Tóm tắt Luận án Prediction of cash flows from operating among non-financial listed companies in Viet Nam

1 INTRODUCTION 1. Imperativeness of the study Cash flows always take an important role in companies like blood in human body. Therefore, predicting cash flows will help investors, managers in evaluating the performance of companies and make economic decisions. Research on prediction of cash flows in order to identify elements have abilities to predict future cash flows is essential in both theory and in reality. In terms of theory aspect, preditions of cash flows are mentioned in National Accounting Standards and some researches (Do Thi Hong Nhung, 2014; Nguyen Huu Anh, 2010; Al –Attar, 2003; Barth & cộng sự, 2001; Chotkunakitti, 2005; Ebaid, 2011; Farshadfar & Partners, 2008; Mooi, T.L, 2007). However, National Accounting Standards only provides predictive abilities in accounting standards and lack of specific evidences; and the researches still not unify the ability of predictive variables. Vietnamese accounting standard No.24 (Ministry of Finance, 2003) said that: operating cash flows information, once used jointly with other information, will help users to predict future operating cash flows. US Statement of financial accounting concepts No.1 (FASB, 1978) stated: “users interested in future cash flows and its abilities to generate favorable cash flows usually pay attention in information about enterprise’s earnings rather than information directly about cash flows” and the information about earnings and its components therefore is generally more predictive of future cash flows than current cash flows” (Barth et.al, 2001). Therefore, there are increasing demands of cash flows forecast research to provide empirical evidences for national accounting standard’s assertion and literature on cash flows predictions researches. 2 In terms of reality aspect, for many investors, bankers, finance managers, predicting cash flows take an extreme important part before making their economic decision. 80% creditors in US stated that: in loan proposal, future cash flow plan is crucial document (Do Thi Hong Nhung, 2014; Fulmer, Gavin & Bertin, 1991; Waddell D. & cộng sự, 1994). Therefore, researches which identify those indications having abilities in predicting future cash flows are helpful for many users especially for users in Viet Nam where stock market has just operated for 15 years, investigation results show that: cash flows predictions have not strictly implemented and those elements that effects cash flows have not been considered carefully; predicting qualities mostly depends on chief accountant’s experiences; methods for predicting cash flows is simple and subjective (Do Thi Hong Nhung, 2014). For those reasons, research on predicting cash flows is very essential to decision making process, especially prior studies were mainly conducted in countries with developed capital market for decades but have not been studied comprehensively and systematically in Viet Nam. Among companies operating in Viet Nam, non-financial listed companies have known as big size, manufacture and operate in vital industries and contribute large amount to GDP. Beside, cash flows from operating activities are generate main revenues for enterprises (Ministry of Finance, 2002) and be considered as signal of enterprise’s abilities to earn enough cash in order to meet daily’s performances (Boyd & Cortese – Daniel, 2000/2001). Therefore, the researcher has choosen the research of “Prediction of cash flows from operating among non-financial listed companies in Viet Nam”. 2. Research Objectives and Research Questions
Research Objectives: 3 o Review and summarize systematically theories on features of cash accounting based information and accrual accounting based information. o Examine the predictive abilities of future operating cash flow by using earnings. o Examine the predictive abilities of future operating cash flow by using operating cash flows. o Examine the predictive abilities of future operating cash flow by using operating cash flows combined with accrual components. o Examine the predictive abilities of future operating cash flow by using operating cash flows combined with disaggregated accrual components. o Examine the predictive abilities of future operating cash flow by using cash flows ratio. o Identify the superior model to predict future cash flows.
Research Questions + General research question: “Which financial elements of reported accounting information can be used to predict the future operating cash flows of non-financial listed companies in Ho Chi Minh Stock Exchange?” + Detailed questions • Question 1: Do historical earnings have the significant ability to predict the future operating cash flows of non-financial listed companies in HOSE? • Question 2: Do historical operating cash flows have the significant ability to predict the future operating cash flows of non-financial listed companies in HOSE? • Question 3: Do historical operating cash flows combined with aggregated accruals of earnings have the significant ability to predict the future operating cash flows of non-financial listed companies in HOSE? 4 • Question 4: Do historical operating cash flows combined with disaggregated accruals of earnings have the significant ability to predict the future operating cash flows of non-financial listed companies in HOSE? • Question 5: Do historical cash flow ratios have the significant ability to predict the future operating cash flows of non-financial listed companies in HOSE? • Question 6: Which of the above models has the highest ability in predicting the future operating cash flows of non-financial listed companies in HOSE? 3. Object and scope of the research Object of the research: Net Cash flows from operating activities of non-financial listed companies in HOSE. Scope of the research: 142 non-financial companies listed in HOSE within the period of 6 years (from 2009 to 2014) 4. New contributions of the research In terms of academic and theoretical aspect: Empirical evidences from this study support for Vietnamese Accounting Standard No.24 (VAS 24) on kinds of information should be used with cash flow information to enhance predictive abilities of future operating cash flows. Those important information based on accrual accounting basis included: depreciation expenses, changes in account receivables, changes in account payables and changes in inventories. In terms of practice aspect: In Viet Nam, predictions of cash flows from operating mostly based on production & sale plan or percentage of changes in accounting items compared to revenue. Those forcasting methods depend on internal 5 documents and/or bias by forcasters’ view. In order to help outsiders who find hard to reach internal information can make exact predictions, this thesis construct and investigate cash flow prediction models such as: earnings models, cash flow models, aggregated accrual components models, disaggregated accrual components model, cash flow ratios models by using financial information extracted from financial statements of listed companies on HOSE. Regressions analysis like OLS (Ordinary Least Squared), REM (Random Effects Models), FEM (Fixed Effects Models) were applied for above prediction models and FEM (Fixed Effects Models) are chosen as the most suitable models to predict operating cash flows. Results from FEM reveal that above models are significant in predicting future operationg cash flows but have different predictive powers (adjusted R-squared value of models range from 51% to 93%) with operating cash flows combined with disaggregated accrual components providing a superior comparative predictive ability on future cash flow. Therefore, potential investors and internal executive managers can use these prediction models to forcast operating cash flows for each company before making economic decisions. 5. Structure of the Research
Chapter 1: Introduction to the Research
Chapter 2: Theoretical Frame Work of cash flow and predictions of cash flow and Literature Reviews on predictions of cash flow in listed companies.
Chapter3: Hypothesis Construction and Research Methodology
Chapter 4: Data Analysis of Predictions of cash flows from operating of non – financial listed companies in Vietnamese securities market.
Chapter 5: Discussion on Research results, Conclusions and Implications CHAPTER 1: INTRODUCTION TO THE RESEARCH 6 CHAPTER 2: THEORICAL FRAME WORK AND LITERATURE REVIEW 2.1 Frame work on predictions of cash flows from operating in security market 2.1.1 Feature of accounting cash-based information and accounting accrual- based information Cash-based information may be more objective and understandable than accrual-based information. However, the Cash-based information may be less comprehensive than accrual-based information. Cash-based information cannot be replaced for accrual-based information (for example: earnings information). Combination of cash-based information and accrual-based information are potentially predictive indicators for future cash flows forecast. 2.1.2 Relation between cash flows and financial status of listed companies Cash flows always take an important role in companies like blood in human body. Cash flow provides an essential indicator on the firm’s ability to repay liabilities and directly linked to dividends policy. Besides, cash flows ratios are used as measurement for effectiveness of firm performance and indicators for bankruptcy predictions. 2.1.3 Measurement of net cash flows from operating activities for listed companies - Estimating net cash flows from operating activities (operating cash flows): before issuing International Accounting Standard No.7 - Reporting cash flows from operating activities according to National Standard and International Standard. 2.1.4 Conducting predictions of operating cash flows for listed companies Role of predictions of cash flows 7 It is accepted that: one can make predictions without making decisions but should not make decisions without making predictions (Beaver, 1966). Prediction of future cash flows is highly effective in making decision for loan and investment, estimating dividends, or company’s shares value. Factors affecting future cash flows prediction: There are inside factors and outside factors as well as objective and subjective causes, such as: investor’s interests, age of firm, size of firm, operating income results, voluntary attitude of managers, prediction techniques, competence of forecasters, input information for forecast, national regulations. 2.2 Over view of research on prediction of cash flows from operating - Predictive indicators of cash flows forecast: predictive indicators are divided into 5 groups: historical earnings, historical operating cash flows, historical operating cash flows combined with aggregated accrual components, historical operating cash flows combined with disaggregated accrual components, cash flows ratios. - Predictive method of cash flow forecast: Ordinary Least Square (OLS) - Research context and scope: Mainly in developed countries, rarely in developing countries. 2.3 Assessments of research on predictions of operating cash flows for listed companies - Firstly, since prior studies were mainly conducted in specific time, and economic context, the conclusions about the superior predictive variables still inconsistent. - Secondly, scopes of prior researches are mainly covered non-financial listed firms and therefore not categorized into specific industry groups. 8 - Thirdly, prior studies were mainly conducted in countries with developed capital market. Only few studies were conducted in developing countries and emerging market. - Finally, majority of prior researches adopted the Ordinary Least – Squared technique (OLS) in order to process and analyze data. Only few studies adopted Random Effects Models (REM), Fixed Effects Models (FEM) or step – wise models. 2.4 Research Gaps - Need of conducting research on the extent accounting and operating cash flows information are able to predict future operating cash flows of listed companies in Viet Nam. - Cash flow ratios should be considered as predictive indicators in cash flows prediction models. - Modern prediction techniques (REM, FEM, step-wise) suitable for predictions of cash flows should be applied. - Identify financial information to be used with cash flows information that enhance the predictive abilities of cash flows forecast in order to provide direct empirical evidences for assessment in Vietnamese Accounting Standard No. 24. - Scope of research in term of research time should be expanded (time of data in sample after the year of 2010) CHAPTER 3: HYPOTHESIS CONSTRUCTION AND RESEARCH METHODOLOGY 3.1 Construction of Hypothesis o Hypothesis 1: Historical earnings have the significant ability to predict the future operating cash flows of non-financial companies listed in HOSE. 9 o Hypothesis: Historical operating cash flows have the significant ability to predict the future operating cash flows of non-financial companies listed in HOSE. o Hypothesis 3: Historical operating cash flows combined with aggregated accruals have the significant ability to predict the future operating cash flows of non-financial companies listed in HOSE. o Hypothesis 4: Historical operating cash flows combined with disaggregated accruals have the significant ability to predict the future operating cash flows of non-financial companies listed in HOSE. o Hypothesis 5: Historical cash flow ratios have the significant ability to predict the future operating cash flows of non-financial companies listed in HOSE. o Hypothesis 6: Historical operating cash flows combined with disaggregated accruals components provide a superior comparative predictive ability on future cash flow of non-financial companies listed in HOSE 3.2 Research Methodology 3.2.1 Data for research This research extracted information reported on Financial Statements of 142 non – financial companies listed in HOSE for 6 years (from 2009 to 2014). Data is provided by Stox Plus Company. 3.2.2 Prediction models and prediction methods - Firstly, predict models by using regression OLS, FEM, REM and step- wise. - Secondly, assess the concordance between models and according to regression assumptions. 10 - Thirdly, compare Adjusted R2 among models and use Hausman test for choosing appropriate model. Prediction models in the research as followings 3.2.2.1 Prediction of cash flows from operating using historical earnings (Earnings Model) CFOt = β0 + β1 EARNt-1 + ε (3.1) CFOt = β0 + β1EARNt-1 + β2EARNt-2 + ε (3.2) CFOt = β0 + β1EARNt-1 + β2EARNt-2 + β3EARNt-3 + ε (3.3) 3.2.2.2 Prediction of cash flows from operating using historical Operating Cash Flows (Operating Cash Flows Model) CFOt = α0 + α1 CFOt-1 + µ (3.4) CFOt = α0 + α1 CFOt-1 + α2 CFOt-2 + µ (3.5) CFOt = α0 + α1CFOt-1 + α2 CFOt-2 + α3 CFOt-3 + µ (3.6) 3.2.2.3 Prediction of cash flows from operating using historical Operating cash flows combined with aggregated accruals CFOt = λ0 + λ1 CFOt-1 + λ2 ACRt-1 + ε (3.7) CFOt = λ0 + λ1 CFOt-1 + λ2 CFOt-2+ λ3 ACRt-1 + λ4 ACRt-2+ ε (3.8) CFOt = λ0 + λ1 CFOt-1 + λ2 CFOt-2+ λ3 CFOt-3+ λ4 ACRt-1 + λ5 ACRt-2+ λ6 ACRt-3 + ε (3.9) 3.2.2.4 Prediction of cash flows from operating using historical operating cash flows combined with disaggregated accruals CFOt = e 0 + e1 CFO t-1 + e2 ∆ARt-1 + e3∆AP t-1 + e4∆INV t-1 + e5 ∆OTH t-1 + e6DPRM t-1 + ρ (3.10) CFOt = e 0 + e1 CFOt-1 + e2 CFOt-2 + e3 ∆ARt-1 + e4 ∆AR t-2 + e5∆AP t-1+ e6∆APt-2 e7∆INVt-1 + e8∆INV t-2 + e9∆OTH t-1 + e10 ∆OTHt-2 + e11DPRM t-1 + e12DPRM t-2 + ρ (3.11) 11 CFOt = e 0 + e1CFOt-1 + e2CFOt-2 + e3 CFO t-3 + e4∆AR t-1 + e5 ∆ARt-2 +e6 ∆AR t-3 + e7∆AP t-1 + e8∆AP t-2 + e9∆AP t-3 + e10∆INV t-1 + e11∆INV t-2 + e12∆INVt-3 + e13 DPRM t-1 + e14DPRM t-2 + e15DPRM t-3 +e16∆OTH t-1 + e17 ∆OTH t-2 + e18 ∆OTH t-3 +ρ (3.12) 3.2.2.5 Prediction of cash flows from operating using historical cash flows ratios (Cash flows ratios models) CFOt = β0 + βi CFR1t-1 + βi CFR2t-1 + βi CFR3t-1 + βi CFR4t-1 + βi CFR5t-1 + βi CFR6t-1 + βi CFR7t-1 + βi CFR8t-1 + βi CFR9t-1 + ε (3.13) CFOt = β0 + β1 CFR1t-1 + β2 CFR2t-1 + β3 CFR3t-1 + β4 CFR4t-1 + β5CFR5t-1 + β6 CFR6t-1 + β7 CFR7t-1 + β8 CFR8t-1 + β9 CFR9t-1 + β10CFR1t-2 + β11 CFR2t-2 + β12 CFR3t-2 + β13 CFR4t-2 + β14 CFR5t-2 + β15 CFR6t-2 + β16 CFR7t-2 + β17 CFR8t-2+ β18 CFR9t-2 + ε (3.14) CHAPTER 4: DATA ANALYSIS: PREDICTIONS OF CASH FLOWS FROM OPERATING OF NON – FINANCIAL LISTED COMPANIES 4.1 Characteristic of companies listed on Viet Nam Securities Market 4.2 Descriptive Statistics Analysis 4.3 Correlation Evaluation 4.4 Empirical results of predictions of cash flows from operating of non- financial companies listed on HOSE 4.4.1 Regression results of earnings models Table 4.4a: Regression results of earnings models (FEM) (general information) FEM Variables Prob (F- Statistic) Adjusted R2 4.1.1 EARNt-1 0.00000 0.773219 4.1.2 EARNt-1 0.792889 12 EARNt-2 0.00000 4.1.3 EARNt-1 0.00000 0.813363 EARNt-2 EARNt-3 Table 4.4b: Regression results of earnings models (FEM) (detailed information) FEM Variables Coefficients Prob. 4.1.1 EARNt-1 0.708268*** 0.000 4.1.2 EARNt-1 1.037564*** 0.000 EARNt-2 -0.832427*** 0.000 4.1.3 EARNt-1 0.841508*** 0.000 EARNt-2 -1.072893*** 0.000 EARNt-3 0.699031*** 0.000 4.4.2 Regression results of operating cash flows models Table 4.7a: Regression results of cash flows from operating model (FEM) (general information) FEM Variabl es Prob (F- Statistic) Adjusted R 2 Durbin - Watson 4.2.1 CFOt-1 0.0000 0.764312 2.613703 4.2.2 CFOt-1 0.00000 0.789254 2.695733 CFOt-2 4.2.3 CFOt-1 0.00000 0.796054 2.648768 CFOt-2 CFOt-3 Table 4.7b: Regression results of cash flows from operating models (FEM) (detailed information) 13 FEM Variables Coefficients Prob. 4.2.1 CFOt-1 -0.497463*** 0.000 4.2.2 CFOt-1 -0.493368*** 0.000 CFOt-2 0.411615*** 0.000 4.2.3 CFOt-1 -0.515405*** 0.000 CFOt-2 0.337359*** 0.000 CFOt-3 0.260078*** 0.000 4.4.3 Regression results of Operating cash flows combined with aggregated accruals Models Table 4.10a: Regression results of operating cash flows combined with aggregated accruals model (FEM) (general information) FEM Prob (F- Statistic) Adjusted R2 Durbin - Watson 4.3.1 0.000 0.828346 2.418038 4. 3.2 0.000 0.828309 2.434564 4.3.3 0.000 0.875557 2.737023 Table 4.10b: Regression results of operating cash flows combined with aggregated accruals models (FEM) (detailed information) Model Variables Coefficients Prob. 4.3.1 CFOt-1 0.187462** 0.005 ACRt-1 0.903857*** 0.000 4.3.2 CFOt-1 0.192402** 0.0193 CFOt-2 -0.029462 0.7628 ACRt-1 0.879525*** 0.000 ACRt-2 -0.095209 0.3141 CFOt-1 0.080905 0.2753 4.3.3 CFOt-2 -0.415047*** 0.000 14 CFOt-3 0.763306*** 0.000 ACRt-1 1.001574*** 0.000 ACRt-2 -0.16145** 0.0762 ACRt-3 1.106636*** 0.000 4.4.4 Regression results of Operating cash flows combined with disaggregated accruals Models Table 4.13a: Regression results of operating cash flows combined with disaggregated accruals model (FEM) (general information) FEM Prob (F- Statistic) Adjusted R - squared Durbin - Watson 4.4.1 0.000 0.883925 2.484538 4.4.2 0.000 0.922246 2.548625 4.4.3 0.000 0.93063 2.501873 Table 4.13b: Regression results of operating cash flows combined with