Nonlinearities In Stock Return Prediction: Evidence From South Africa

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>

Nonlinearities In Stock Return Prediction: A Blended Approach

“…Using stock selection models with inputs that represent different characteristics of sample stocks, our prior study concludes that nonlinear models outperform linear models in the global equity markets . When a similar study is conducted on the South African JSE Securities Exchange, we find that nonlinear models outperform linear models at times, but the results are inconclusive over the entire examination period from 2002 to 2007 (Hodnett, Hsieh and van Rensburg, 2012). This finding motivates for a blended approach that has the potential of improving the stock selection technique by efficiently diversifying the periodic forecasting errors of the linear and nonlinear models.…”

“…The performance statistics of the best linear stock selection model and the best nonlinear stock selection model in Hodnett, Hsieh and van Rensburg (2012) are presented in Table 1. The best linear model is represented by the model having the objective of selecting inputs that optimize the in-sample Qian and Hua (2003) information ratio (QH IR).…”

“…Hodnett, Hsieh and van Rensburg (2012) evaluate the performance of a series of linear and nonlinear stock selection models on the JSE Securities Exchange in South Africa over the period from 1997 to 2007. The two linear models in this study have the objectives of maximizing the in-sample Grinold (1989) …”