This study examines the impact of South Africa’s national soccer, rugby and cricket teams’ performances in international matches on returns on the Johannesburg Stock Exchange (JSE). Match results constitute a mood proxy variable hypothesised to affect stock returns through its influence on investor mood. The unconditional mean return on the JSE All Share index for a 13½ year period from September 1995 to February 2009 was compared to the mean return after wins, draws and losses by the national sport teams. An event study approach was followed and four different statistical tests were conducted in order to test for a relationship. The results of the tests indicate the existence of a moderate win effect, with mean returns after wins being statistically significantly higher for the categories all sports combined, cricket and soccer.

This paper offers an explanation of the use of tenure-track contracts in academia. It argues that, because the results of academic research cannot be sold, a professor's profitability depends on the market value of the instruction he or she provides. But because that value depends directly on the extent of his or her observable research accomplishments, a profit-maximizing university will dismiss a professor who fails to initially establish a strong research record, but will tolerate a professor who fails to augment a record that is already strong.

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none; mso-layout-grid-align: none;"><em style="mso-bidi-font-style: normal;"><span style="color: black; font-size: 10pt;"><span style="font-family: Times New Roman;">Genetic programming is employed to develop trading rules, which are applied to test the efficient market hypothesis. Most previous tests of the efficient market hypothesis were limited to trading rules that returned simple buy-sell signals. The broader approach taken here, developed under a framework consistent with the standard portfolio model, allows use of trading rules that are defined as the proportion of an investor&rsquo;s total wealth invested into the risky asset (rather than being a simple buy-sell signal). The methodology <span style="mso-bidi-font-weight: bold;">uses average utility of terminal wealth as the fitness function, as a means of adjusting returns for risk. With data on </span>daily stock prices from 1985 to 2005, the algorithm finds trading rules for 24 individual stocks. These rules then are applied to out-of-sample data to test adaptive efficiency of these markets. Applying more stringent thresholds to choose the trading rules to be applied out-of-sample (an extension of previous research) improves out-of-sample fitness; however, the rules still do not outperform the simple buy-and-hold strategy. <span style="mso-bidi-font-weight: bold;">These findings therefore imply that the 24 stock markets studied were adaptively efficient during the period under study.</span></span></span></em></p>

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