Marco A. Navone

Which Factors Affect Bond Underwriting Fees? The Role of Banking Relationships

The question of which factors are relevant in determining bond underwriting fees is empirically investigated by analysing 2,202 bond issues completed by European firms during the 1993 – 2003 period. Four major results emerge from the analysis. First, the introduction of the single currency in 1999 has generated an increase in competition among banks, and, as a result, a reduction in underwriting fees. Second, a strong relationship with the issuers main bank reduces the level of underwriting fees. Third, new issuers are charged with lower underwriter fees relative to firms that have completed issues without building any strong relationship with a bank. Fourth, higher reputation banks charge lower underwriting fees. The implications of these findings are also discussed.

The Power of Dynamic Asset Allocation

This article re-assesses the evidence and practical relevance of asset returns’ long-horizon predictability, investigating whether practitioners can profitably exploit predictability patterns by using relatively simple, dynamic asset allocation strategies. The analysis shows forward-looking models that rely on steady-state equations for equities and initial yields to maturity for bonds are far better predictors of markets’ long-run direction than is the industry’s conventional approach, which involves extrapolating from historical averages. Using a long-term U.S. sample from 1926 to 2010, the authors find that predictability translates into significantly better risk-adjusted performance from dynamic asset allocation strategies that rely on forward-looking inputs.

The Determinants of the Convexity in the Flow-Performance Relationship

There is substantial evidence that the flow-performance relationship of mutual funds is convex. The authors empirically investigate the determinants of such convexity. In particular, they study how fund fees (for example, marketing and nonmarketing fees) and the uncertainty related to the replacement option of fund production factors (investment strategies and managerial ability) impact the convexity of the flow-performance relationship. The evidence suggests that marketing fees are positively related to the convexity of the flow-performance relationship. Nonmarketing fees appear to have a negative impact on this convexity. Consistent with investment restrictions being relevant in explaining investors’ allocation decisions, sector and index funds exhibit lower convexity in their flow-performance relationship than respectively diversified and non-index funds. Finally, the dispersion of managerial abilities within a mutual fund segment is associated with higher convexity in the flow-performance relationship.

Why Do Financial Analysts Strive to Be Irrelevant? Career Concerns and Endogenous Coverage Termination

In this paper we investigate the negative relationship between analysts’ coverage and stocks idiosyncratic volatility. While prior research argues that analysts cause the low level of idiosyncratic risk because they lack access to firm-specific information we hypothesize that the causal relation goes in the opposite direction. Specifically we argue that due to reputational concerns analysts avoid covering stocks with high levels of specific risk. Using three novel quasi-natural experiments we show that analysts’ coverage drops after an exogenous increase in idiosyncratic volatility. We also show that coverage increases after an exogenous decrease of specific risk and that an exogenous decrease in the number of analysts covering a firm lead to a decrease firm-specific information. Looking at individual analysts’ characteristics we show that the probability of dropping coverage after an increase in idiosyncratic risk is higher for younger analysts, analysts with lower reputation and lower risk aversion.

Practical Applications of The Power of Dynamic Asset Allocation

Global multi-asset managers continue to rely on faulty historical models for asset allocation, in spite of evidence showing the shortcomings of this model. Forward-looking returns are much more reliable forecasting models, says Mirko Cardinale, Head of Asset Allocation for Europe, the Middle East and Africa at Russell Investmentsin London. Cardinale co-wrote The Power of Dynamic Asset Allocation , In this Practical Applications report, Cardinale explains that the article attempts to bridge the gap between theoretical research on equity and bond return predictability and industry practice. It was co-written by Marco Navone , a Senior Lecturer in Finance at the UTS Business School of the University of Technology Sydney , and Andrzej Pioch, a Multi-Asset Fund Manager at Aviva Investors in London.

Firm Opacity Lies in the Eye of the Beholder

Given the central role of firm opacity in most finance theories, empirical proxies that identify firm opacity correctly should allow for more powerful tests of these theories. The last decade has seen adoption of several different empirical proxies that aim to capture firm opacity. However, there is no study that has compared all of these measures. In this paper, we investigate how these different measures are related to each other. We classify the main empirical measures of firm opacity into three broad categories; those based on behavior of stock returns, those based on information produced by intermediaries such as stock analysts, and those based on market microstructure. Since opacity of depositary institutions has been focus of a number of recent studies, it provides a natural laboratory to test these different proxies. Our results show that while there is only a limited correlation among various opacity measures, most of them indicate that banks are less opaque compared to non-banks. Our failure to find consistent evidence on bank opacity suggests that the results are highly dependent on the opacity measure employed by the researcher. To measure the effectiveness of various opacity proxies, we use credit rating initiation as a significant shock to the firm information environment. We adopt a difference-in-difference approach, by comparing newly rated firms with “unchanged” firms, i.e. already rated or unrated firms. Our results suggest that the number of analysts and the price impact (as measured by the Amihud’s (2002) ratio) appear to be the most reliable proxies for firm opacity.