Forecasting stock returns using model-selection criteria

This paper examines the economic significance of return predictability in Australian equities. In light of considerable model uncertainty, formal model-selection criteria are used to choose a specification for the predictive model. A portfolio-switching strategy is implemented according to model predictions. Relative to a buy-and-hold market investment, the returns to the portfolio-switching strategy are impressive under several model-selection criteria, even after accounting for transaction costs. However, as these findings are not robust across other model-selection criteria examined, it is difficult to conclude that the degree of return predictability is economically significant.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Do socially responsible fund managers really invest differently?

To date, research into socially responsible investment (SRI), and in particular the socially responsible investment funds industry, has focused on whether investing in SRI assets has any differential impact on investor returns. Prior findings generally suggest that, on a risk-adjusted basis, there is no difference in performance between SRI and conventional funds. This result has led to questions about whether SRI funds are really any different from conventional funds. This paper examines whether the portfolio allocation across industry sectors and the stock-picking ability of SRI managers are different when compared to conventional fund managers. The study finds that SRI funds exhibit different industry betas consistent with different portfolio positions, but that these differences vary from year to year. It is also found that there is little difference in stock-picking ability between the two groups of fund managers.

Stock structure of Spanish mackerel Scomberomorus commerson along the Australian east coast deduced from parasite data

The parasite fauna of Spanish mackerel Scomberomorus commerson from three regions off eastern Australia was examined for evidence of separate stocks. The abundance of five metacestodes was very similar in all areas suggesting that extensive mixing of the fish occurs along the coast, unlike the Situation across northern Australia where large differences have been found between regions. The similarity in abundances of two metacestodes from Townsville fish and south-east Queensland fish Suggests that these two regions have fish with very similar histories. The data lead to the conclusion that the seasonal fishery for Spanish mackerel off south-east Queensland is based on a random group of fish from the same origin as fish sampled off Townsville and is not a subpopulation that moves south each year. (c) 2006 The Fisheries Society of the British Isles.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.