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The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa
This study examined the relative information content of earnings and cash flow from operations data in predicting future (one-year-ahead) cash flow from operations across prevalent economic sectors in South Africa. Firstly, the ability of aggregate earnings is compared to that of cash flows from ope...
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| Format: | Thesis |
| Language: | English |
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Graduate School of Business (GSB)
2020
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| _version_ | 1867613703196639232 |
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| access_status_str | Open Access |
| author | Eltringham, Steven |
| author2 | Alhassan, Latif |
| author_browse | Alhassan, Latif Eltringham, Steven |
| author_facet | Alhassan, Latif Eltringham, Steven |
| author_sort | Eltringham, Steven |
| collection | Thesis |
| description | This study examined the relative information content of earnings and cash flow from operations data in predicting future (one-year-ahead) cash flow from operations across prevalent economic sectors in South Africa. Firstly, the ability of aggregate earnings is compared to that of cash flows from operations in predicting future cash flow from operations, and secondly, the study examines if there is any incremental benefit to disaggregating aggregate earnings information into cash flow from operations and accrual components in predicting future cash flows from operations. The study utilises a sample of 925 South African firms listed on the JSE over the period of 1994 to 2018 (25 years). The methodology made use of panel data regression techniques and instrumental variable regression techniques to estimate the relevant regression models. Moreover, to evaluate the robustness of the prediction performance of the regression models, both in-sample estimation tests over 1994-2015 and out-ofsample estimation tests over 2016-2018 were undertaken. The findings reveal that cash flow from operations is a superior predictor of future cash flows from operations as compared to aggregate earnings with respect to the full sample and the following economic sectors: (1) basic materials; (2) industrials; (3) consumer cyclicals; (4) consumer non-cyclicals; and (5) other. However, there is no evidence of either aggregate earnings or cash flow from operations being superior to one another in predicting future (one-year-ahead) cash flows from operations as it relates to either the financials or technology sector. The results further show that there is clear evidence of the explanatory gains with respect to disaggregated earnings data in predicting future cash flows from operations insofar as it relates to the full sample and the following economic sectors: (1) basic materials; (2) industrials; (3) consumer cyclicals; (4) consumer non-cyclicals; (5) technology; and (6) other. However, there is no such evidence of these same explanatory gains insofar as it relates to the financial sector. It follows that earnings disaggregated into cash flow from operations and accrual components is the best prediction model of future cash flow from operations for all sectors, excluding the consumer cyclicals sector, the financial sector as well as the full sample. Cash flow from operations and earnings disaggregated into cash flow from operations and accrual components are considered to be the best prediction models for the full sample and the consumer cyclicals sector with no apparent superiority of one over the other, whereas for the financial sector, the evidence is inconclusive as to the best model/s to utilise in predicting future cash flow from operations. In summary, this study highlights the explanatory variables that have a statistically significant effect on future cash flow from operations as well as the difference in persistence of the explanatory variables across economic sectors, thereby providing further insight into the unique cash flow generating process of each prevalent economic sector in South Africa. Therefore, users of cash flow information must consider the heterogeneity that exists amongst firms, industries and ultimately economic sectors when undertaking a study of this nature as such a consideration will enhance the identification of information content that is relevant to generating superior predictions of future cash flow from operations. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32353 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:21.707Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Graduate School of Business (GSB) |
| publisherStr | Graduate School of Business (GSB) |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32353 The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa Eltringham, Steven Alhassan, Latif Development Finance This study examined the relative information content of earnings and cash flow from operations data in predicting future (one-year-ahead) cash flow from operations across prevalent economic sectors in South Africa. Firstly, the ability of aggregate earnings is compared to that of cash flows from operations in predicting future cash flow from operations, and secondly, the study examines if there is any incremental benefit to disaggregating aggregate earnings information into cash flow from operations and accrual components in predicting future cash flows from operations. The study utilises a sample of 925 South African firms listed on the JSE over the period of 1994 to 2018 (25 years). The methodology made use of panel data regression techniques and instrumental variable regression techniques to estimate the relevant regression models. Moreover, to evaluate the robustness of the prediction performance of the regression models, both in-sample estimation tests over 1994-2015 and out-ofsample estimation tests over 2016-2018 were undertaken. The findings reveal that cash flow from operations is a superior predictor of future cash flows from operations as compared to aggregate earnings with respect to the full sample and the following economic sectors: (1) basic materials; (2) industrials; (3) consumer cyclicals; (4) consumer non-cyclicals; and (5) other. However, there is no evidence of either aggregate earnings or cash flow from operations being superior to one another in predicting future (one-year-ahead) cash flows from operations as it relates to either the financials or technology sector. The results further show that there is clear evidence of the explanatory gains with respect to disaggregated earnings data in predicting future cash flows from operations insofar as it relates to the full sample and the following economic sectors: (1) basic materials; (2) industrials; (3) consumer cyclicals; (4) consumer non-cyclicals; (5) technology; and (6) other. However, there is no such evidence of these same explanatory gains insofar as it relates to the financial sector. It follows that earnings disaggregated into cash flow from operations and accrual components is the best prediction model of future cash flow from operations for all sectors, excluding the consumer cyclicals sector, the financial sector as well as the full sample. Cash flow from operations and earnings disaggregated into cash flow from operations and accrual components are considered to be the best prediction models for the full sample and the consumer cyclicals sector with no apparent superiority of one over the other, whereas for the financial sector, the evidence is inconclusive as to the best model/s to utilise in predicting future cash flow from operations. In summary, this study highlights the explanatory variables that have a statistically significant effect on future cash flow from operations as well as the difference in persistence of the explanatory variables across economic sectors, thereby providing further insight into the unique cash flow generating process of each prevalent economic sector in South Africa. Therefore, users of cash flow information must consider the heterogeneity that exists amongst firms, industries and ultimately economic sectors when undertaking a study of this nature as such a consideration will enhance the identification of information content that is relevant to generating superior predictions of future cash flow from operations. 2020-11-02T09:48:50Z 2020-11-02T09:48:50Z 2020-11-02T09:48:02Z Master Thesis Masters MCOM http://hdl.handle.net/11427/32353 eng application/pdf Graduate School of Business (GSB) Faculty of Commerce |
| spellingShingle | Development Finance Eltringham, Steven The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| thesis_degree_str | Master's |
| title | The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| title_full | The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| title_fullStr | The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| title_full_unstemmed | The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| title_short | The Role of Earnings, Cash Flows and Accruals in Predicting Future Cash Flows: A Sectoral Experience in South Africa |
| title_sort | role of earnings cash flows and accruals in predicting future cash flows a sectoral experience in south africa |
| topic | Development Finance |
| url | http://hdl.handle.net/11427/32353 |
| work_keys_str_mv | AT eltringhamsteven theroleofearningscashflowsandaccrualsinpredictingfuturecashflowsasectoralexperienceinsouthafrica AT eltringhamsteven roleofearningscashflowsandaccrualsinpredictingfuturecashflowsasectoralexperienceinsouthafrica |