This paper investigates nonlinear time series modelling using the general state-dependent autoregressive model. To achieve the estimate of the model, an attempt is made to approximate the state-dependent parameter by employing the Gaussian radial basis function for its universal approximation capability. As a result, a radial basis function-based autoregressive (RBF-AR) model is derived which has a form similar to a generalized exponential autoregressive model. To reach the applicability of the RBF-AR model, the evolutionary programming algorithm is employed to select a suitable set of radial basis function centres. By applying the resulting model to some complex data, it is shown that the RBF-AR model can not only reconstruct the dynamics of given nonlinear time series e ectively, but also give much better ® tting to complex time series than the approach of directly RBF neural network modelling. Therefore, as a paradigm combining a statistical model and neural network, the RBF-AR model has better performance than the RBF neural network especially for its problem reduction of the curse of dimensionality which is usually regarded as one of the potential main di culties of RBF neural network modelling.

We study a sample of Form 13F filings where fund advisors seek confidential treatment for some or all of their 13(f)-reportable positions. Consistent with the hypothesis that managers seek confidentiality to protect proprietary information, we find that confidential positions earn positive and significant abnormal returns over the post-filing confidential period. We also find that managers are more likely to seek confidential treatment of illiquid positions that are more susceptible to front-running. Overall, our analysis highlights important benefits of reduced disclosure that are relevant to the current policy debate on hedge fund transparency.

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