Prediction of cash flows from operating among non-financial listed companies in Viet Nam

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Research Objectives: Review and summarize systematically theories on features of cash accounting based information and accrual accounting based information; examine the predictive abilities of future operating cash flow by using earnings.

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Nội dung Text: Prediction of cash flows from operating among non-financial listed companies in Viet Nam

INTRODUCTION In terms of reality aspect, for many investors, bankers, finance 1. Imperativeness of the study managers, … predicting cash flows take an extreme important part before Cash flows always take an important role in companies like blood in making their economic decision. 80% creditors in US stated that: in loan human body. Therefore, predicting cash flows will help investors, managers proposal, future cash flow plan is crucial document (Do Thi Hong Nhung, in evaluating the performance of companies and make economic decisions. 2014; Fulmer, Gavin & Bertin, 1991; Waddell D. & cộng sự, 1994). Research on prediction of cash flows in order to identify elements have Therefore, researches which identify those indications having abilities in abilities to predict future cash flows is essential in both theory and in reality. predicting future cash flows are helpful for many users especially for users in In terms of theory aspect, preditions of cash flows are mentioned in Viet Nam where stock market has just operated for 15 years, investigation National Accounting Standards and some researches (Do Thi Hong Nhung, results show that: cash flows predictions have not strictly implemented and 2014; Nguyen Huu Anh, 2010; Al –Attar, 2003; Barth & cộng sự, 2001; those elements that effects cash flows have not been considered carefully; Chotkunakitti, 2005; Ebaid, 2011; Farshadfar & Partners, 2008; Mooi, T.L, predicting qualities mostly depends on chief accountant’s experiences; 2007). However, National Accounting Standards only provides predictive methods for predicting cash flows is simple and subjective (Do Thi Hong abilities in accounting standards and lack of specific evidences; and the Nhung, 2014). researches still not unify the ability of predictive variables. Vietnamese For those reasons, research on predicting cash flows is very essential to accounting standard No.24 (Ministry of Finance, 2003) said that: operating decision making process, especially prior studies were mainly conducted in cash flows information, once used jointly with other information, will help countries with developed capital market for decades but have not been users to predict future operating cash flows. US Statement of financial studied comprehensively and systematically in Viet Nam. accounting concepts No.1 (FASB, 1978) stated: “users interested in future Among companies operating in Viet Nam, non-financial listed companies cash flows and its abilities to generate favorable cash flows usually pay have known as big size, manufacture and operate in vital industries and attention in information about enterprise’s earnings rather than information contribute large amount to GDP. Beside, cash flows from operating activities directly about cash flows” and the information about earnings and its are generate main revenues for enterprises (Ministry of Finance, 2002) and be components therefore is generally more predictive of future cash flows than considered as signal of enterprise’s abilities to earn enough cash in order to current cash flows” (Barth et.al, 2001). Therefore, there are increasing meet daily’s performances (Boyd & Cortese – Daniel, 2000/2001). demands of cash flows forecast research to provide empirical evidences for Therefore, the researcher has choosen the research of “Prediction of cash national accounting standard’s assertion and literature on cash flows flows from operating among non-financial listed companies in Viet Nam”. predictions researches. 2. Research Objectives and Research Questions Research Objectives: 1 2
o Review and summarize systematically theories on features of cash • Question 4: Do historical operating cash flows combined with accounting based information and accrual accounting based information. disaggregated accruals of earnings have the significant ability to predict o Examine the predictive abilities of future operating cash flow by using the future operating cash flows of non-financial listed companies in earnings. HOSE? o Examine the predictive abilities of future operating cash flow by using • Question 5: Do historical cash flow ratios have the significant ability to operating cash flows. predict the future operating cash flows of non-financial listed companies o Examine the predictive abilities of future operating cash flow by using in HOSE? operating cash flows combined with accrual components. • Question 6: Which of the above models has the highest ability in o Examine the predictive abilities of future operating cash flow by using predicting the future operating cash flows of non-financial listed operating cash flows combined with disaggregated accrual components. companies in HOSE? o Examine the predictive abilities of future operating cash flow by using 3. Object and scope of the research cash flows ratio. Object of the research: Net Cash flows from operating activities of o Identify the superior model to predict future cash flows. non-financial listed companies in HOSE. Research Questions Scope of the research: 142 non-financial companies listed in HOSE + General research question: “Which financial elements of reported within the period of 6 years (from 2009 to 2014) accounting information can be used to predict the future operating cash 4. New contributions of the research flows of non-financial listed companies in Ho Chi Minh Stock Exchange?” In terms of academic and theoretical aspect: + Detailed questions Empirical evidences from this study support for Vietnamese • Question 1: Do historical earnings have the significant ability to predict Accounting Standard No.24 (VAS 24) on kinds of information should be the future operating cash flows of non-financial listed companies in used with cash flow information to enhance predictive abilities of future HOSE? operating cash flows. Those important information based on accrual • Question 2: Do historical operating cash flows have the significant accounting basis included: depreciation expenses, changes in account ability to predict the future operating cash flows of non-financial listed receivables, changes in account payables and changes in inventories. companies in HOSE? In terms of practice aspect: • Question 3: Do historical operating cash flows combined with In Viet Nam, predictions of cash flows from operating mostly based on aggregated accruals of earnings have the significant ability to predict the production & sale plan or percentage of changes in accounting items future operating cash flows of non-financial listed companies in HOSE? compared to revenue. Those forcasting methods depend on internal 3 4
documents and/or bias by forcasters’ view. In order to help outsiders who CHAPTER 2: THEORICAL FRAME WORK AND LITERATURE find hard to reach internal information can make exact predictions, this thesis REVIEW construct and investigate cash flow prediction models such as: earnings 2.1 Frame work on predictions of cash flows from operating in security models, cash flow models, aggregated accrual components models, market disaggregated accrual components model, cash flow ratios models by using 2.1.1 Feature of accounting cash-based information and accounting accrual- financial information extracted from financial statements of listed companies based information on HOSE. Regressions analysis like OLS (Ordinary Least Squared), REM Cash-based information may be more objective and understandable than (Random Effects Models), FEM (Fixed Effects Models) were applied for accrual-based information. However, the Cash-based information may be less above prediction models and FEM (Fixed Effects Models) are chosen as the comprehensive than accrual-based information. Cash-based information most suitable models to predict operating cash flows. Results from FEM cannot be replaced for accrual-based information (for example: earnings reveal that above models are significant in predicting future operationg cash information). Combination of cash-based information and accrual-based flows but have different predictive powers (adjusted R-squared value of information are potentially predictive indicators for future cash flows models range from 51% to 93%) with operating cash flows combined with forecast. disaggregated accrual components providing a superior comparative 2.1.2 Relation between cash flows and financial status of listed companies predictive ability on future cash flow. Therefore, potential investors and Cash flows always take an important role in companies like blood in internal executive managers can use these prediction models to forcast human body. Cash flow provides an essential indicator on the firm’s ability operating cash flows for each company before making economic decisions. to repay liabilities and directly linked to dividends policy. Besides, cash 5. Structure of the Research flows ratios are used as measurement for effectiveness of firm performance Chapter 1: Introduction to the Research and indicators for bankruptcy predictions. Chapter 2: Theoretical Frame Work of cash flow and predictions of cash 2.1.3 Measurement of net cash flows from operating activities for listed flow and Literature Reviews on predictions of cash flow in listed companies companies. - Estimating net cash flows from operating activities (operating cash Chapter3: Hypothesis Construction and Research Methodology flows): before issuing International Accounting Standard No.7 Chapter 4: Data Analysis of Predictions of cash flows from operating of - Reporting cash flows from operating activities according to National non – financial listed companies in Vietnamese securities market. Standard and International Standard. Chapter 5: Discussion on Research results, Conclusions and Implications 2.1.4 Conducting predictions of operating cash flows for listed companies CHAPTER 1: INTRODUCTION TO THE RESEARCH Role of predictions of cash flows 5 6
It is accepted that: one can make predictions without making decisions - Thirdly, prior studies were mainly conducted in countries with developed but should not make decisions without making predictions (Beaver, 1966). capital market. Only few studies were conducted in developing countries and Prediction of future cash flows is highly effective in making decision for loan emerging market. and investment, estimating dividends, or company’s shares value. - Finally, majority of prior researches adopted the Ordinary Least – Squared Factors affecting future cash flows prediction: technique (OLS) in order to process and analyze data. Only few studies There are inside factors and outside factors as well as objective and adopted Random Effects Models (REM), Fixed Effects Models (FEM) or subjective causes, such as: investor’s interests, age of firm, size of firm, step – wise models. operating income results, voluntary attitude of managers, prediction 2.4 Research Gaps techniques, competence of forecasters, input information for forecast, - Need of conducting research on the extent accounting and operating cash national regulations. flows information are able to predict future operating cash flows of listed 2.2 Over view of research on prediction of cash flows from operating companies in Viet Nam. - Predictive indicators of cash flows forecast: predictive indicators are - Cash flow ratios should be considered as predictive indicators in cash flows divided into 5 groups: historical earnings, historical operating cash flows, prediction models. historical operating cash flows combined with aggregated accrual - Modern prediction techniques (REM, FEM, step-wise) suitable for components, historical operating cash flows combined with disaggregated predictions of cash flows should be applied. accrual components, cash flows ratios. - Identify financial information to be used with cash flows information that - Predictive method of cash flow forecast: Ordinary Least Square (OLS) enhance the predictive abilities of cash flows forecast in order to provide - Research context and scope: Mainly in developed countries, rarely in direct empirical evidences for assessment in Vietnamese Accounting developing countries. Standard No. 24. 2.3 Assessments of research on predictions of operating cash flows for - Scope of research in term of research time should be expanded (time of data listed companies in sample after the year of 2010) - Firstly, since prior studies were mainly conducted in specific time, and CHAPTER 3: HYPOTHESIS CONSTRUCTION AND RESEARCH economic context, the conclusions about the superior predictive variables still METHODOLOGY inconsistent. 3.1 Construction of Hypothesis - Secondly, scopes of prior researches are mainly covered non-financial listed o Hypothesis 1: Historical earnings have the significant ability to predict firms and therefore not categorized into specific industry groups. the future operating cash flows of non-financial companies listed in HOSE. 7 8
o Hypothesis: Historical operating cash flows have the significant ability - Thirdly, compare Adjusted R2 among models and use Hausman test for to predict the future operating cash flows of non-financial companies listed in choosing appropriate model. HOSE. Prediction models in the research as followings o Hypothesis 3: Historical operating cash flows combined with 3.2.2.1 Prediction of cash flows from operating using historical earnings aggregated accruals have the significant ability to predict the future operating (Earnings Model) cash flows of non-financial companies listed in HOSE. CFOt = β0 + β1 EARNt-1 + ε (3.1) o Hypothesis 4: Historical operating cash flows combined with CFOt = β0 + β1EARNt-1 + β2EARNt-2 + ε (3.2) disaggregated accruals have the significant ability to predict the future CFOt = β0 + β1EARNt-1 + β2EARNt-2 + β3EARNt-3 + ε (3.3) operating cash flows of non-financial companies listed in HOSE. 3.2.2.2 Prediction of cash flows from operating using historical Operating o Hypothesis 5: Historical cash flow ratios have the significant ability to Cash Flows (Operating Cash Flows Model) predict the future operating cash flows of non-financial companies listed in CFOt = α0 + α1 CFOt-1 + µ (3.4) HOSE. CFOt = α0 + α1 CFOt-1 + α2 CFOt-2 + µ (3.5) o Hypothesis 6: Historical operating cash flows combined with CFOt = α0 + α1CFOt-1 + α2 CFOt-2 + α3 CFOt-3 + µ (3.6) disaggregated accruals components provide a superior comparative 3.2.2.3 Prediction of cash flows from operating using historical Operating predictive ability on future cash flow of non-financial companies listed in cash flows combined with aggregated accruals HOSE CFOt = λ0 + λ1 CFOt-1 + λ2 ACRt-1 + ε (3.7) 3.2 Research Methodology CFOt = λ0 + λ1 CFOt-1 + λ2 CFOt-2+ λ3 ACRt-1 + λ4 ACRt-2+ ε (3.8) 3.2.1 Data for research CFOt = λ0 + λ1 CFOt-1 + λ2 CFOt-2+ λ3 CFOt-3+ λ4 ACRt-1 + λ5 ACRt-2+ λ6 This research extracted information reported on Financial Statements of ACRt-3 + ε (3.9) 142 non – financial companies listed in HOSE for 6 years (from 2009 to 3.2.2.4 Prediction of cash flows from operating using historical operating 2014). Data is provided by Stox Plus Company. cash flows combined with disaggregated accruals 3.2.2 Prediction models and prediction methods CFOt = e 0 + e1 CFO t-1 + e2 ∆ARt-1 + e3∆AP t-1 + e4∆INV t-1 + e5 ∆OTH t-1 + - Firstly, predict models by using regression OLS, FEM, REM and step- e6DPRM t-1 + ρ (3.10) wise. CFOt = e 0 + e1 CFOt-1 + e2 CFOt-2 + e3 ∆ARt-1 + e4 ∆AR t-2 + e5∆AP t-1+ - Secondly, assess the concordance between models and according to e6∆APt-2 e7∆INVt-1 + e8∆INV t-2 + e9∆OTH t-1 + e10 ∆OTHt-2 + e11DPRM t-1 + regression assumptions. e12DPRM t-2 + ρ (3.11) 9 10
CFOt = e 0 + e1CFOt-1 + e2CFOt-2 + e3 CFO t-3 + e4∆AR t-1 + e5 ∆ARt-2 +e6 EARNt-2 0.00000 ∆AR t-3 + e7∆AP t-1 + e8∆AP t-2 + e9∆AP t-3 + e10∆INV t-1 + e11∆INV t-2 + EARNt-1 e12∆INVt-3 + e13 DPRM t-1 + e14DPRM t-2 + e15DPRM t-3 +e16∆OTH t-1 + e17 4.1.3 EARNt-2 0.00000 0.813363 ∆OTH t-2 + e18 ∆OTH t-3 +ρ (3.12) EARNt-3 3.2.2.5 Prediction of cash flows from operating using historical cash flows Table 4.4b: Regression results of earnings models (FEM) (detailed ratios (Cash flows ratios models) information) CFOt = β0 + βi CFR1t-1 + βi CFR2t-1 + βi CFR3t-1 + βi CFR4t-1 + βi CFR5t-1 + FEM Variables Coefficients Prob. βi CFR6t-1 + βi CFR7t-1 + βi CFR8t-1 + βi CFR9t-1 + ε (3.13) 4.1.1 EARNt-1 0.708268*** 0.000 CFOt = β0 + β1 CFR1t-1 + β2 CFR2t-1 + β3 CFR3t-1 + β4 CFR4t-1 + β5CFR5t-1 + EARNt-1 1.037564*** 0.000 β6 CFR6t-1 + β7 CFR7t-1 + β8 CFR8t-1 + β9 CFR9t-1 + β10CFR1t-2 + β11 CFR2t-2 + 4.1.2 EARNt-2 -0.832427*** 0.000 β12 CFR3t-2 + β13 CFR4t-2 + β14 CFR5t-2 + β15 CFR6t-2 + β16 CFR7t-2 + β17 *** EARNt-1 0.841508 0.000 CFR8t-2+ β18 CFR9t-2 + ε (3.14) 4.1.3 EARNt-2 -1.072893*** 0.000 CHAPTER 4: DATA ANALYSIS: PREDICTIONS OF CASH FLOWS EARNt-3 0.699031*** 0.000 FROM OPERATING OF NON – FINANCIAL LISTED COMPANIES 4.4.2 Regression results of operating cash flows models 4.1 Characteristic of companies listed on Viet Nam Securities Market Table 4.7a: Regression results of cash flows from operating model (FEM) 4.2 Descriptive Statistics Analysis (general information) 4.3 Correlation Evaluation Variabl Prob (F- Durbin - FEM Adjusted R2 4.4 Empirical results of predictions of cash flows from operating of non- es Statistic) Watson financial companies listed on HOSE 4.2.1 CFOt-1 0.0000 0.764312 2.613703 4.4.1 Regression results of earnings models CFOt-1 Table 4.4a: Regression results of earnings models (FEM) (general 4.2.2 0.00000 0.789254 2.695733 CFOt-2 information) CFOt-1 Prob (F- 4.2.3 CFOt-2 0.00000 0.796054 2.648768 FEM Variables Adjusted CFOt-3 Statistic) R2 Table 4.7b: Regression results of cash flows from operating models (FEM) 4.1.1 EARNt-1 0.00000 0.773219 (detailed information) 4.1.2 EARNt-1 0.792889 11 12
FEM Variables Coefficients Prob. CFOt-3 0.763306*** 0.000 4.2.1 CFOt-1 -0.497463*** 0.000 ACRt-1 1.001574*** 0.000 CFOt-1 -0.493368*** 0.000 ACRt-2 -0.16145** 0.0762 4.2.2 *** *** CFOt-2 0.411615 0.000 ACRt-3 1.106636 0.000 CFOt-1 -0.515405*** 0.000 4.2.3 CFOt-2 0.337359*** 0.000 4.4.4 Regression results of Operating cash flows combined with CFOt-3 0.260078*** 0.000 disaggregated accruals Models 4.4.3 Regression results of Operating cash flows combined with aggregated Table 4.13a: Regression results of operating cash flows combined with accruals Models disaggregated accruals model (FEM) (general information) Table 4.10a: Regression results of operating cash flows combined with FEM Adjusted R - Durbin - Prob (F- Statistic) aggregated accruals model (FEM) (general information) squared Watson FEM Prob (F- Adjusted Durbin - 4.4.1 0.000 0.883925 2.484538 Statistic) R2 Watson 4.4.2 0.000 0.922246 2.548625 4.3.1 0.000 0.828346 2.418038 4.4.3 0.000 0.93063 2.501873 4. 3.2 0.000 0.828309 2.434564 Table 4.13b: Regression results of operating cash flows combined with 4.3.3 0.000 0.875557 2.737023 disaggregated accruals model (FEM) (detailed information) Table 4.10b: Regression results of operating cash flows combined with FEM Variables Coefficients Prob. aggregated accruals models (FEM) (detailed information) CFOt-1 0.06368 0.4051 Model Variables Coefficients Prob. ∆APt-1 -0.724623*** 0.0000 ** *** CFOt-1 0.187462 0.005 ∆AR t-1 0.877728 0.0000 4.3.1 *** 4.4.1 *** ACRt-1 0.903857 0.000 ∆INV t-1 0.969675 0.0000 CFOt-1 0.192402** 0.0193 ∆OTH t-1 -0.449346 0.3221 *** CFOt-2 -0.029462 0.7628 DPRM t-1 2.492599 0.0000 4.3.2 ACRt-1 0.879525*** 0.000 CFOt-1 0.067608 0.3367 ACRt-2 -0.095209 0.3141 CFOt-2 -0.278544*** 0.0000 4.4.2 *** CFOt-1 0.080905 0.2753 ∆APt-1 -0.763389 0.0000 4.3.3 CFOt-2 -0.415047*** 0.000 ∆APt-2 0.079242 0.2840 13 14
∆AR t-1 0.751735*** 0.0000 Table 4.16a: Regression results of cash flows ratios models (FEM) (general ∆AR t-2 -0.190873*** 0.0081 information) ∆INV t-1 0.856248*** 0.0000 FEM Prob (F- Adjusted ∆INV t-2 0.581657 *** 0.0000 Statistic) R2 ∆OTH t-1 -0.926885** 0.0224 4.5.1 0.000000 0.333233 ∆OTH t-2 0.440919 0.2708 4.5.2 0.000000 0.518494 *** DPRM t-1 1.552724 0.0000 Table 4.16b: Regression results of cash flows ratios model (FEM) (detailed DPRM t-2 1.097229*** 0.0004 information) CFOt-1 -0.105004 0.1356 FEM Variables Coefficients Prob. *** CFOt-2 -0.206533 0.0019 CFR1 t-1 0.000343 0.7689 CFOt-3 -0.106958 0.1165 CFR2 t-1 -0.000016 0.7200 ∆APt-1 -0.706308*** 0.0000 CFR3 t-1 -0.000389 0.6077 ∆APt-2 -0.113543 0.1587 CFR4t-1 0.000601* 0.0970 * ∆APt-3 0.018948 0.8493 4.5.1 CFR5 t-1 -0.000150 0.0705 * ∆ARt-1 0.803101*** 0.0000 CFR6t-1 0.002356 0.0113 ∆ARt-2 0.095524 0.2806 CFR7 t-1 -0.000567 0.7739 ∆ARt-3 0.244711 0.0312 CFR8 t-1 -0.000072 0.6112 4.4.3 *** * ∆INVt-1 0.871865 0.0000 CFR9 t-1 -0.039756 0.0693 ∆INVt-2 0.889791*** 0.0000 CFR1 t-1 0.000225 0.8485 ** ∆INVt-3 0.474898*** 0.0000 CFR2 t-1 -0.000098 0.0429 ** ∆OTHt-1 -1.148572 0.0093 CFR3 t-1 0.000180 0.8599 ∆OTHt-2 0.353376 0.4140 CFR4t-1 0.001132*** 0.0008 *** ∆OTHt-3 -0.32586 0.4645 4.5.2 CFR5 t-1 -0.000414 0.0000 *** DPRMt-1 0.321975 0.3889 CFR6t-1 0.005206 0.0003 DPRMt-2 0.780995** 0.0189 CFR7 t-1 -0.000538 0.7727 DPRMt-3 1.647527*** 0.0001 CFR8 t-1 -0.000023 0.8861 ** 4.4.5 Regression results of cash flows ratios models CFR9 t-1 -0.055162 0.0120 15 16
CFR1 t-2 0.000223 0.8860 5.1.1 Testing research hypothesis CFR2 t-2 0.000060 0.2665 Earnings Models CFR3 t-2 0.001788 0.1820 According to FEM regression results of 3 detailed earnings models (1 year, *** CFR4t-2 -0.008647 0.0000 2 years and 3 years lags), the three models comprising of past earnings were CFR5 t-2 -0.000131 0.2567 highly significant in predicting future operating cash flows. The more CFR6t-2 -0.005951** 0.0386 inclusion of year-lag to the model, the more prediction powers the model can CFR7 t-2 -0.001585 0.6386 achieve (R2 increase from 77% to 81%). These findings are consistent with CFR8 t-2 -0.000340 0.2510 prior studies, including Nguyen Huu Anh (2013), Barth, et al. (2001), CFR9 t-2 -0.053952** 0.0382 Chotkunakitti (2005) and Habib (2010). Therefore, Hypothesis 1 is accepted. Step-wise regression results of cash flows ratios models Operatings cash flow Models Table 4.18: Regression results using step wise regression for cash flow ratios According to FEM regression, the study concludes that: the three models models comprising of past operating cash flows predictors were highly significant in Stepwise Variabes R2 DW Coefficients Prob. predicting future operating cash flows. The more inclusion of year-lag to the CFR9 t-1 0.0445120** 0.0271 model, the more prediction powers the model can achieve (R2 increase from 4.5.1 CFR7 t-1 0.007806 1.743085 0.0021880 0.2307 76% to 79%). These findings are consistent with prior studies, including CFR8 t-1 0.0000946 0.4411 Greenberg, et al. (1986), Bowen et al. (1986), Nguyen Huu Anh (2013), CFR9 t-2 0.059124** 0.0104 Barth, et al. (2001), Chotkunakitti (2005) and Habib (2010). Therefore, Hypothesis 2 is accepted. CFR9 t-1 0.050371** 0.0192 Operating cash flows combined with aggregated accruals Models CFR4 t-2 -0.003586*** 0.0000 According to FEM regression, the study concludes that: the three CFR6 t-2 -0.006108*** 0.0000 4.5.2 0.068736 1.579891 prediction models comprising of past operating cash flows and aggregated CFR4 t-1 0.001174*** 0.0021 accruals predictors were all highly significant in predicting future operating CFR6 t-1 0.003782*** 0.0073 cash flows. The more inclusion of year-lag to the model, the more prediction CFR5 t-1 -0.000139* 0.0603 powers the model can achieve (R2 increase from 82% to 87%). These CFR7 t-1 0.002128 0.2596 findings are consistent with prior studies, including Barth, et al. (2001), CHAPTER 5: Discussion on Research results, Conclusions and Chotkunakitti (2005) and Ebaid (2011), Chong, K.W (2012). Therefore, Implications Hypothesis 3 is accepted. 5.1 Discussion on Research results of cash flows forecast models 17 18
Operating cash flows combined with disaggregated accruals The model comprising of both operating cash flows and disaggregated Models accrual components have highest adjusted R2 value (from 88% to 93%) and According to FEM regression, the study concludes that: the three have been the most effective model prediction models comprising of past operating cash flows and disaggregated The operating cash flows combined with aggregated accruals model has accruals predictors were all highly significant in predicting future operating higher predictive power than the 3 other models in this study. cash flows. The more inclusion of year-lag to the model, the more prediction The earnings models have stronger predictive power than operating cash powers the model can achieve (R2 increase from 88% to 93%). These flows models. findings are consistent with prior studies, including Barth, et al. (2001), The cash flows ratios models have the least predictive ability on future Chotkunakitti (2005) and Ebaid (2011), Chong, K.W (2012). Therefore, earnings cash flows. The adjusted R2 value of two years – lag cash flow Hypothesis 4 is accepted. ratios model is 51% - lowest in compare to other models introduced in this Cash flows ratios Models study. According to FEM regression, the study concludes that both one year - lag 5.1.3 Prediction equations and two years – lag cash flows ratio models have predictive abilities. Equations for prediction for operating cash flows based on earnings However, the prediction power of this model is much lower in compare to the CFOt = 96.900.000.000 + 1,037564 EARNt-1 - 0,832427 EARNt-2 (5.2) 2 prediction models above (maximum R 51.8% with 2 years-lags model) CFOt =79.700.000.000 + 0,841508 EARNt-1 – 1,072893 EARNt-2 + 0,699031 According to step – wise regression, both one year - lag and two years – lag EARNt-2 (5.3) cash flows ratio models have predictive abilities. There are only four cash Equations for prediction for operating cash flows based on historical flows ratios (CFR 4, CFR 5, CFR6, CFR 9) among 9 cash flow ratios used as operating cash flows predictive indicators have predictive abilities on operating future cash flows CFOt = 191.000.000.000 - 0,497463 CFOt-1 (5.4) of the listed firms. Therefore, Hypothesis 5 is accepted. CFOt = 160.000.000.000 - 0,493368 CFOt-1 + 0,411615 CFOt-2 (5.5) 5.1.2 Assessment predictive abilities of models CFOt = 152.000.000.000 - 0,515405 CFOt-1 + 0,337359 CFOt-2 + 0,260078 2 2 Table 5.10b summarize R value of predictions models (sorting adjusted R CFOt-3 (5.6) value from top to bottom, the study concludes that: Equations for prediction for operating cash flows based on historical The more year – lag used in the models, the more predictive abilities the operating cash flows combined with aggregated accruals models enhanced. CFOt = 41.600.000.000 + 0,187462CFOt-1 + 0,903857 ACRt-1 (5.7) CFOt = 55.200.000.000 + 0,192402 CFOt-1 + 0,879525 ACRt-1 - 0,029462 CFOt-2 - 0,095209 ACRt-2 (5.8) 19 20
CFOt = - 42600.000.000+ 0,080905 CFOt-1 + 1,001574 ACRt-1 – 0,415047 managements and creative accounting; Lastly, Ministry of Finance should CFOt-2 – 0,16145 ACRt-2 + 0,763306 CFOt-3 + 1,106636 ACRt-3 (5.9) push companies listed to declare cash flows forecast voluntarily. Equations for prediction for operating cash flows based on historical 5.2.2 Implications for companies operating cash flows combined with disaggregated accruals Companies should apply method introduced in this study to predict CFOt = - 49.300.000.000+0,06368CFOt-1 - 0,724623∆APt-1 + 0,877728 ∆ARt- cash flows from operating besides traditional methods (production & sale 1 + 0,969675 ∆INVt-1 + 2,492599 DPRN t-1 (5.10) plan and percentages of revenue). More over, executive managers should CFOt = - 44.700.000.000+0,067608 CFOt-1 - 0,763389 ∆APt-1 + 0,751735 support for cash flows forcasts, believe in results of cash forcast and set ∆ARt-1 + 0,856248 ∆INVt-1 – 0,926885 ∆OTH t-1 + 1,552724 DPRN t-1 - positive working conditions in enterprise. 0,278544 CFOt-2– 0,190873 ∆ARt-2 + 0,581657 ∆INVt-2 + 1,097229 DPRN t-2 5.2.3 Implications for investors (5.11) Investors can make cash flows forecasts based on reported information CFOt = - 68.100.000.000 - 0,105004 CFOt-1 - 0,706308 ∆APt-1 + 0,803101 in financial statements rather than internal documents, may not be available ∆ARt-1 + 0,871865 ∆INVt-1 – 1,148572 ∆OTH t-1 – 0,206533 CFO t-2 + for external use. The most effective predictive power model is the operating 0,889791 ∆INVt-2 + 0,780995 DPRN t-2 – 0,106958 CFO t-3 + 0,474898 cash flows combined with disaggregated accruals. Predictive abilities of ∆INVt-3 + 1,647527 DPRN t-3 (5.12) those models increased from 84% to 93% by increasing one year-lag to three Equations for prediction operating cash flows based on cash flows years-lag variables in the model. In addition, investors should pay more ratios using step-wise regression model attentions on cash flows ratio such as CFR4 - reinvestment ratio (cash flows CFOt = 0.044512 CFR9 t-1 (5.13) from operating/Payment for property, plant & equipment), CFR 5 - Debt CFOt = 0,001174 CFR4t-1 - 0.000139 CFR5t-1 + 0,003782 CFR6t-1 + coverage ratio (Total debt/ Operating cash flows, CFR 9 – Operating cash 0.002128 CFR7t-1 + 0,050371 CFR9t-1 – 0,003586 CFR4t-2 - 0,006108 CFR6t- flows return on assets ratio (Cash Flows/Average total assets), CFR 6 2+ 0,059124 FR9t-1 (5.14) Depriciation/ Operating cash flows. By using those ratios, investors can 5.2 Implications realize that they may interpret for more than 50% of changes in future 5.2.1 Implication for Ministry of Finance operating cash flows. Firstly, Ministry of Finance should require enterprise to provide accrual 5.3 Limitations components information in order to help users predict cash flows; Secondly, The scope of this study was restricted to non – financial companies listed Ministry of Finance should add cash flows ratio in “Notes of financial in HOSE rather than in HNX. Besides, this study still has not categorized statements”; Thirdly, Ministry of Finance should investigate earnings companies into specific industry groups. 21 22
LIST OF PH.D ATTENDANT’S ARTICLES CONCLUSION 1. Nguyen Thanh Hieu (2012), “Application of fair value in balance Cash flows are crucial economic sources for every firm. In most financial sheet”, Banking review, No.4, February, 2012. decision and finance management, cash flows take an important role. Despite 2. Nguyen Huu Anh & Nguyen Thanh Hieu (2012), “Accounting for numerous researches in this area on the comparative abilities of variables in prefered stocks in VietNamese firms and financial entities”, Journal predicting future operating cash flows, the results are contradictory and of Economics & Development, No. 172. January, 2012. inconclusive. 3. Nguyen Huu Anh & Nguyen Thanh Hieu (2012), “Accounting for This study firstly emphasize on theoretical frame work of accounting intangible fixed assets in VietNames firms: Fact & Solutions”, basis, cash basis and predictions of cash flows from operating and then using Journal of Economics & Development, Special Issues, December, accounting information from published financial statements of non-financial 2012. companies listed in HOSE to construct cash flows predictions models. The 4. Nguyen Thi Lan Anh & Nguyen Thanh Hieu (2013), “Influence of study used regressions techniques including: OLS, FEM, REM and step-wise auditing firm size on auditing service quality”, Journal of for all prediction models. The findings from this study conclude that the Economics & Development, No. 196 (II), December, 2013. model of operating cash flows combined with disaggregated accrual 5. Nguyen Thanh Hieu & Ta Thu Trang (2013), “Importance of Cash components have the most effective prediction abilities. The study also Flows information”, Journal of Economics & Development, No. 194 provide empirical evidences supported for Vietnamese Accounting Setters’ (II), August, 2013, pp 40-45. statement in VAS 24 “Cash flows statements” that: operating cash flows 6. Nguyen Huu Anh & Nguyen Thanh Hieu (2014), “Predictions of information should use jointly with other information (accrual information) operating cash flows in Viet Nam: Fact & Solutions”, Journal of to help users predict cash flows. The research results may enrich the Economics & Development, No. 205 (II), July, 2014. understandings of finance analyst, lenders, Government and other users about 7. Do Thi Huong Thanh & Nguyen Thanh Hieu (2014), “Tangible the important of accounting information such as earnings, operating cash fixed asset accounting regimes in Viet Nam: current situation and flows, accruals and cash flows ratios in predictions of operating cash flows. solutions”, Journal of Economics & Development, Special Issue, Future research on predictions of cash flows from operating should explore December, 2014, pp 56-62. more predictive indicators including non-financial information (management 8. Nguyen Thanh Hieu (2014), “Discussion about quality of accrual policies, company cultures) and macroeconomic information (inflation rate, accounting basis and cash accounting basis”, National conference, foreign exchange, etc.) in order to provide the evidences on the abilities of October, 2014, NEU. those information in future cash flows forecast. 23 24