Do benchmark African equity indices exhibit the stylized facts?

This paper investigates if benchmark African equity indices exhibit the stylized facts reported for financial time-series returns. The returns distributions of the Africa All-Share, Large, Medium and Small Company Indices were found to be leptokurtotic, had fat-tails, over time experienced volatility clustering and exhibited long memory in volatility. Both the All-Share and Large Company Indices were found to exhibit leverage effects. In contrast, positive shocks had a greater impact on future volatility for the Small Company Index which implies a reverse leverage effect. This finding could reflect a bull/bubble market for small capitalisation stocks in Africa.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.