Abstract
<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 35.7pt 0pt 0.5in; text-align: justify; mso-layout-grid-align: none; mso-outline-level: 1;" class="MsoNormal"><span style="font-family: Times New Roman;"><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: ZH-HK;">This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear stock selection models are constructed based on the cross-section of equity returns with firm-specific attributes as model inputs.</span><span style="color: black; mso-themecolor: text1; mso-fareast-language: ZH-HK;"><span style="font-size: small;"> </span></span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: ZH-HK;">Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE.</span><span style="color: black; mso-themecolor: text1; mso-fareast-language: ZH-HK;"><span style="font-size: small;"> </span></span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: ZH-HK;">The risk-adjusted performance of the nonlinear models is found to be comparable with that of linear models.</span><span style="color: black; mso-themecolor: text1; mso-fareast-language: ZH-HK;"><span style="font-size: small;"> </span></span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: ZH-HK;">In terms of artificial neural network modeling, the extended Kalman filter learning rule is found to outperform the traditional backpropagation approach. This finding is consistent with our prior findings on global stock selection.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>
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