1 LOS CONVENIOS SECTORIALES DE NACIONES UNIDAS.

There is an apparent lack of robust, empirical research on return prediction based of cash flow figures in South Africa. Given the country’s relatively sophisticated financial sector and challenging business conditions, cash flows are generally studied in terms of business failure or liquidity. Jooste (2007) finds that cash flow ratios predict bankruptcy up to three years prior to the event. These companies often show a net profit and positive liquidity ratios, yet the cash flows provide early signals of financial strengths or weaknesses of a firm (Jooste, 2007).

Nyamgero (2015) finds that levels of cash holdings of South African companies are lower than they were in 1997, contrary to the criticisms major South African companies have received regarding excess cash sitting on their balance sheets. Jooste (2007) investigates failed entities and finds evidence suggesting bankrupt companies have lower cash flows and smaller reserves of liquid assets. This makes it difficult to meet debt obligations and

36 creates further credit risk. It is unclear whether this relationship is reflected in share prices and returns on the Johannesburg Stock Exchange.

Gumbi (2013) runs a simple regression analysis of current cash flows and future share prices over a sample period of 11 years on the JSE. The results show a weak relationship in both the long and short run - average R2 values are 0.24 and 0.33, respectively. These findings are consistent with Kim and Kross (2005) but differ in that the relationship between cash flows and share prices is growing (rather than declining) in the long run. It must be noted that the Gumbi (2013) did not address stationarity of cash flows and share prices, nor was the analysis particularly robust.

Van Niekerk (1992) shows that cash flows (from operating activities and from investing activities) have incremental information content to model share prices on the JSE. There was no such evidence for share returns. Fourie (1992) finds that cash flows are significantly associated with abnormal returns, using various cash flow variables to model twelve-month cumulative abnormal returns of 35 companies listed on the JSE over the 1987-1991 period. Wessels, Smith and Gevers (1993) propose that cash flow from operations can be considered an important indicator of the quality of income of a company. In addition, the simple linear regression model based on a smoothed cash flow beta is shown to provide significant explanatory power of the variability in market beta. Wapenaar (1996) however, does not find any significant correlation of cash flow variables with returns. De Jager (1997) conducts a factor analysis on which ratios best serve as explanatory variables in predicting corporate success or failure on the JSE. Surprisingly, out of 62 variables, De Jager finds that cash flow return on investment and financial leverage are the two most significant.

3.3 Summary and Conclusion

This chapter introduced the empirical findings on anomalies and the predictive ability of traditional accrual-based measures as well as research on cash components. Although many anomalies have been documented, limited attention has been given to cash flow variables, especially within a South African context. International research around the usefulness of cash flow data for security pricing has produced inconsistent results. This study aims to explore the value of using cash flow information in the fundamental investment process by providing clarity on the relationship between a company’s cash

37 flows and return behaviour. The lack of empirical evidence around this topic motivates further investigation.

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Chapter 4: Data

This chapter introduces the data used to answer the research problems set out in Chapter 1. The data consists of two distinct subsets: company fundamental data and share price data. The biases that may occur in financial data will be discussed, as well as adjustments that were made to the dataset to mitigate such biases. Descriptive statistics of the preliminary dataset are also presented.

Section 4.1 discusses the sample collected and used in the study. Section 4.2 covers share data and adjustments in terms of completeness, comparability, liquidity and outliers. Section 4.3 considers the possibility of bias and adjustments with regards to data snooping, look ahead bias and survivorship bias. Section 4.4 analyses some descriptive statistics of the dataset and Section 4.6 presents a summary and conclusion.

4.1 Data

All market and fundamental data were obtained from Datastream, accessed from the Business corner in The Research Wing at The Chancellor Oppenheimer Library at the University of Cape Town. Together with Microsoft Excel, EViews was used perform the data analyses in this study.

Share price, return and financial statement information was collected for constituents of the Johannesburg Stock Exchange All-Share Index (JALSH) for the period June 2007 to August 2018. However, the analysis was conducted for the period March 2008 to March 2018 due to trailing and lagging values employed in the methodology. The data is also subject to constraints discussed in Section 4.3 and Section 4.4 below.

The major fundamental and market variables are outlined below. The study will refer to variables by their ‘Code’, while ‘Datatype’ refers to the Datastream mnemonic. Detailed definitions are provided in Appendix A.

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Table 4.1 Fundamental and Market Data

Financial Statement Data Code Datatype

Statement of Profit/Loss and Other Comprehensive Income

Operating Profit

Net Income Before Extraordinary Activities Net Income OP NIBX NI SOPI NIBX NINC

Statement of Financial Position

Total Assets Total Equity Shares Outstanding TA BVE NOSH DWTA QTLE NOSH

Statement of Cash Flows

Net Cash Flows from Operating Activities Cash Flows from Financing Activities Cash Flows from Investing Activities Free Cash Flow

Net Change in Cash

CFOA Fin Act Inv Act FCF NCIC OTLO FTLF ITLI FCF SNCC Market Data Ticker Name Industry/Sector

Market Value of Equity Price

Total Return Index

MVE P TR MV P RI

The dataset in Table 4.1 ensures that each share has sufficient cash flow and profitability variables on an interim basis. Though ‘Gross Profit ‘and ‘Capital Expenditure’ were initially included, there was significant data missing from the sample. This is attributed to a limitation of Datastream, specifically the lack of single-period semi-annual cash flow figures for ‘Capital Expenditure’ and semi-annual ‘Gross Profit’ figures. Although annual figures were available, it was considered inaccurate to assume an even split of year-end results. Therefore, these measures were excluded from the analysis.

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