Liquidity is a measure of the ease with which an asset can be converted into cash. In a perfectly liquid market, conversion is instantaneous and does not incur costs. Amihud and Mendelson (1986:224) proposed that illiquidity increases the expected return on an investment (liquidity premium) and simultaneously lengthens the holding period. These two effects are known respectively as the “spread-return relationship” and the “clientele effect” and have theoretical as well as practical implications. From a theoretical perspective it may help to explain the gap between the capital asset pricing model (which assumes that markets are perfectly liquid) and the associated empirical evidence; which thus far has been rather poor. From a practical perspective, liquidity will influence stakeholders’ decisions and market competitiveness (Amihud&Mendelson, 1991:61-64). The relevant stakeholders are governments, stock exchange regulators, corporations, investors and financial intermediaries. Emerging economies such as the South African economy typically have less liquid markets than the developed world. While this may be attractive for investors looking for higher returns, Amihud and Mendelson (1991:61) are of the opinion that liquid markets are more generally favoured by investors. Constantinides (1986:842-858), also proposes a model for liquidity, but found the liquidity premium to be of lesser importance than that proposed by Amihud and Mendelson (1986:223-231) but also supports the suggestion that investors will favour liquid markets. Although it is by no means a perfect proxy, a security’s bid-ask spread has been found to be an attractive and effective measure of liquidity. It has been found to correlate with beta as well as market capitalisation and several other variables commonly used in capital markets research. Because of this correlation the effect of the bid-ask spread cannot be studied in isolation when regression techniques are employed (Ramanathan, 1998:166). This is particularly problematic because empirical evidence for beta, which is arguably the most important independent variable in financial cross sectional relationships, is weak. Beta has to be estimated and so it is not clear if real markets do not support CAPM theory or if beta cannot be estimated with the required accuracy. All of the common independent variables used in empirical capital markets research are correlated to beta, and for this reason it cannot be established if these variables have a real effect or if they are simply serving as a proxy for the difference between the real and the estimated beta. Various strategies have been proposed to increase the accuracy of beta estimation and these are discussed in detail in this research. Successes with these strategies have been mixed. A second problem encountered in the empirical research base relating to the CAPM is that in the theory the cross-sectional relationship is between expected market return (which cannot be observed due to the vast number of real investments beyond those listed on exchanges) and beta, whereas empirical research makes use of actual return on a market proxy and beta. In order for the actual return to approach the expected return, empirical studies have to be conducted over extended periods. Accurate data for such periods are generally lacking and severe macro-economic changes such as wars, may also affect rational economic behaviour. It has to be kept in mind that the entire CAPM theory flows from the simple assumption that investors aim to achieve the highest return per unit of risk, and so a rejection of beta is a rejection of rational investor behaviour. Liquidity however, addresses one of the assumptions of CAPM, namely that markets are perfectly liquid; which obviously is not met in real markets and so CAPM models expanded for liquidity should be a reasonably fundamental starting point for all empirical capital markets research. The current empirical evidence for the spread-return relationship is inconclusive. While some researchers have found a significant relationship, others have questioned the ability of the methodology to differentiate a true relationship from the ‘proxy for errors in the estimated beta’ problem. Deductions (as explained in section 4.3) that have been made from the research of Marshall and Young (2003:176-186) in particular, provide strong evidence that at least some of the relationship is due to the ‘errors in estimated beta’ problem. Little empirical work has been done on the clientele effect. Atkins and Dyl (1997:318-321) found a significant relationship between holding period and bid-ask spread, although their approach was somewhat unorthodox in the sense that portfolio formation was not done and the effect of beta was not tested. This study tests empirically both the spread-return relationship and the clientele effect on the Johannesburg Stock Exchange over the period stretching from January 2002 to June 2007. The methodology of Fama and Macbeth (1973:614-617) as well as the aggregated beta of Dimson (1979:203-204) were mainly used, with some modifications as suggested by other researchers. With regard to the spread-return relationship, the findings of this study do not support theoretical expectations. This may be due to the short time period that was used as well as the difficulty in estimating beta. To the contrary, very significant evidence for the clientele effect was found, with little to no influence from market capitalisation and beta, which is as expected. Further investigation into the spread-return relationship is required. If a liquidity premium is not present, foreign investors will favour liquid developed markets above the JSE. This implies that efforts of exchange regulators and the government to decrease illiquidity will lead to foreign portfolio investment inflow into the South African economy.