Developing a systematic risk model for JSE listed companies

Abstract

Since 1952, the modern portfolio theory (MPT) has dominated academic thought on the estimation of systematic risk. The MPT puts forward that systematic risk and return are positively and linearly interrelated. Although this notion has been widely accepted in financial theory and modelling, researchers have empirically questioned its viability since the 1960s. While the MPT has proven to be scientifically inadequate because it generalises the risk-return trade-off principle, some studies have yielded diverse findings, indicating that the type of market and the type of sample influence the degree to which risk and return are related and/or unrelated. Furthermore, the MPT does not provide explicit details on the quantification of ‘systematic risk’ or ‘return’. This leaves gaps for different interpretations, as users of financial information may quantify these concepts differently, which could, naturally, lead to diverse conclusions. This study was undertaken to determine whether systematic risk and return are interrelated for a sample of JSE listed companies. It set out to explore whether the assumed association between systematic risk and return has empirical substance from a South African standpoint, and whether other accountancy-related measures are able to better predict behaviour in systematic risk, in relation to sampled South African companies. The primary objective of the study was to develop a new systematic risk model, which consists of accountancy-related items other than returns, to predict leveraged and unleveraged systematic risk in selected JSE listed companies A mixed research methodology was applied, in the form of a triangular mixed research design. Specifically, the study applied a qualitative method (in the form of a systematic review) to seek support for the quantitative models constructed. The research objectives

(both primary and secondary) were fulfilled through the execution of three research stages. The first stage empirically tested the relationship between systematic risk and accountancy-related return measures. It was statistically found that these return measures were not necessarily well associated with systematic risk proxies in the sample of JSE listed companies. To address this predicament, the second stage of the research process identified alternative measures which associate with systematic risk. These alternative measures were quantitatively modelled by applying multiple regression analysis. Stage 2 of the research found that aspects such as liquidity, cash flow per share, market value added, gearing, debt to assets, price to book value, price to net assets and return on external investments could predict both leveraged and unleveraged systematic risk in sampled companies. The third stage of the research process was undertaken to seek qualitative validation for the empirical findings of the study. This was done by qualitatively analysing similar research in order to explore whether previous studies could confirm the empirical findings. This means that other researchers have also observed similar patterns in the execution of their statistical analysis. Qualitative analysis was applied through systematic reviewing of peer-reviewed research articles published in accredited academic journals. This thesis contributes to financial theory by reminding the reader that generalised financial theories should not necessarily be accepted as gospel. Systematic risk is a complex concept and there is no ‘one-size-fits-all’ definition to enable the user to comprehend it. Users of financial statements will need to do on-going research in order to keep track of how systematic risk and its behaviour change under different circumstances, over different time spans and in different markets. Where financial managers and stock brokers adopt the risk-return trade-off oblivious of scientific and empirical findings, systematic risk and its estimation may not be tracked in a manner that is helpful to the investor. Ultimately, only statistical analysis can reflect the true state of systematic risk behaviour within a given time frame.