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An apples-to-apples comparison of transition pre-trade analyses is not straightforward, if indeed possible. Due to fiduciary responsibilities, transition clients are inclined to provide transition managers with only high-level pre-trade portfolio characteristics instead of the actual underlying holdings to avoid information leakage. Providing portfolio characteristics leaves room for different interpretations by transition managers on the exact underlying assets involved in the transition. This issue is a major concern that causes noise in comparing pre-trades. This article presents several suggestions for reducing noise in pre-trade comparison while improving the client’s understanding of pre-trades (and comparisons.)

This paper discusses the issues one encounters when comparing transition track records. Different transition benchmarks, different (sub) asset class classifications, "fair" versus "unfair" and differences in pre-trade assumptions and client instructions affect transition composites/track records and make it difficult to make a reliable comparison. To remove the need of sub-dividing transitions and addressing the problem of insufficient transition events at the same time, one can apply an independent sophisticated post-trade algorithm. Such an algorithm takes into account differences in market volatility, liquidity and price movements of each security during the transition. An algorithm basically "normalizes" the multiple variations of portfolio transitions so they can legitimately be compared against each other in a broader universe.

This article investigates the commonality in risk factors and sector biases in managed volatility equity strategies (both active and passive). A unique aspect of this article is that it is written from the perspective of a user of the strategies. Unlike all other past studies it considerers several versions of low volatility strategies—from different managers with different investment approaches and risk models. The findings here are therefore more general and not specific to a particular implementation or risk model. We will explore both actively managed volatility strategies as well as an index strategy.

In this article,Tol andWanningen aim to quantify empirically how much the information ratio may improve for a 130/30 extension strategy versus a long-only strategy. The authors collected 42 estimates of transfer coefficients for U.S. large-cap benchmarked 130/30 products and their equivalent long-only products. According to the fundamental law of active management, an improvement in the transfer coefficient directly translates into an improvement in the information ratio, assuming a skillful manager. Their results show an average increase in the transfer coefficient of between 40% and 50%, depending on the long-only tracking error. This finding implies a 40%–50% increase in the information ratio. Furthermore, the authors reason that the increase in the transfer coefficient depends on the tracking error of the long-only strategy. Tol and Wanningen’s empirical results show that to move from a long-only to a 130/30 strategy would result in an average increase in the transfer coefficient of 42% for a strategy with an ex ante tracking error of between 0% and 2%; 48% for a strategy with an ex ante tracking error of between 2% and 3%; and 29% for strategies with a tracking error higher than 3%. This shows that the sweet spot (i.e., the highest increase in transfer coefficient by moving from long-only to 130/30) occurs in the 2%–3% long-only tracking error range. The study reveals theoretically overestimated information ratio improvements for more than 40% of the product pairs.

Many have advanced the theoretical attractiveness of long-only extension products, more widely known as 130/30 strategies. In this article, the authors address the practical aspects of successfully implementing a 130/30 strategy based on an analysis of their unique database of actual manager performance for 130/30 versus long-only. The authors compare the returns of extension products with their long-only counterparts using a dataset of 73 product pairs from 53 managers, and find that 55% of the extension products have a higher information ratio than the corresponding long-only product. After testing for differences in the mean monthly alphas, the authors find that managers who deliver a higher information ratio in the 130/30 product also deliver mean monthly alphas in excess of the long-only product at a 5% significance level. Furthermore, the authors9 analysis reveals that only 33% of the entire sample of managers adds value through shorting and only a small subset of managers is able to compensate for underperformance in short positions by outperforming in long positions while achieving a higher information ratio than the long-only counterpart product. The authors thus conclude that adding value in short positions is highly important in running a successful 130/30 strategy.

This article investigates whether bond yield spreads are suitable for analyzing country risk. As bond prices and bond yields are determined in the secondary market, bond yields and their spreads vis-a-vis U.S. Treasuries may provide a more continuous and reliable information base than traditional measure of country risk. In more than a dozen countries, we study the association between the yield spread and the Institutional Investor country rating in the mid-1990s. Rank correlations show that bond yields are a good reflection of country risk.