Lợi nhuận bền vững và dự báo lợi nhuận bền vững: Nghiên cứu cho các doanh nghiệp niêm yết tại Việt Nam

Nội dung text: Lợi nhuận bền vững và dự báo lợi nhuận bền vững: Nghiên cứu cho các doanh nghiệp niêm yết tại Việt Nam

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 SUSTAINABLE EARNINGS AND FORECAST OF SUSTAINABLE EARNINGS: CASE OF VIETNAM LISTED COMPANIES LỢI NHUẬN BỀN VỮNG VÀ DỰ BÁO LỢI NHUẬN BỀN VỮNG: NGHIấN CỨU CHO CÁC DOANH NGHIỆP NIấM YẾT TẠI VIỆT NAM Nhung Hong Do, Nha Tue Van Pham, Hien Thu Thi Tran National Economis University nhungdh@gmail.com ABSTRACT The literature of earnings quality analysis in developed countries suggests earnings persistence as a noteworthy determinant of future earnings forecast and stock valuation. However, research of sustainable earnings in developing countries is still highly underdeveloped. The study was conducted to provide a comprehensive analysis of earnings persistence of business enterprises in Vietnam as an example of developing economies in South-East Asia. Firstly, by applying fixed effect regression model for 1,279 publicly traded companies (excluding banking and financial services firms) on Vietnam’s Stock Exchange from 2008 to 2017, we found that the correlation between profit margin change and asset turnover change and the proportion of accruals in sales to have positive effect on earnings persistence while market concentration, capital intensity, correlation between earnings change and profit margin change, correlation between earnings change and asset turnover change, asset growth, correlation between directionally identical earnings/asset turnover changes and asset growth, past profitability, correlation between current and past profitability all had negative effect with different statistical significance levels. Such empirical results then provided the basis to measure the “Pers” index, which is the proxy of earnings persistence level. For Vietnamese listed firms, the average Pers index was estimated to be - 0.0850 for the period from 2008 to 2010, indicating low level of earnings persistence. We also incorporated earnings persistence level into future earnings forecast, using the Pers index. Besides, the research was extended to examine the effects of two widely recognized influential factors (earnings volatility and accruals) on different aspects of earnings persistence. In particular, two additional dynamic regression models were used to examine the effects of earnings volatility on earnings persistence and earnings predictability of firms with the most extreme earnings volatility levels, and one additional dynamic regression model was used to examine the effect of accrual components on future earnings. Key words: sustainable earnings, forecast of earnings persistence. TểM TẮT Tổng quan nghiờn cứu về chất lượng lợi nhuận của cỏc doanh nghiệp tại cỏc quốc gia đang phỏt triển cho thấy lợi nhuận bền vững là yếu tố quyết định, quan trọng để dự bỏo lợi nhuận và giỏ trị cổ phiếu trong tương lai. Tuy nhiờn, nghiờn cứu về lợi nhuận bền vững tại cỏc nước đang phỏt triển vẫn cũn nhiều hạn chế. Nghiờn cứu này được thực hiện để cung cấp những phõn tớch tổng hợp về lợi nhuận bền vững của cỏc doanh nghiệp tại Việt Nam, như một vớ dụ cho cỏc nền kinh tế đang phỏt triển ở Đụng Nam Á. Thứ nhất, nghiờn cứu kiểm định mụ hỡnh hồi quy tỏc động cố định cho 1.279 cụng ty niờm yết (khụng bao gồm cỏc cụng ty tài chớnh và ngõn hàng) trờn Sàn Giao dịch Chứng khoỏn Việt Nam, giai đoạn từ 2008 - 2017, kết quả cho thấy cú mối tương quan giữa thay đổi lợi nhuận biờn và vũng quan tài sản, tỷ trọng doanh thu dồn tớch cú tỏc động tớch cực đến lợi nhuận bền vững trong khi mức độ tập trung thị trường, quy mụ vốn tương quan giữa thay đổi lợi nhuận và lợi nhuận biờn, tương quan giữa thay đổi lợi nhuận và thay đổi vũng quay tài sản, tăng trưởng tài sản, tương quan giữa thay đổi tỷ suất sinh lời tổng tài sản và tăng trưởng tài sản, lợi nhuận trong quỏ khứ, mối tương quan giữa lợi nhuận hiện tại và quỏ khứ đều cú tỏc động tiờu cực với cỏc mức ý nghĩa thống kờ khỏc nhau. Cỏc kết quả thực nghiệm cũng đó cung cấp cơ sở để đo lường chỉ số của chỉ số Pers, với mức độ duy trỡ lợi nhuận. Đối với cỏc cụng ty niờm yết tại Việt Nam, chỉ số Pers trung bỡnh được ước tớnh là -0,0850 trong giai đoạn 2008 - 2010, kết quả này cho thấy mức độ bền vững của lợi nhuận vẫn cũn thấp. Nghiờn cứu cũng kết hợp mức độ bền vững lợi nhuận vào dự bỏo thu nhập trong tương lai bằng việc sử dụng chỉ số Pers. Bờn cạnh đú, nghiờn cứu cũn được mở rộng để kiểm định chi tiết hơn tỏc động của 2 nhõn tố quan trọng là mức độ biến động của thu nhập và cỏc khoản lợi nhuận dồn tớch tới cỏc phương diện khỏc nhau của lợi nhuận bền vững. Cụ thể, 2 mụ hỡnh hồi quy trễ được sử dụng để kiểm định tỏc động của mức biến động lợi nhuận tới mức lợi nhuận bền vững và khả năng dự bỏo lợi nhuận đối với những doanh nghiệp cú độ biến động lợi nhuận cực đoan nhất, 1 mụ hỡnh hồi quy trễ được sử dụng để kiểm định tỏc động của cỏc bộ phận lợi nhuận dồn tớch tới lợi nhuận tương lai. Từ khúa: Lợi nhuận bền vững, dự bỏo lợi nhuận bền vững. 304
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 1. Introduction Earnings persistence has been addressed in the literature of profit forecasting, which provides the basis for security valuation and business enterprise valuation. Investors wish to determine the fair price of stocks and thus identify the overpriced and underpriced stocks. They will subsequently select the underpriced stocks as investment targets. Therefore, the key question for every investor is how to determine stocks’ fair prices. Basically, the fair price of a stock can be computed by applying various discounted earnings models, most of which require firm’ future earnings prediction. Although the forecasting methods may vary, every prediction of future earnings starts with a review of current earnings. For a long time, it was believed that firms’ earnings did not strictly follow “random walk” pattern, instead past earnings and future earnings are somehow correlated (Graham and Dodd, 1951; Ball and Watts, 1972). The likelihood of current earnings to be sustained in future is referred to as earnings persistence. Ceteris paribus, future earnings tend to be more assured when earnings are more persistent. Therefore, estimating earnings persistence and incorporating it should be an essential part of developing earnings prediction models. Another approach towards earnings forecast based on sustainable earnings analysis was proposed by Frankel & Litov (2009) as they examined earnings predictability differences of firms with different levels of past earnings volatility. Their findings show that past earnings volatility negatively affected current earnings persistence, thus it could be used to predict future earnings. Frankel & Litov (2009) also confirmed the importance of sustainable earnings forecast for business enterprise valuation. Similarly, Moienadin & Tabatabaenasab (2014) proved the relationship between current and future earnings as well as the relationship between sustainable earnings and cash dividend ratio. Previous studies in developed countries identified various influential factors of earnings persistence such as business cycles (Johnson, 1999); profit margin level (Eli Amir, Eti Einhorn, Itay Kama, 2012), etc. However, the influences of operating cash flow, accruals and profit margin changes on earnings persistence have not been analyzed in depth. In Vietnam, studies of sustainable earnings are also very limited as they mainly relied on the analyses of past profit growth rates and business environment to predict short-term future earnings, which involved subjective and intuitive assessment of the analysts. Besides, a few studies developed long-term earnings forecast models based on earnings’ influential factors of firms in certain industries but the impact of each factor was not analyzed in detail. For stock markets in developing countries, questionable accounting and financial information quality of business enterprisesare still a potential trouble for investors, which calls for more advanced research. For instance, a number of Vietnamese listed companies were reported to have highly volatile earnings over time or even experience financial distress and bankruptcy due to unsustainable business operations and low predictability of future earnings, which gave way to security market shocks and investors’ losses. In such a situation, investors are supposed to improve their perception of earnings persistence and its importance for future profitability forecasting and investment decision making. Nonetheless, research on earnings persistence in developing countries is apparently not so well-established as earnings persistence is sparsely and incomprehensively estimated and integrated into forecasting models of their business enterprises. Besides, there was no widely publicized research of influential factors of sustainable earnings in those countries either. As a result, our study was conducted as an attempt to cope with such research gap. As we focused on identifying the impacting factors of earnings persistence of publicly traded companies in Vietnam and subsequently examining the effect of earnings persistence on earnings forecast of those firms, we hope to contribute to the literature of sustainable earnings analysis in developing economies where a lot of investors are in need of a theoretically supported and practically applicable guidance for stock valuation and investment decision making. 305
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 In general, despite the underdeveloped literature of sustainable earnings in Vietnam, it is undeniably necessary to develop long-term sustainable earnings forecast model as firms’managers and investors can benefit from it in their decision making. Therefore, we conduct this research to firstly identify the influential factors of sustainable earnings, develop a proxy to measure firms’ earnings persistence level, which will then be employed for future earnings forecast of business enterprises in Vietnam. 2. Literature and hypothesis development Earnings persistence has its roots from works of profit and stock return forecast. In general, the ability of current earnings to be sustained in future is termed earnings persistence. To estimate earnings persistence, the following lag regression model was employed in early relevant works (Komendi and Lipe, 1987; Ou and Penman, 1989; Lev and Thiagarajan, 1993; Sloan, 1996): Et = α0 + α1 Et-1 + εt Where Et and Et-1 are respectively earnings of the current and previous periods. Firm’s earnings of different periods are correlated if the slope coefficient α1 is statistically significantly different from zero. α1 closer to 1 indicates higher earnings persistence while α1 being statistically insignificant or very close to 0 indicates random walk pattern of earnings over time. Till now, such approach is still the most commonly applied in the literature of earnings persistence estimation even though the measurement of earnings variable E may differ depending on researchers’ perspectives (Abarbanell and Bushee, 1997; Fairfield et al., 1996; Chan et al., 2001; Fairfield and Yohn, 2001; Sloan et al., 2002; Fairfield et al., 2003). Moreover, Penman and Zhang (2004) extended the conceptual discussion of earnings persistence as they attributed the sustainable component of additional earnings to investment in net operating assets while earnings improvement due to change of return on assets is transitory or unsustainable. Such conceptualization of earnings persistence/sustainable earnings has great contribution to the practice of asset valuation as it provides investors with a theoretical framework of forecasting future earnings which will subsequently be discounted to determine present value of the investment (Brownlee et al., 1990; Haskins et al., 1993; Kieso and Weygandt, 1995; Damodaran, 1999). Hence the practice of earnings prediction and asset valuation can benefit from improving earnings persistence and earnings quality (Dechow and Schrand, 2004; Richardson et al., 2005). 2.1. The influential factors of earnings persistence Previous studies identify a number of potentially impacting factors of earnings persistence, which can be categorized into 3 groups: (Firm-specific) financial performance factors, (firm-specific) accounting methodology factors and industry characteristic factors. (1) Financial performance factors Earnings volatility is arguably the most studied factor. There are both theoretical arguments and empirical evidences for negative relationship between volatility level and earnings persistence although volatility level measurement may vary (Brooks and Buckmaster, 1976; Freeman and Tse, 1992; Das and Lev, 1994; Beaver et al., 1970; Dichev and Tang, 2004; Frankel and Litov, 2008). Such negative impact can be explained as the result of competition pressures in the market of investment assets, which makes extraordinary earnings unsustainable (Beaver et al., 1970). Chen (2004) took a further step as he not only examined earnings persistence effect of earnings volatility level but also that of forces behind earnings volatility, which are represented by the correlation between earnings volatility and profit margin changes as well as asset turnover changes. However, such novel approach has not been adapted much in later studies. Asset growth may contribute to earnings persistence as well. As Penman and Zhang (2004) suggested, earnings increases are sustainable only if they are the result of additional investment in net operating assets. Chen (2004), Li (2005), Kocamis and Gungor (2016) integrated asset growth into their 306
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 earnings persistence model accordingly as an independent variable which is proven to have significantly positive sign. In addition, capital structure also has well-developed theoretical framework for its effect on future earnings. Nonetheless, studies of capital structure’s influence on earnings persistence are limited with unclear or non-unanimous conclusion. In the empirical research by Al-Momani (2017), three different capital structure ratios were employed but only one of them was proven to have statistically significant and positive effect on earnings persistence. Hogan (2013) also found capital structure to have significant effect on earnings persistence of a certain group of companies (not all companies in the sample) but such effect turned out to be negative. (2) Accounting methodology factors Previous works found that the accounting of losses can impact earnings persistence as some firms may recognize expected loss transactions in current period (Basu, 1997). Accounting conservatism also requires firms to recognize losses due to temporarily negative shocks on a timely basis while positive shocks’ effects are only gradually recognized over several periods, making losses less long-lasting than profit. Therefore, losses are generally less persistent than profit in practice (Wang and Zhan, 2006; Narayanamoorthy, 2006). Accounting methodology may also exert its influence on sustainable earnings via the formation of accruals. Empirical studies by Dichev and Tang (2004), Frankel and Litov (2008) both confirmed negative impact of accruals’ amount on earnings persistence as there are potentially more subjectivity and mistakes by accountants in the accounting of accruals in comparison with cash flow accounting (Sloan, 1996; Xie, 2001; Ball et al., 2016). Darjezi (2016), Scott et al. (2005), Dechew and Ross (2005), Wang and Zhan (2006), Atashband (2004) took further step to investigate earnings persistence effect of accruals’ certain aspects such as accruals’ quality and their duration. In contrary to accruals, the cash flow component of earnings was proven to positively influence earnings persistence (Dechew et al., 2008; Wang and Zhan, 2006; Atashband, 2004). Basically, accruals and cash flows can be regarded as the two integral components of earnings so when the cash flow component’s weight increases, the weight of accruals must decrease (and vice versa) and they have opposite effects on earnings persistence. However, cash flow component is similar to accruals in the sense that not only their amount but also their quality can affect earnings persistence (Dichev and Tang, 2004). (3) Industry characteristic factors The structure of market share of an industry may affect sustainable of firms in such industry. According to Dechow et al. (2009), a firm’s market share indicates its position in the industry and has positive impact on its earnings quality. Previous work by Chen (2004) also empirically proved positive correlation between firm’s market share and its earnings persistence. Market share structure also implies market concentration. The fact that most of the market shares are acquired by a small number of firms signals high level of market concentration and those firms are more capable of sustaining their earnings thanks to their oligopolistic power. A few researchers hence attempted to empirically test earnings persistence effect of market concentration (Chen, 2004; Hogan, 2013) but most of them ended up with a statistically insignificant result. Besides, capital intensity may represent industry’s barriers to entry so intuitively higher capital intensity should result in more sustainable earnings for existing firms of the industry (Lev, 1983; Gregory et al., 2016). Nevertheless, the impact of capital intensity on earnings persistence is not widely examined in empirical studies yet. In Vietnam, research on influential factors of earnings persistence is still very limited. The only notable paper was publicized by Nguyen et al. (2014), who studied the impact of accruals accounting on 307
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 sustainable earnings, but their earnings persistence model did not fully incorporate all the potential impacting factors as suggested in the literature. There are a few studies about influential factors of earnings quality (Le et al., 2013; Duong, 2013; Bui and Nguyen, 2018) but none of them explicitly integrated earnings persistence as the dependent variable in their research models. The lack of a comprehensive study of influential factors of earnings persistence hence implies a considerable research gap in Vietnam as well as other developing countries. As a result, our study makes a comprehensive effort in examining the influential factors of earnings persistence in Vietnam by testing the hypothesis that earnings persistence of Vietnamese listed companies is influenced by all three groups of factors: Financial performance factors, accounting methodology factors and industry characteristic factors (Hypothesis 1). In particular, to investigate the potential impact of earnings volatility, the approach by Chen (2004) was adapted to analyze the relationships between earnings volatility’s driving forces and earnings persistence in depth. Besides, as profitability is proxied by return on operating assets instead of net income, the potential effect of financial leverage is removed and thus not needed to be integrated into the model. 2.2. Earnings persistence and earnings forecast Although earnings persistence has been empirically widely proven to affect future financial performance, works of earnings forecast are largely yet to implicitly employ earnings persistence as the basis for earnings forecast. For instance, a wide range of typical studies by Jackson (2017), Khalai and Zabihi (2016), Demmer (2015), Evans et al. (2017), Fairfield and Yohn (2001), Esplin et al. (2013), Bauman (2013), Schrửder and Yim (2016), Jin (2017) applied various mathematical and statistical methods such as time series regression, earnings disaggregation using DuPont equation or financial and non-financial factor regression analysis to forecast earnings but did not emphasize the allegedly important role of earnings persistence in earnings forecast. Those studies actually intended to examine the efficiency of different forecasting methods and forecasting models in which earnings persistence was not regarded as a variable of interest or even non-existent. Meanwhile, there is just a highly limited number of earnings forecast studies relying on earnings persistence analysis with certain drawbacks. In particular, the research by Penman and Zhang (2004) made an effort to correlate earnings persistence’s influential factors with future earnings volatility but such attempt served only as an intermediate step to achieve their ultimate research objective which is to explain the fluctuations of P/E rather than to predict future profit. Dichev and Tang (2008), Frankle and Litov (2008) also studied the impact of earnings volatility on earnings predictability by comparing earnings predictability across groups of companies with different earnings volatility without constructing an implicit earnings persistence-based forecasting model. Other studies mostly based on analysis of past profitability and allegedly influential factors of sustainable earnings as cash flows and accruals to make predictions of future profitability (Sloan et al., 2002; Fairfield and Yohn, 2003; Atashband, 2004). Chen (2004) was the only one to create a proxy which comprehensively represented earnings persistence and incorporated it into his future earnings forecasting model. The proxy can be calculated based on regression coefficients and values of independent variables of earnings persistence’s impacting factors analysis model. The proxy was empirically proven to be statistically significant too. Nevertheless, that research by Chen (2004) was the only one to achieve such feat. In Vietnam, studies of financial performance forecast are not widely publicized and those applying earnings persistence to forecast future earnings are extremely rare. Those studies apparently have underdeveloped content and methodology as their results are not yet internationally recognized. For example, Nguyen et al. (2014) found cash flow component of earnings to have higher persistence level than accruals and positive effect on earnings forecast accuracy but did not develop anearnings forecast model implicitly based on earnings persistence. A few other studies developed earnings or cash flow 308
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 forecast models but did not integrate earnings persistence into the model, with their results presented only in working papers or internally circulated research reports of some domestic educational institutions so the research quality was still unconfirmed. In short, earnings persistence is yet to be comprehensively and thoroughly applied as the basis of works on future earnings forecast in Vietnam. In summary, the problem of forecasting earnings based on earnings persistence analysis is still not fully resolved even in developed countries. In Vietnam as well as other developing countries, there is even a bigger gap in the literature of sustainable earnings analysis and earnings prediction which needs to be addressed and filled. Therefore, our research is conducted to develop a profitability forecasting model based on earnings persistence analysis of Vietnamese listed companies. Following previous studies especially the research by Chen (2004), we hypothesize that earnings persistence level of Vietnamese listed companies can be determined by analyzing sustainable earnings’ influential factors (Hypothesis 2). Another proposed hypothesis is that future profitability of those firms can be forecasted based on their sustainable earnings analysis (Hypothesis 3). Profitability forecasting method and forecast error analysis method are adapted from Dichev and Tang (2008). Our contribution is to expand the literature of examining sustainable earnings’ impacting factors and applying earnings persistence to predict future earnings, which is still highly insufficient in Vietnam and other developing countries. 3. Methodology 3.1. Research models and variables The model is approached to analyze the persistence profit of listed companies on the Vietnamese stock market by offering factors that affect sustainable profitability and assess its impact on the sustainable of corporate profits. From the identification of influencing factors and impact level on sustainable profits, the study provides a method to estimate the sustainability of corporate profits and hypothesize that sustainable profits are one of factors which help predict the profitability of businesses in the future. Based on the literature review, the factors affecting sustainable profitability of enterprises are categorized into 3 main groups as follows: Firstly, the group of influential factors representing industry characteristics: Table 1: Industry characteristics -based independent variables Predicted Variable Formula Notation Sources sign (Patricia M. Dechow, Weili Ge, Market MS (+) 2006), (Patricia M. Fairfield, Share Teri Lombardi Yohn, 2001) (Lev, 1983), (Chen, 2004), HERF = Sum of squared market shares Herfindahl (Alan Gregory, Julie Whittaker, in the industry HERF (+) Index Xiaojuan Yan, 2016), (Richard Frankel, Lubomir Litov, 2009) (Lev, 1983), (Johnson, 1999), Capital (Alan Gregory, Julie Whittaker, CI (+/-) Intensity Xiaojuan Yan, 2016), (Richard Frankel, Lubomir Litov, 2009) (Lev, 1983), (Richard Frankel, Firm Size SIZE (+) Lubomir Litov, 2009), (Chen, 2004) 309
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Secondly, the group of influential factors representing the financial situation and performance of the business. Table 2: Financial Statement Fundamentals-based independent variables Predicted Variables Formula Notation Sources Signs = 1 if the yearly change of profit margin (Tugce Uzun Kocamis, is in the same direction as the yearly Aysegul Gungor, 2016), Profit Margin change in earnings, and 0 otherwise, DPM (+/-) (Chen, 2004), (Stephen Dummy where profit margin= operating income H. Penman, Xiao-Jun after depreciation/Sales Zhang, 2004) = 1 if the yearly change of asset turnover (Chen, 2004), (Li, 2005), Asset Turnover is in the same direction as the yearly DATO (+/-) (Stephen H. Penman, Dummy change in earnings, and 0 otherwise, Xiao-Jun Zhang, 2004) where asset turnover= Sales/Assets. (Chen, 2004), (Tugce Growth in Assets (Assetst – Assetst-1)/Assetst-1 GA (+/-) Uzun Kocamis, Aysegul Gungor, 2016), (Li, 2005) Interactive Effect = 1 if yearly changes of profit margin (Chen, 2004), (Patricia of PM and ATO and asset turnover are in the difference DPMATO (-) M. Dechow, Catherine Dummy direction, and 0 otherwise. M. Schrand, 2004) = 1 if yearly changes of asset turnover and earnings are both positive, but Interactive Effect (Chen, 2004), (Stephen growth in assets is negative; or if yearly of ATO and GA DATOGA (+) H. Penman, Xiao-Jun changes of asset turnover and earnings Dummy Zhang, 2004) are both negative, but the growth in assets is positive, and 0 otherwise. Thirdly, the group of influential factors representing the accounting method of the enterprise. Table 3: Accounting Methodology-based independent variables Predicted Variables Descriptions Notation Sources Signs = Absolute value of accruals/Sales, where accruals= Change in Current assets – (Chen, 2004), (Narayanamoorthy, Change in cash/cash equivalents – Absolute 2006), (Richard Frankel, Lubomir (Change in current liabilities – Change in ACCR (-) Value of Litov, 2009), (Srinivasan Rangan, debt included in current liabilities – Accruals Richard G. Sloan, 1998) Change in income taxes payable – Depreciation and amortization expense (Chen, 2004), (Johnson, 1999), Loss = 1 when Earnings is negative, and 0 (Nilabhra Bhattacharya, Hemang LOSS (-) Dummy otherwise Desai, Kumar Venkataraman, 2012) (Chen, 2004), (Johnson, 1999), (Nilabhra Bhattacharya, Hemang Decrease = 1 when Change in Earnings is negative DECR (-) Desai, Kumar Venkataraman, Dummy and zero otherwise 2012), (Srinivasan Rangan, Richard G. Sloan, 1998) 310
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Firstly, those independent variables are incorporated into the following model (model 1) to estimate the relationship between the aforementioned factors and the transitory component of standardized annual profit. Model 1: Where SUE is the difference of standardized annual profit scaled by total assets; Fi are factors affecting the sustainability of profits which are divided into 3 groups as described above. Subsequently, the estimation results of model 1’s regression coefficients are used to calculate the profit’s persistence index, which is denoted as “Pers”, as follows: The “Pers” index is then incorporated into sustainability measurement model which is used to estimate the impact of earnings persistence determinants on future earnings: SUEt+k = ak + bk x SUEt + εt+k (1.1) SUEt+k = ak + bk x SUEt + ck x Perst + dk x Perst x SUEt + εt+k (1.2) Model 1 is employed as the “key” model of our research as it provides a comprehensive estimation of potential influential factors of earnings persistence. The discussion section of this paper will concentrate on the estimation results of this model. In addition, we also develop three other models (Models 2, 3 and 4) to investigate the effects of some significant factors on different aspects of earnings persistence in more detail. These models are described below. As earnings volatility is one of the most specifically researched and universally recognized influential factors of profit sustainability in the literature, it makes sense to examine the effect of earnings volatility on future profit in the context of emerging markets which may differ from developed markets. We employ an additional model (model 2) for this purpose: Model 2: Earningsi,t +k = α + β ì Earningsi,t + εi,t+k k = 1 → 5 (2) The earnings variable in the model is earnings before extraordinary item deflated by average total assets. The examination of earnings volatility’s effect on future earnings is conducted rather indirectly. Instead of directly incorporating a proxy for earnings volatility into the model, we divide the sample of selected firms into quintiles based on their earnings volatility levels in each year, which is calculated as earnings’ standard deviation in the last 5 years. We then run regression model (2) for the first quintile (firms with lowest earnings volatility) and fifth quintile (firms with highest earnings volatility) only. Subsequently, we compare the value of beta coefficients of the two regression models and test the statistical significance of their difference. The purpose of this model is to examine whether difference in earnings volatility actually causes difference in future earnings, particularly for the groups of firms with the most extreme earnings volatility. Besides, we expand the investigation of earnings volatility’s effect on earnings predictability by examining the relationship between earnings volatility and future earnings forecast error. Model (3) is developed for this purpose. Model 3: (3) Where High_volt is the dummy variable which is equal to 1 for firms in the highest quintile of earnings volatility and 0 for firms in the lowest quintile (firms in other quintiles are not considered); FE is the forecast error which is equal to the difference between the actual profit and the median of the forecasted profit before the actual profit is announced. This model helps to examine whether difference in earnings volatility actually causes difference in earnings forecast errors (hence difference in earnings predictability), particularly for firms with the most extreme earnings volatility. 311
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Aside from earnings volatility, accruals are also another common influential factors of earnings persistence in the literature. Therefore, it is necessary to investigate the influence of accruals on future earnings in more detail by breaking down accruals into components and examine each component’s earnings persistence effect. Model 4 is developed for this purpose. Model 4: Testing the role of accruals’ components in predicting future profits NIt+1 = α0 + α1 x CACCt + α2 x NCACCt + α3 x FCFt + ųt (4) The variables in model 4 are defined as follows: CACC: Short-term accrued profit from business activities = [Δ (Short-term assets - Cash and cash equivalents) - Δ (Short-term debts - Short-term loans)] / Total average assets NCACC: Long-term accrued profits from business activities = [Δ (Total assets - Short-term assets) - Δ (Total debts - Short-term liabilities - Long-term borrowings) / Total average assets FCF: Free cash flow / Total average assets 3.2. Sample selection The dataset of 1,278 businesses collected as of December 2018 is divided into 9 sectors by Industry Classification Benchmark. In this study, companies in finance, banking and insurance sectors are excluded. This exclusion is appropriate because businesses in these sectors are very different from the rest of the businesses., businesses have historical data less than 4 years are also excluded. Chart 1: Number of companies in sectors from 2007 to 2017 007 008 009 010 011 012 013 014 015 016 017 Basic Materials 67 92 108 119 132 143 157 166 166 164 162 Consumer Goods 104 142 154 160 176 192 202 211 215 214 209 Consumer Services 42 61 73 77 88 94 102 109 110 113 114 Health Care 25 35 38 42 45 47 51 51 50 53 53 Industrials 260 355 406 450 484 504 523 568 575 582 579 Oil & Gas 5 6 6 6 7 8 9 10 10 10 10 Technology 21 24 28 28 27 27 28 29 29 29 29 Telecommunications 2 2 5 5 5 5 5 7 7 7 7 Utilities 31 45 52 59 73 91 95 110 112 112 115 Total 557 762 870 946 1,037 1,111 1,172 1,261 1,274 1,284 1,278 Source: FiinGroup Joint Stock Company Number of enterprises allocated by sector and year respectively. The number of companies by sector increased every year. Enterprises in the Industrials sector account for the majority (45% -48%) and the number of enterprises in the Telecommunications industry accounts for less than 1% in the data set. 3.3. Analysis techniques With large, complex and observations sample and tabulated for all listed companies in the Vietnamese stock market, the Fixed Effects and Random Effects regression methods are selected. i=1, 2, , N and t=1, 2, , T The error of the model is split into two parts: where is the unobserved differences between the objects but does not change over time, t is the other unobserved elements between objects and change over time. 312
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 We used Hausman Test to determine an appropriate estimation method between fixed impact and random impact model of data table. Hausman Test propounded two theories about the correlation among the independent variables of the model which was given in the study (Hausman, 1978) on the basis of inheritance and development from theories in the study (Wu, 1973). Fixed Effects Regression is selected. On the basis of identifying the factors which affected the sustainability of profit in model 1 and special character of the business, the sustainability level was verified through Pers index in model (1.3). This was an important indicator to estimate the sustainability level as well as the volatility of the index in the data table, which brought many statistical and economical significance. The profit forecast: Used a combination of analysis according to the degree of influence which divided by the quintile of the factors and changed the time period to assess the predictability of each element in the model (2) and (3). The quintile regression model divided the data into 5 equal parts (k = 1, 2, 3 or 4) and conducted each section independently to see the difference in each percentile, thence assessed the factors’ influence to the specific model. This was the way for investors to forecast the profit easily in the short-term and long-term and evaluated the accuracy of forecasting models in each different level(Roger, 2005) and (Furno Marilena, 2018). Additionally, the authors used the model (4) to evaluate the impact of the past profits elements to the future profits through testing fixed and random effect model, tested according to the sustainable impact model with data tables Ilia D. Dichev & amp; Vicki Wei Tang (2008). From the results of model (4), the correlation among three factors: the profit elements, current profit and future profit were more comprehensively identified. In addition, this regression also provided a comparison of the possibility of regression in the past with Pers index in the model (1.3), thence helped investors to get the most effective forecasting tool when making decisions. 4. Results and Discussion 4.1. Estimating sustainable earning for listed firms on Vietnam Stock Market The impact of factors estimation on the profitability of enterprises through Fixed effects model and Random effects model showed that all factors affect the fluctuation of the difference of annual profit, in which there are 8 negative impact factors (coefficient with negative sign) and 2 positive impact factors (coefficient with positive sign). This showed that the level of fluctuation of marginal profit depends heavily on the value of SUE in the previous year (the coefficient is 1.2355) and the impact level of the factors which have absolute value less than 1 and range from -0.8347 to 0.1686. Typically, we can see a lot of factors that have a significant impact on the profit volatility level, such as the correlation between the profit fluctuation and the assets turnover in the same period (-0.8347) and the relationship between income of enterprises in the last 2 years (-0.8219). This is not appropiate with the results presented in the study of (Chen, 2004) - most of the factors have a positive impact and with a large coefficient on the marginal profit fluctuation. Table 4: Factors affecting company’spersistence of profit Fixed-Effects Random-Effects Mean Coef. t-stat Mean Coef. t-stat SUEt-2 1.2355 20.90 0.9820 18.81 HERF*SUEt-2 -0.0243 -0.27 -0.0673 -0.83 CI*SUEt-2 -0.4459 -2.48 -0.1985 -1.19 DPM*SUEt-2 -0.1169 -3.29 -0.1881 -5.89 DATO*SUEt-2 -0.8347 -20.88 -0.7945 -22.56 313
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 GA*SUEt-2 -0.0003 -0.04 -0.0102 -1.51 DPMATO*SUEt-2 0.1686 4.14 0.1027 2.77 DATOGA*SUEt-2 -0.4471 -14.34 -0.6399 -22.75 ACCR*SUEt-2 0.0118 1.02 0.0106 0.99 LOSS*SUEt-2 -0.8219 -13.48 -0.5259 -10.59 DECR*SUEt-2 -4.0548 -16.37 0.0098 0.32 Mean R 0.1793 0.1687 Total variables 7,517 7,517 The results of the authors group Sustainability of corporate profits is measured by Pers index. The trend of profit change is reflected in whether the changeable of marginal profit, the fluctuations in margins are positive or negative, and how they affect investors' choices. Pers results showed that the sustainability of the corporate profits on the Vietnam Stock Market was quite diverse and varies widely, from -3,8250 to 2,4368. The average value of Pers by -0.0850 showed the marginal profit fluctuation was not too large. This result is in contrast to the study of (Chen, 2004). In order to have a deeper perspective on sustainable measures across the Vietnam Market, the authors estimated Pers index of businesses by business sectors. Table 5: Estimate Pers by industry Obs Mean n Max Basic materials 1.300 0.4025 -9.7910 7.2106 Consumer goods 1.748 0.0511 -12.9768 352.3363 Customer service 862 -0.0958 -88.8640 18.1120 Health care 431 -1.7140 -250.854 12.2616 Industry 4.633 -0.3715 -2.9395 22.4686 Oil and gas 76 0.3890 -2.0384 6.0680 Technology 268 0.1013 -2.4451 8.3959 Telecommunication 48 1.2317 -7.3439 11.3894 Utilities 756 0.2014 -15.8478 77.1564 The results of the authors group Although the market's Pers index is negative, most industries are positive, except for the health care. Industry includes 579 firms, accounting for 45.3% of the number of Vietnamese listed firms. Therefore, the industry Pers is -0.3715 which has a strong impact on the index, leading to the negative overall market index. In contrast to the basic materials industry, the Pers index is 0.4025. The industry's Perspective is much higher than the market-wide Pers. This result is consistent with previous studies measuring the specific nature of the basic materials industry (Chen, 2004). Consumer goods industry with 209 firms. This day has positive Pers + 0,01111, but quite close to the market value of Pers, expressing a high level of reliability, or in other words, consumer goods has a quite sustainable profitability. In addition, a particular industry of Vietnam's market is oil and gas industry. The industry's Pers index is +0.3890. This value is quite large, reflecting the high level of profitability of the whole industry 314
12. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 compared to the entire Vietnam market. It can be seen that competitive factors and advantages of business conditions also create a certain sustainability of profits of businesses. 4.2. Predict future profits based on the sustainability of profits Model (2) is selected regressively to directly assess the future self-predictability of profit margins and the ability to predict future profits based on the sustainable profitability index calculated from the above, this is the simplest model but can also contain many defects and give inaccurate results. Table 6: Regression model results (2) k=1 k=2 k=3 k=4 k=5 Model (2.1) ak 11.6656 9.2327 11.9370 8.7115 10.6572 t-stats 5,20 3.78 3.14 2.75 2.95 bk -0,1007 -0.0191 -0.1851 0.0596 0.0292 t-stats -9,22 -1.49 -9.11 2.46 1.05 Mean Adj-R 0,3100 0.1046 0.0815 0.0839 0.0825 Model (2.1) ak 14.4126 6.8958 11.8774 5.2741 6.9957 t-stats 6.42 2.81 3.15 3.17 1.94 bk -0.0582 -0.0893 -0.1809 0.0171 -0.0011 t-stats -5.20 -6.32 -8.52 0.69 -0.04 ck -3.3151 -0.7189 1.2004 1.4570 1.2306 t-stats -4.15 -0.17 0.25 0.27 0.20 dk 0.3053 -0.2797 -0.0068 -0.4757 -0.5328 t-stats 15.54 -11.82 -1.05 8.61 1.94 Mean Adj-R 0.4910 0.0633 0.0817 0.0056 0.0708 Total variables 8,761 7,435 6,199 5,093 4,042 The results of the authors group The results of the model (2) as an origin point to evaluate the regression results of the model (3). The results are significant in predicting the profitability of businesses with the base year to year forecast of 1 to 5 years, respectively. The values of autocorrelation coefficients bk (-0.1007, -0.0191, -0.01851, 0.0596, 0.0292 with k equals 1, 2, 3, 4, 5 respectively) show the different from (Chen, 2014). The autocorrelation coefficient b1 = -0.1007, this value is close to the average of Pers variable calculated in the model (1). This suggests that Pers index reflects quite accurately the firm's profitability in the short- term. The sign (-) of the correlation coefficients also shows the concern about future profitability changes when all indications of future decline. This result is supported by previous studies (Chen, 2004), Dichev & Vicki Wei Tang (2008), Frankel & Litov (2008). Results table Model (3) is the result of regressing SUEt+kvariable according to the variables SUEt, Perst and the product of the above two variables to evaluate the ability of Perstvariable in assessing the 315
13. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 sustainability of profit. The coefficient dk is expected to be 1 because then Perst - the variable formed from the SUE self-regression model - will fully reflect the change in SUE's self-regression structure. At the same time, the index bk - coefficient of the Perst variable - is expected to be 0. From the model results we see that with k = 1, the 2 coefficients bk and dk are all near the expected threshold (b1 = -0.0582 and d1 = 0.3053). With k = 2.3 bk falling to a negative level (-0.0893, -0.1809) this indicates a negative sign when both the sustainability of the profit lies at a negative level and may have negative impacts on firm. with k = 4.5, bk gives a better value, this is a very positive thing to show predictability and meet the expectations of the model. The value of dk at k = 2,3,4,5 all returns to the negative level, which is the negative value that is misleading for future earnings forecast of the business. 4.3. Determine the relationship between the volatility of profit and the ability to forecast profits 4.3.1. Profit forecast by quintile Model (2) is a model that evaluates the self-predictability of profit based on the profit of the previous period. The results of the quintile regression are as follows: Table 7: Results for the earnings persistence regression β Quintile Earnings volatility Absolute amount of Earnings level Cash flow volatility accruals Quintile 1 0.8053 06646 0.7216 0.6199 Quintile 2 0.8433 0.6646 0.7140 0.6674 Quintile 3 0.8128 0.6809 0.8241 0.8115 Quintile 4 0.6790 0.6814 0.8316 0.8733 Quintile 5 0.6536 0.7769 0.8313 Difference 0.1517 -0.1123 0.7509 -0.2114 The results of the authors group The result of the regression regression β across the whole market is 0.8241, indicating a negative relationship between the hypothesis of profit volatility and the sustainability of firm profitability. The above results also show that the level of volatility of profits and profits positively affects the sustainability of profits. On the contrary, the accrued profit and the movement of operating cash flow negatively impact the sustainability of these businesses. Also from this result shows that the percentile 1 to the 5-indicator unit of profitability according to the fluctuation of profit and the fluctuation of cash flow of business operations. This result also confirms the contradictory law between maximizing profitability and increasing the solvency of businesses in Ross corporate finance management (2016). Meanwhile, the prediction results based on the units based on profitability are presented in Table 4.16, showing a sustainable index of volatility fluctuations between units and adjusted R2 from 0.2698. in percentile 1 down to 0.4083 in percentile 4 and decrease to 0.3663 in percentile 5. The significance of the independent variable is larger than the running model according to the quintile of the accrued profit. This result is similar to the test result of (Tang, 2008) when showing the ability to predict profitability based on profit level and adjusted R2 to the highest level in the average units Dichev & Vicki Wei Tang (2008), Frankel & Litov (2008). From the forecast of sustainable profits according to the quintile, long-term profit forecasts help to make judgments about the stability of profitability of businesses. Forecasting profits in the long term (5 years) is as follows: 316
14. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 8: Model regression results table (3) β Adj-R Market overall 2.6903 0.3938 Maximum fluctuations 2.2152 0.1221 Minimum fluctuations 3.0434 0.3038 The results of the authors group From the regression results, the indicators reflect quite accurately and in accordance with the theory of economics and statistics. Businesses with large profit fluctuations have a lower level of sustainable profitability than many businesses with stable profits. For the whole market, this index is quite high, close to the group of businesses with the most stable profit and the significance level is also very high (0.3938), which shows that despite the overall profit index is negative. But in the long term, it is still possible to make positive comments and expectations for the profit of the market. 4.3.2. Test the role of components of past sustainable profits in predicting future profits From the regression results, the P-value of all variables is less than 0.05, indicating significant variables affecting the dependent variable NI. The statistic value t must be higher than 1.96, and the variables will satisfy this. Therefore, the variables have a significant influence on the dependent variable, the higher the t-statistic value shows, the greater the correlation between that variable and the dependent variable (NI). Realizing that t_FCF = 34.1 is higher than t_ (CACC) = 22.56 and t_NCACC = 5.61, so the effect of free cash flow is higher than short-term accrued profit from business activities in a suitable future profit forecast. with the Examining the Earnings Persistence and Its Components results in Explaining the Future Profitability (Mahmoud Moienadin, Zohre Tabatabaenasab, 2014) Armita Atashband (2014). Table 9: Regression results model (4) Variable Mean Coef. Stt. Err. t-stat CACC 0.0842 0.0037 22.56 FCF 0.1748 0.0051 34.10 NCACC 0.0269 0.0048 5.61 Cons 77.1401 2.2542 34.22 Adj-R: 0.2906 Obs: 10,159 The results of the authors group 5. Conclusion Through the analysis of the factors affecting sustainable profitability, the study examinedthe impact of these factors on the sustainability of the enterprises' profits, thereby giving the formula of calculation. This sustainable indicator and the method of predicting future profits for businesses. The study provides a measure of the Pers index - an indicator of profitability (Chen, 2004), (Narayanamoorthy, 2006), (Richard Frankel, Lubomir Litov, 2009), (Srinivasan Rangan, Richard G. Sloan, 1998), (Stephen H. Penman, Xiao-Jun Zhang, 2004). After verification, the result of Vietnam market Perspectives index is -0.0850, showing that the profits of enterprises in Vietnam market are not sustainable. This result shows the differentiation of enterprises in the industry, with large gaps and uneven changes. In some sectors, large-scale enterprises have stable profits and growth rates. On the contrary, there are small-scale businesses and production and business capabilities, business activities are not 317
15. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 focused, profits and profit growth are low, even at a loss. Accordingly, these businesses face an imbalance in profit, affecting the entire industry and the whole market(See Appendix). The results of the study also show Herfindahl index on the concentration of the industry (Lev, 1983), (Chen, 2004), (Alan Gregory, Julie Whittaker, Xiaojuan Yan, 2016), (Richard Frankel, Lubomir Litov, 2009), correlation between asset turnover and changes in income with asset growth during the period (Chen, 2004), (Stephen H. Penman, Xiao-Jun Zhang, 2004), correlation between accrued profits and Revenue (Chen, 2004), (Narayanamoorthy, 2006), (Richard Frankel, Lubomir Litov, 2009), (Srinivasan Rangan, Richard G. Sloan, 1998) have a negative impact on previous studies at some developed market in the world. This is a particular factor of the market. In Vietnam's growing market, businesses have not really positioned their business and mainly classified relative industries based on the weight of revenue in the total revenue of the business. Therefore, create a certain dispersion on the concentration of careers. In addition, it can be seen that the number of enterprises in the industry is not divided according to the rules. In the same industry, there are very large enterprises with large scale and concentration, but there are also small-scale businesses with low concentration. In the industry, this is the industry with the proportion of enterprises accounting for half of the total number of businesses operating in Vietnam and there is an increasing prospect. However, the ability of these enterprises to operate is quite large. Some businesses are very developed with a large market share in the industry, Herfindahl index is high with high concentration of industries and vice versa, some businesses are small, business activities are not concentrated. and market share in the industry only accounts for a small, even very small share. This is also one of the reasons for the profitability of this industry to reach very low. REFERENCE [1] Abarbanell, J. S. (1991). Do analysts’ earnings forecasts incorporate information in prior stock price changes? Journal of Accounting and Economics, 14(2), 147-165. [2] Alan Gregory, Julie Whittaker, Xiaojuan Yan. (2016). Corporate social performance, competitive advantage, earnings persistence and firm value. Journal of Business Finance & Accounting, 3-30. [3] Amy P. Hutton, Gregory S. Miller, Douglas J. Skinner. (2003). The Role of Supplementary Statements with Management Earnings Forecasts. Journal of Accounting Research, 41(5), 867-890. [4] Amy P. Hutton, Phillip C. Stocken. (2009). Prior Forecasting Accuracy and Investor Reaction to Management Earnings Forecasts. papers.ssrn.com. [5] Basu, S. (1977). Investment Performance of Common Stocks in Relation to their Price-Earnings Ratios: A test of the efficient market hypothesis. The Journal of Finance, 663-682. [6] Bựi Kim Phương, Nguyễn Thị Ngọc Trang. (2018). “Cỏc yếu tố ảnh hưởng đến chất lượng lợi nhuận của cỏc cụng ty niờm yết tại Việt Nam”. Tạp chớ Nghiờn cứu Kinh tế và Kinh doanh chõu Á, 05-20. [7] Chen, C. (2004). Earnings Persistence and Stock Price Under- and Overreaction. Madison : Documento de trabajo - University of Wisconsin-Madison. [8] Eli Amir, Eti Einhorn, Itay Kama. (2012). Extracting Sustainable Earnings from Profit Margins. European Accounting Review, 22(4), 685-718. [9] Furno Marilena, V. D. (2018). Quantile Regression: Estimation and Simulation. John Wiley & Sons, 50-62. [10] Gregory D. Lyimo, Girish Jain. (2014). Predictability Persistence of Earnings and stock price synchronicity evidence from Indian stock market. International Journal of Economics, Commerce and Management, 2(6), 1-16. [11] Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica 46 (6), 1251–1271. 318
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