Determinants of Sovereign Ratings: A Comparison of Case-Based Reasoning and Ordered Probit Approaches

The paper compares two alternative techniques for the modelling of the determinants of sovereign ratings, specifically, ordered probit and case-based reasoning. Despite the differences in approach the two alternative modelling approaches produce similar results in terms of which variables are significant and forecast accuracy. This suggests that either approach can be used, and that there is some robustness in the results. As regards significant variables, both models find that a proxy for technological development, specifically, mobile phone use, is the most important variable. Apart from the technology proxy, a range of conventional macroeconomic variables are found to be significant, in particular GDP and inflation. The models are then used to produce forecasts for 2002 and for a set of unrated countries. The forecast comparison indicates the critical role played by the technology proxy variable in the modelling.Sovereign Ratings, Ordered Response Models, Case-Based Reasoning

New evidence on sovereign to corporate credit rating spill-overs

We explore what happens to domestic firm-level ratings around the time of a sovereign-rating action on a day-by-day and country-by-country basis. Our granular approach provides banks and investors with a fuller picture of their sovereign credit risk exposure and, as such, our analysis might feed into banks' internal modelling of their credit risk exposure for the purpose of determining regulatory capital, introduced under Basel II. We also provide a novel analysis of any bias in spill-over and we show that, inter alia, the tendency for greater spill-over of negative sovereign-rating actions can largely be accounted for by firm-and sovereign-level factors. However, even after allowing for these factors, some countries suffer from negative bias. The implied higher correlation between sovereign and firm-level ratings in times when countries are in crisis versus when they are in recovery may contribute to quicker and/or deeper crises versus slower and/or longer recoveries. Keywords: (C) 2017 Elsevier Inc. All rights reserved

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.G12 G14 G15 Volume Stock market returns Binary probit

Country risk and the estimation of asset return distributions

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Asset allocation of Australian superannuation funds:a Markov Regime switching approach

We extend an observable Markov Regime Switching framework to assess the switching behaviour of asset classes of Australian superannuation funds across different fund sizes. We identify the most prominent asset class which contributes to the performance of the investment options and what factors trigger funds’ decisions on rebalancing their portfolio. We find that smaller funds tend to be more active in switching to aggressive options and the larger funds are more conservative. However, in periods of volatility, the large funds are the risk seekers and tend to switch their asset classes and hence their investment strategies. The asset classes whose values add to the performance of the investment options are equity markets and bond markets with the domestic equity market having better performance than international equity market. The switch for the larger funds is driven by volatility of the equity market.</p