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Outlines previous research on measuring the performance of investment funds, suggesting that data envelopment analysis (DEA) techniques can overcome some of the problems of the capital asset pricing model and give pointers for improvement. Uses DEA to assess the relative performance of 257 Australian mutual funds 1995‐1999 and logistic regression to investigate the characteristics which affect it. Describes the methodology and presents the results, which suggest that scale efficiency is the main source of overall technical efficiency and that both are higher for risk‐averse funds with high positive net asset flows. Explains the ASSIRT rating system for managed funds and finds the ratings strongly associated with DEA relative efficiency scores. Believes the findings are useful to analysts, investors and managers.

This paper investigates the association between portfolio returns and higher-order systematic co-moments at different timescales obtained through wavelet multi-scaling, a technique that decomposes a given return series into timescales enabling investigation at different return intervals. In Australian industry portfolios, the relative risk positions indicated by systematic co-moments at some timescales are different from those revealed in daily returns. A strong positive (negative) linear association between beta and portfolio return and co-kurtosis and portfolio return in the up (down) market is observed in daily returns and at different timescales. The beta risk is priced in the up and down markets. Co-kurtosis is not priced when the beta is in the pricing model. Co-skewness appears to be priced at a relatively high timescale and this is observed only after the up and down separation of market returns.Wavelet multi-scaling, Higher-order systematic co-moments, Asset pricing, Conditional pricing models,

Traditional procedures for clustering time series are based mostly on crisp hierarchical or partitioning methods. Given that the dynamics of a time series may change over time, a time series might display patterns that may enable it to belong to one cluster over one period while over another period, its pattern may be more consistent with those in another cluster. The traditional clustering procedures are unable to identify the changing patterns over time. However, clustering based on fuzzy logic will be able to detect the switching patterns from one time period to another thus enabling some time series to simultaneously belong to more than one cluster. In particular, this paper proposes a fuzzy approach to the clustering of time series based on their variances through wavelet decomposition. We will show that this approach will distinguish between time series with different patterns in variability as well identifying time series with switching patterns in variability

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