), and is calculated by calculating the cost of each source of capital, and then the weights of each source are assigned on the basis of proportion of each source to the total capital employed. The weights can be assigned on book value basis or market value basis, but Stern Stewart & Co. recommended the market value basis (Madhavi & Prasad, 2015). (3) Where: : Cost of equity capital, which is calculated according to Dividends Capitalization Model, whi ch is equal to: (dividends per share for the next year ÷ current market share price) + dividends growth rate, : Cost of debt capital (weighted average cost of borrowed capital), : Market value of equity capital, : Market value of debt capital, : Tax rate. Although Stern Stewart & Co. recommended the use of market value basis to calculate the WACC, the book value basis was used in this study because the data of market value of debt is not available in ASE. ) ( 1WACC IC NOPAT EVA it adj it adj it it    NOPAT adj it IC adj it 1  WACC it   T MM M C MM M CWACC D E D D D E E E it      1 C E C D M E M D T 62 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 MVA CALCULATION The MVA is a measure of the wealth a firm has created for its shareholders. It is the difference between the total market value of the firm and the total capital invested in the firm (Kim, Jae-Hyeon, & Yun, 2004). The total market value of the firm is the sum of the market value of its equity and the market value of its debt (De Wet, 2005). (4) Where: : Firm market value, : Invested capital. In the absence of information about the market value debt capital, MVA could be calculated from the perspective of common shareholders, and it equals the excess of the market value of equity capital over the book value of equity capital (Thenmozhi, 2000; Abu-Wadi & Saqfalhait, 2016). (5) Where: : Market value of equity capital, : Book value of equity capital, As with EVA, the firm is creating value for its shareholders if MVA is positive, and is destroying value if MVA is negative. RESEARCH METHODS Sample and data Collection The study initial sample consisted of (97) firms, representing all the non-financial firms listed on ASE for the year 2016, (26) firms with insufficient information to compute the study variables were excluded, leaving (71) firms that were examined, representing approximately 73% of the population. Data were collected from firms’ annual reports and ASE online databas e. For each sample firm, the data of net operating profit before tax (NOPBT), net cash flow (NCF), equity capital, borrowed capital, interest rates and the information required to make Stern Stewart & Co. suggested adjustments were obtained from firms’ ann ual reports. The data of firm market value, stock market price, dividends, and dividends growth percentages were obtained from ASE online database. IC FMVMVA it it it   FMV it IC it BM MVA EE it  M E B E International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 63 Measurement of Variables Net operating profit after tax (NOPAT), net cash flow (NCF), and economic value added (EVA) represent the independent variables in this study. NOPAT is calculated as the net operating profit before tax (after adjustments suggested by Stern Stewart & Co.) minus the applicable income tax, taking into account that the tax rates levied on the non-financial firms listed on ASE range between (14%-24%), depending on the nature of industry or service. NCF is obtained directly from the cash flow statement. EVA is calculated using equation (2) above. Taking into account the most common adjustments suggested by Stern Stewart & Co., the only adjustments that were made on NOPAT and invested capital were: Goodwill amortization, capitalized R&D, one-time restructuring charges, operating lease, and pension expense. The other common adjustment could not be performed due to the insufficient disclosures in firms’ financial reports. The change in Market Value Added ( ΔMVA) represents the dependent variable in this study and is calculated as the difference between the MVA at the end and beginning of th e period. Where the MVA is calculated using equation (5) above. According to Kramer and Pushner (1997) and De Wet (2005), the MVA is calculated at a specific moment and in order to assess whether the value has been created or destroyed, the change in MVA from one date to another should be used. RESULTS AND DISCUSSION Table 1 presents summary statistics of NOPAT, NCF, EVA, MVA at the beginning and at the end of the study period, and the change in MVA during the study period for the sample firms. As shown, NOPAT and EVA are positive on average, while NCF is negative. EVA shows a lower average than NOPAT, indicating the influence of the cost of capital on operating profit. Although NOPAT and EVA show a positive average, the change in MVA shows a negative average. This provides an initial indication that the NCF, which has a negative average, may have the highest effect on the change in MVA. Table 1 Summary Statistics * NOPAT NCF EVA Δ MVA Mean 3,908 (3,320) 453 42,321 32,601 (9,720) Max. 49,839 82,117 41,852 1,207,730 1,040,151 424,068 Min. (61,483) (105,290) (65,712) (559,148) (763,097) (540,773) S.D. 12,024 20,998 11,081 196,177 196,061 88,168 * Numbers in Thousand US Dollars Table 2 presents Pearson correlation matrix among the study variable. As shown, the correlation coefficients indicate the non-existence of multicollinearity. MVA is significantly positively correlated with all the independent variables, where the NCF shows the strongest correlation followed by EVA and then by NOPAT. The results also indicated a positive significant correlation between EVA and NOPAT, while there is no significant correlation between NCF and NOPAT. MVA t1  MVA t 64 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 Table 2 Pearson Correlations Matrix Variable NOPAT NCF EVA Δ MVA NOPAT 1.000 NCF 0.074 1.000 EVA 0.792 ** 0.271 * 1.000 Δ MVA 0.250 * 0.633 ** 0.462 ** 1.000 * Significant at p < 0.05 level ** Significant at p < 0.01 level To test the study hypotheses a univariate and multivariate regression analysis were performed. The univariate regression aimed to find out the superior independent variable that explains the change in MVA, while the multivariate regression aimed to find out the best set of independent variables that explain the change in MVA. The regression models that were performed and tested are: (6) (7) (8) (9) Table 3 Univariate Linear Regression Results Unstandardized Coefficients R Square Adjusted R Square ANOVA B Std. Error Beta t Sig. F Sig. Panel A: Model (6) Constant * (16,886) 10,737 – 1.573 .120 .063 .049 4.602 .035 NOPAT 1.834 .855 .250 2.145 .035 Panel B: Model (7) Constant * (899) 8,264 – .109 .914 .400 .392 46.063 .000 NCF 2.657 .391 .633 6.787 .000 Panel C: Model (8) Constant * (11,383) 9,356 – 1.217 .228 .213 .202 18.710 .000 EVA 3.675 .850 .462 4.325 .000 * Unstandardized Coefficients values for constant are in Thousand US Dollars. Panels A, B, and C of Table 3 present the results of univariate regression of NOPAT, NCF, and EVA (respectively) on the change of MVA. As shown, all of these independent variables are statistically significant in explaining the change in MVA (p<.05), but they differ in their explanatory power. The adjusted R square results indicated that NCF has the strongest explanatory power, as it explains (39.2%) of the variance in ΔMVA, followed by EVA with (20.2%) explanatory power, and finally by NOPAT with (4.9%) explanatory power. Also, the Beta results confirm that NCF have the strongest contribution in explaining the dependent ii i NOPAT MVA   10 ii i NCF MVA 10 ii i EVA MVA 10 ii i i i EVA NCF NOPAT MVA   3 2 10 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 65 variable outcomes, followed by EVA then by NOPAT. These results provide additional support to the initially revealed results concerning the strong power of NCF in explaining the change in MVA. The results also show that the regression coefficients for all the independent variables are positive, confirming the Person correlation results regarding the positive relationship between MVA and the independent variables. Table 4 presents the results of Stepwise Multiple Regression. The method of Stepwise was used to find out the best combination of independent variables that best explain the change in the dependent variable, and to eliminate the independent variables that do not have significant contribution in the explanatory power. As shown, after performing this regression, the independent variable of NOPAT was dropped from the regression model, indicating that NOPAT does not add additional significant explanatory power to the other independent variables. The resulting model that includes the variables of NCF and EVA is significant (p<.05) and have explanatory power of (47.6%). Table 4 Stepwise Multiple Regression Results Unstandardized Coeffic ients R Square Adjusted R Square ANOVA B Std. Error Beta t Sig. F Sig. Model (9)** Constant * (899) 8,264 – .109 .914 .400 .392 46.063 .000 NCF 2.657 .391 .633 6.787 .000 Model (9)*** Constant * (3,212) 7,697 – .417 .678 .491 .476 32.824 .000 NCF 2.300 .377 .548 6.094 .000 EVA 2.492 .715 .313 3.485 .001 * Unstandardized Coefficients values for constant are in Thousand US Dollars. ** Predictors: Constant, NCF. *** Predictors: Constant, NCF, EVA. The previous findings provide support for rejecting the first null hypothesis which proposed that “Net Operating Profit after Tax (NOPAT) is superior to Economic Value Added (EVA) in explaining the change in Market Value Added (MVA)”, and accepting the s econd null hypothesis which proposed that “Net Cash Flow (NCF) is superior to Economic Value Added (EVA) in explaining the change in Market Value Added (MVA)”. On the one hand, the result concerning the superiority of EVA over NOPAT in explaining MVA is consistent with the results of the previous studies of (Kramer & Pushner, 1997; Kim, Jae-Hyeon & Yun, 2004; Abu-Wadi & Saqfalhait, 2016) that revealed that EVA is superior to the traditional profitability measures in explaining MVA. On the other hand, the re sult concerning the superiority of NCF over EVA in explaining MVA is consistent with the results of the previous studies of (De Wet, 2005; Visaltanachoti, Luo & Yi Yi, 2008; Khan, Aleemi & Qureshi, 2016) that revealed that the traditional cash flow measure are superior to EVA in explaining MVA. 66 International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 CONCLUDING REMARKS Motivated by the continuous debate about the superiority of Economic Value Added (EVA) over the traditional accounting performance measures in explaining the Market Value Added (MVA), as a proxy for shareholder value, this study investigated if EVA is superior to Net Operating Profit after Tax (NOPAT) and Net Cash Flow (NCF) in explaining the change in Market Value Added (MVA). A sample of (71) non-financial firms listed on Amman Stock Exchange (ASE) for the year 2016 were analyzed. Univariate and multivariate regression analyses were performed in order to empirically test the study hypotheses. The univariate regression results indicated that NCF have the strongest explanatory power in explaining the change in MVA, followed by EVA and finally by NOPAT. This was confirmed by the stepwise multivariate regression, which was performed to find out the best set of independent variables that explain the change in MVA, where the final significant regression model includes only the NCF and EVA, while the NOPAT was dropped because it does not add additional significant explanatory power to NCF and EVA in explaining the change in MVA. In light of the results of this study and previous studies, it could be argued that the debate about the superiority of EVA over the traditional accounting measures will continue. Two possible justifications to the mixed and controversial results could be taken into consideration: First, MVA is the appreciation of the firm market value over the book value of its invested capital. Where, the firm market value is the sum of the market values of its equity and debt. So, the MVA is affected by firm stock market price, which in turn could be affected by other factors (e.g., economic va riable, investors’ behaviors, supply and demand) that differ across countries. So, the empirical results concerning the superiority of EVA over the traditional accounting measures will differ. In this regard, Kramer & Pushner (1997) stated that “much of th e determination of MVA remains unexplained”. Second, the developer of EVA (Stern Stewart & Co.) suggested more than 160 adjustments to eliminate the distortions in the accounting profit. These adjustments made the calculation of EVA very complex, as stated by Keys, Azamhuzjaev & Mackey (2001) and Nagarajan (2015), which in turn was reflected on the few numbers of adjustments that were made in practice, as concluded by Sirbu (2012), and because the firms do not disclose all information that is necessary to make these adjustments, the previous studies varied in the degree of considering these adjustments, accordingly they revealed controversial results. Finally, this study recommends the use of EVA along with the traditional accounting measures because they are not substitutes for each other. Instead, EVA should be seen as an enhancement to the traditional accounting measures, which if used properly with them, will provide a more powerful tool to evaluate the performance. Sharma & Kumar (2012) asserted this when they indicated that the investors should use EVA along with the traditional accounting measures for decision making. 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EVA Versus Conventional Performance Measures – Empirical Evidence from India. Proceeding of ASBBS, 19 (1), 804 -815. International Journal of Business, Accounting, and Finance, Volume 13, Number 1 , Spring 2019 69 Sirbu, A. (2012). Economic Value Added (EVA) Approach in Russia. Concepts. Approaches. Instruments. Review of International Comparative Management , 13 (2), 305-312. Sparling, D., & Turve, C. G. (2003). Further Thoughts on the Relationship between Economic Value Added and Stock Market Performance. Agribusiness, 19 (2), 255 -267. Stancu, I., Obrejabrasoveanu, L., Ciobanu, A., & Stancu, A. T. (2017). Are Company Valuation Models the Same?-A Comparative analysis Between the Discounted Cash Flows (DCF), the Adjusted Net Assets, Value and Price Multiples, the Market Value Added (MVA) and the Residual Income (RI) Models. Economic Computation & Economic Cybernetics Studies & Research , 51 (3), 5-20. Thenmozhi, M. (2000). Market Value Added and Share Price Behaviour: an Empirical Study of Best Sensex Companies. Delhi Bu siness Review, 1 (1). Tortella, B. D., & Brusco, S. (2003). The Economic Value Added (EVA): an Analysis of Market Reaction. Advances in Accounting , 20, 265 -290. Tsuji, C. (2006). Does EVA beat earnings and cash flow in Japan?. Applied Financial Economics , 16 (16), 1199-1216. Turve, C. G., & Lake, L. (2000). The Relationship between Economic Value Added and the Stock Market Performance of Agribusiness Firms. Agribusiness, 16 (4), 399-416. Visaltanachoti , N., Luo, R., & Yi Yi. (2008). Economic Value Added (EVA) and Sector Return. Asian Academy of Management Journal of Accounting & Finance , 4 (2), 21-41. Worthington, A. C., & West, T. (2004). Australian Evidence Concerning the Information Content of Economic Value-Added. Australian Journal of Management , 29 (2), 201-223. About the Author: Ahmad N. Obaidat is Associate Professor of Accounting at Tafila Technical University, Jordan. Dr. Obaidat has twelve years teaching experience and his main research interests are in the areas of market related studies. Copyright ofInternational JournalofBusiness, Accounting, &Finance isthe property of International AcademyofBusiness &Public Administration Disciplines(IABPAD), LLCand its content maynotbecopied oremailed tomultiple sitesorposted toalistserv without the copyright holder’sexpresswrittenpermission. However,usersmayprint, download, oremail articles forindividual use.
Instructions For this assignment, you will write an essay in which you compare and contrast the following strategic measurement tools used by business and human resource professionals as presented in
Influence of the Indicators Return on Investment and Earning per Share on the Decision of the Indivi dual Investor – an Econometric Approach No. 4 (1 68 )/20 22 681 Influence of t he Indicators Return on Investment and Earning per Share on the Decision of the Individual Investor – an Econometric Approach Cristian LUNGU, Ph. D. Student, West University of Timisoara, e-mail: [email protected] -uvt.ro Abstract In a global financial crisis, access to various investment resou rces becomes inevitable. Stable economic growth is possible under maximum use of accumulated savings for investment purposes. Ensuring favorable conditions for investing the population’s savings in national investment assets remains an urgent problem. Citi zens’ savings, constituting their private property, should be used in the investment process, considering the differences between their individual preferences, the economic development of regions etc. In countries with developed market economies, individua l investors are the main participants in the market for investment resources. By occupying a certain part of the investment market, individual investors serve as an important stabilizing force in times of crisis. Thus, this study aims to evaluate the impac t of ROI and EPS indicators on the investment strategy of natural persons on the Bucharest Stock Exchange and to predict the behavior of investors according to the variation of these variables. Key words: return on investments; earning per share; stock mar ket; investment; JEL Classification : G10, G11, M42 Audit Finan ciar, XX, Nr. 4(168) /2022, 681 -688 ISSN: 1583 -5812 ; ISSN on -line: 1844 -8801 To cite this article: Lungu, C. (2022), Influence of the Indicators Return on Investment and Earning per Share on the Decision of the Individual Investor – an Econometric Approa ch, Audit Financiar , vol. XX, no. 4(168)/2022, pp. 681 -688 , DOI: 10.20869/AUDITF/2022/168/025 To link this article: http://dx.doi.org/10.20869/AUDITF/2022/168/025 Received: 25.05.2022 Revised: 2.08.2022 Accepted: 8.10.2022 Cristian LUNGU AUDIT FINANCIAR , year XX 682 1. Introduction The importance of individual investors for the development of the economy and the stock market is highlighted by the fact that the investment activity of citizens is seen as an indicato r of the development of market relations. From the point of view of the development of the financial market as a whole and the stock market, its component, the massive attraction of investors – natural persons is considered a necessary condition. On the Ro manian stock exchange, individual investors can form an investment portfolio in different ways. The first is with the help of a professional participant in the securities market (broker, trustee), which in turn involves either transferring funds for the ad ministration of the trust to a broker, or making independent decisions on the purchase and sale of securities, while the broker only provides technical access to the exchange market. The second way is with the help of collective investment institutions. In the current context, the volatile economic environment forces any investor in a stock market to have considerable funds and financial – accounting skills to make a profit/income as high as possible and reduce the losses related to the trading activity. Thus , by imposing some “minimum trading requirements” to achieve the objective, the need to study the important factors that contribute to shaping the investment strategy of natural persons appears. Taking into account the universal character of the ROI and EP S financial indicators in the stock market investment strategies of individual and institutional investors, the following objectives were formulated:  Evaluation of the impact of ROI and EPS on the investment strategy of individuals on the Bucharest Stock E xchange;  Forecasting the investment strategy according to the variation of the ROI and EPS indicators. The proposed study was developed in five sections: the first part presents the context of the research, the second section is dedicated to the review of the specialized literature existing up to the present moment, and the following two sections include the research methodology together with the results obtained. The final section, fifth, highlights the conclusions resulting from the econometric analysis. 2. Specialized literature overview In a concise, selective way, the current state of knowledge, in a national and international context, will be highlighted in what follows. The primary objective of investors to make investment decisions is to obtain a max imum level of financial benefits. For most investors active on the stock market, the profitability of companies is an important criterion in determining the investment strategy. The specialized research carried out by Meythi and Hartono (2012) at the level of the Indonesian Stock Exchange, came to the conclusion that ROI and EPS information had a more significant impact on investors’ decisions, compared to other financial performance indicators. Besides this, Qodriyah (2018) in his study on companies in the manufacturing industry showed that information on return of investment can be used to evaluate the performance of entities and has a major importance on the level of stock market capitalization Vasilescu (2011) highlighted the importance of using indicat ors such as value added (EVA), return on capital employed (ROCE), return on investment (ROI), earnings per share (EPS) and market value added (MVA) that could influence the process decision -making and investment plan remodelling. Badruzaman (2020) is of th e opinion that a high EPS reflects the level of effectiveness and efficiency of the company’s operations in managing the company, thus influencing investors to invest in the company. Hassanzadeh and Bigdeli (2019) believe that ROI and EPS indicators are he lpful because they allow to examine options and make a more informed choice. They are also an essential component of an investment strategy, as they become “proof” that investing money in a project is a sound business decision. Susetyo (2013) conducted res earch to determine how the market reacted to the disclosure of earnings per share by various food and beverage companies. The study found that earnings information can help investors make better decisions. Several studies have shown this to be true, such a s Praono and Christian (2004), Dimitros et al. (2013) and Nord Allah (2011). Different research found that earnings per share (EPS) had no significant impact on stock returns. This was discovered by Setivironi (2011), Tiswanu (2011) and Rahim (2013), who a ll came to this conclusion. Influence of the Indicators Return on Investment and Earning per Share on the Decision of the Indivi dual Investor – an Econometric Approach No. 4 (1 68 )/20 22 683 3. Research methodology 3.1. Structure of the analyzed sample The econometric analysis of the impact of ROI and EPS indicators on the investment strategy of individual investors is carried out at the level of a sample of 65 comp anies listed on the Bucharest Stock Exchange. The temporal interval on which the study was carried out is 2017 -2021, including a number of 1,950 observations. The distribution of companies by type of activity is described in Figure no. 1 . There is a concen tration of observations that show that most companies operate in the production & materials industry (40%), utilities & services (22%), respectively, heavy industry (12%), totaling over 70% of the analyzed sample. Figure no. 1. Structure of the sample by type of activity Source: Author’s projection 3.2. Empirical data analysis The first part of this study focuses on analyzing the extent to which ROI and EPS indicators influence the strategic decisions of individual investors on the Bucharest Stock Exchange. To test the existence of a link between these three variables, at the BVB level, we opted for an econometric Cristian LUNGU AUDIT FINANCIAR , year XX 684 analysis based on the method of least squares (OLS). Thus, the econometric function related to the stated hypotheses is presented as follo ws: Yt = α + β*Xt + εt, where: Yt – the dependent variable: the individual investor’s decision; α – the coefficient of the free term; β – the coefficient of the independent variable; Xt – the independent variables: ROI and EPS indicators; εt – residual error; t – period of time (2017 – 2021). In the second part of the study, the forecasting of the investment strategy according to the variation of the ROI and EPS indicators was carried out based on a VAR model, which can be written in the form of the fol lowing equation: The investor’s decision = + * + * + where: – the coefficient of the free term; , – are the coefficients of the e ndogenous variables; – denotes the residual error. Carrying out the VAR econometric analysis involved the use of specialized software (for example, Eviews) to complete the following stages: I. Application of stationarity tests A series is sta tionary if a shock to it is temporary and absorbed over time. If there is a non -stationary series, a stationary series is obtained by differentiation. At the level of this study, we tested the stationarity of the series based on the Augmented Dicley -Fuller and Philips -Peron tests. II. Performing the Granger test Using the Pairwise Granger causality test allows checking the proportion in which the current level of a variable is due to previous levels. At the same time, by adding independent variables, the ex planation can be improved. III. Selecting the appropriate VAR model and lag To select the VAR model and the appropriate lag, the “VAR Lag Order Selection Criteria” test was used, the results of which present decision -making information regarding the crite ria LR (sequential modified LR test statistic), FPE (Final prediction error), AIC (Akaike information criterion), SC (Schwarz information criterion), HQ (Hannan -Quinn information criterion). IV. Checking the stability condition of the model Checking the st ability condition of the VAR model is done by using the AR Root functions. V. Identification of impulse functions The evaluation of the effect of a shock of the current or future variation of the independent variables on the investor’s decision was made ba sed on the graphical illustration of the results obtained after performing the Cholesky test. 4. Results and discussions 4.1. Assessing the extent to which ROI and EPS indicators influence the strategic decisions of individual investors Analyzing the data presented in Table no. 1 , the following conclusions can be drawn: 1. The probabilities attached to the test are lower than the 5% relevance level; therefore, the coefficients are considered statistically significant; 2. The correlation coefficients (R -squared) c onfirm that throughout the analyzed period, there is a significant statistical link between the dependent variable – the individual investor’s decision, and the independent variables – the ROI and EPS indicators, the changes in investors’ decisions being r eflected in the change in ROI and EPS ( for example, in the case of 2021, a 1 pp increase in the ROI indicator causes an increase in the private placements of individual investors by 0.17 pp, and in the case of an increase in the EPS indicator by the same value, the investments of natural persons in the analyzed sample increase by 0.49 pp ). Therefore, it can be appreciated that the built model can be considered representative to describe the link between ROI, EPS indicators and the individual investor’s dec ision at the Bucharest Stock Exchange level in the period 2017 -2021. Influence of the Indicators Return on Investment and Earning per Share on the Decision of the Indivi dual Investor – an Econometric Approach No. 4 (1 68 )/20 22 685 Table no.1. The results of the regression equation of the influence of ROI and EPS on the individual investor’s decision Year (2017) Regression coefficient Prob. ROI 0,147009 0,003212 EPS 0,327305 0,023261 R-squared 0,229161 Year (2018) Regression coefficient Prob. ROI 0,116349 0,017352 EPS 0,454488 0,003561 R-squared 0,207898 Year (2019) Regression coefficient Prob. ROI 0,119746 0,038251 EPS 0,430971 0,012581 R-squared 0,338 442 Year (2020) Regression coefficient Prob. ROI 0,223548 0,014791 EPS 0,450259 0,001597 R-squared 0,266966 Year (2021) Regression coefficient Prob. ROI 0,173447 0,012561 EPS 0,498425 0,008935 R-squared 0,208540 Source: Own processing using Eviews 10 4.2 . Forecasting the investment strategy according to the variation of the ROI and EPS indicators In the first stage of the VAR analysis, the results of the Augmented Dicley – Fuller test showed that the series are not stationary, being represented b y a unit root. As a result, we proceeded to the first -order differentiation of the series, and the results indicated that the first -order integrated series are stationary 1 (see Table no. 2 , Philips -Peron test). Table no. 2. Testing the stationarity of the series based on the ADF and PP tests Test results – Augmented Dickley – Fuller Test results – Philips – Peron Null Hypothesis: The investor’s decision has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey -Fuller test statistic -0.935561 0.7707 Test critical values: 1% level -3.534868 5% level -2.906923 10% level -2.591006 Null Hypothesis: D( The investor’s decision ) has a un it root Exogenous: Constant Bandwidth: 0 (Newey -West automatic) using Bartlett kernel Adj. t -Stat Prob.* Phillips -Perron test statistic -0.103745 0.0341 Test critical values: 1% level -3.536587 5% level -2.907660 10% level -2.591396 Null Hypothesis: ROI_2021 has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey -Fuller test statistic -0.081089 0.9466 Test critical values: 1% level -3.534868 5% level -2.906923 10% level -2.591006 Null Hypothesis: D( ROI_2021 ) has a unit root Exogenous: Constant Bandwidth: 0 (Newey -West automatic) using Bartlett kernel Adj. t -Stat Prob.* Phillips -Perron test statistic 0.370709 0.0202 Test critical values: 1% level -3.536587 5% level -2.907660 10% level -2.591396 1 Probability >0.05 represent s non -stationary series Cristian LUNGU AUDIT FINANCIAR , year XX 686 Test results – Augmented Dickley – Fuller Test results – Philips – Peron Null Hypothesis: EPS_2021 has a unit root Exogenous: Constant Lag Length: 9 (Automatic – based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey -Fuller test statistic 0.611708 0.9889 Test critical values: 1% level -3.552666 5% level -2.914517 10% level -2.595033 Null Hypothesis: D( EPS_2021 ) has a unit root Exogenous: Constant Bandwidth: 1 (Newey -West automatic) using Bartlett kernel Adj. t -Stat Prob.* Phillips -Perron test statistic -3.974865 0.0028 Test critical values: 1% level -3.536587 5% level -2.907660 10% level -2.591396 Source: Own processing using Eviews 10 Based on the Pairwise Granger causality methodology, presented in Tabl e no. 3 , it was verified whether the changes in the ROI and EPS indicators led to the remodeling of the investment strategy of natural persons. A value of less than 0.05 for the probability displayed by Eviews implies rejection of the null hypothesis. For a level of significance greater than 5%, it follows that the variation in the values of the financial indicators is a cause of the change in the investor’s decision. Table no. 3. Results of the Pairwise Granger test Pairwise Granger Causality Tests Date: 07/30/22 Time: 13:30, Sample: 1 67, Lags: 2 Null Hypothesis: Obs F-Statistic Prob. ROI_2021 does not Granger Cause The Investor’s Decision _2021 64 0.13528 0.8737 EPS_2021 does not Granger Cause The Investor’s Decision _2021 64 0.03065 0.9698 Source: Own processing using Eviews 10 The results of the VAR Lag Order Selection Criteria test, presented in Table no. 4 , indicate that for the eight theoretical lags, the selection indicators (FPE, AIC, SC, HQ) recomm end a lag equal to 0 for the VAR model “Investor Decision – ROI – EPS”. The appropriate choice of this lag is also confirmed by checking the degree of stability of the VAR model through the AR Root function (see Table no. 5 ). Table no. 4. Criteria for selecting the order of the VAR lag VAR Lag Order Selection Criteria Endogenous variables: The Investor’s Decision _2021 ROI_2021 EPS_2021 Exogenous variables: C Date: 07/30/22 Time: 13:32, Sample: 1 67, Included observations: 60 Lag LogL LR FPE AIC SC HQ 0 -323.4714 NA* 10.68413* 10.88238* 10.98710* 10.92334* 1 -316.5685 12.88541 11.46441 10.95228 11.37115 11.11613 2 -312.5501 7.099233 13.57062 11.11834 11.85136 11.40506 3 -309.6 835 4.777680 16.75285 11.32278 12.36996 11.73239 4 -306.9804 4.234869 20.90490 11.53268 12.89400 12.06517 5 -305.0180 2.878098 26.93073 11.76727 13.44274 12.42264 6 -292.7016 16.83247 24.79684 11.65672 13.64635 12.43497 * indicates lag order selected by the criterion Source: Own processing using Eviews 10 Influence of the Indicators Return on Investment and Earning per Share on the Decision of the Indivi dual Investor – an Econometric Approach No. 4 (1 68 )/20 22 687 Table no. 5. Verification of the stability condition of the model Roots of Characteristic Polynomial Endogenous variables: The Investor’s Decision _2021 ROI_2021 EPS_2021 Exogenous variables: C Lag specification: 1 2, Date: 07/30/22 Time: 13:37 Root Modulus 0.990879 – 4.119421i 4.236918 0.990879 + 4.119421i 4.236918 0.347840 – 0.320326i 0.472865 0.347840 + 0.320326i 0.472865 No root lies outside the unit circle. VAR satisfies the stability condition. No root lies outside the unit circle. Source: Own processing using Eviews 10 The processing of financial data according to the requirements of performing a VAR regression model allowed us to identify two impulse responses, illustrated in Figure no. 2 , which evaluate the effect of a shock on the current or future variation of the variables ROI, EPS and the decision of the individual investor. Figure no. 2. Resul ts of the VAR model using the Cholesky test Source: Own processing using Eviews 10 Therefore, the application of the Cholesky test generated the following conclusions: 1. A shock of +1% of the ROI indicator at the level of the studied sample of 65 compa nies traded on the Bucharest Stock Exchange will generate in the first forecasted year an increase of 0.18% in the investments made by individuals, followed by a decrease in the pace of attracting financial placements by the fourth year and Cristian LUNGU AUDIT FINANCIAR , year XX 688 recovery of gro wth at the level of the third year by the end of the forecasted year seven; 2. A shock of +1% of the EPS indicator at the level of the studied sample of 65 companies traded on the Bucharest Stock Exchange implies an approximate evolution to that found in the case of the ROI indicator, therefore, it will generate a 0.60% increase in investments made by natural persons, followed by a decrease in the pace of attracting financial placements until the fifth year, maintaining this level until the end of the forecast ed time interval. 5. Conclusions The obtained results confirm that the strategy of the individual investor is influenced by the EPS and ROI indicators. The application of the two econometric methods, the OLS method (the least squares) and the VAR method, r egarding forecasting the evolution of the studied variable according to the change of the independent variables at the level of the studied sample highlights the fact that the EPS indicator has a more significant weight compared to the ROI indicator, in th e case of determining the strategy of investment of natural persons. Therefore, this aspect tells us the tendency of the individual investor to orientate financial placements within companies that register a considerable profit per share, classifying the c riterion of recovery of investments over time as a secondary place. The limits of the research consisted in the fact that there is no certified database, the data and indicators were extracted and calculated manually, being taken from the set of financial statements published by companies on the website of the Stock Exchange. Future research directions aim to expand the sample both nationally and internationally, analyzing in addition to financial indicators and cultural/social criteria. BIBLIOGRAPHY 1. Ba druzaman, J., (2020). The impact of earning per share and return on equity on stock price, Journal Syst Rev Pharm , Vol. 15, No. 2, pp. 245 -256; 2. Bigdeli, T., Hassanzadech, M. (2019). Return of Investment (ROI) in Research and Development (R&D): Towards a fr amework , Journal of The International Conference COLLNET , Vol. 3, No. 4, pp. 42 -47; 3. Dimitros. I., (2006). The Introduction of Economic Value Added (EVA) in The Greek Corporate Sector, The South European Review of Business and Accounting , Vol. 4, No.2, pp. 23 -29; 4. Meythi.P, Hartona. H., (2012). The effect of earnings and cash flow information on stock prices, Scientific Journal of Accounting , Vol. 7, No. 2, pp. 87 -93; 5. Nurdillah, M. 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However,usersmayprint, download, oremail articles forindividual use.