Manuel Ammann

Corporate Governance and Firm Value: International Evidence

In this paper, we investigate the relation between firm-level corporate governance and firm value based on a large and previously unused dataset from Governance Metrics International (GMI) comprising 6,663 firm-year observations from 22 developed countries over the period from 2003 to 2007. Based on a set of 64 individual governance attributes we construct two alternative additive corporate governance indices with equal weights attributed to the governance attributes and one index derived from a principal component analysis. For all three indices we find a strong and positive relation between firm-level corporate governance and firm valuation. In addition, we investigate the value relevance of governance attributes that document the companies’ social behavior. Regardless of whether these attributes are considered individually or aggregated into indices, and even when “standard�? corporate governance attributes are controlled for, they exhibit a positive and significant effect on firm value. Our findings are robust to alternative calculation procedures for the corporate governance indices and to alternative estimation techniques.

Are Convertible Bonds Underpriced?: An Analysis of the French Market

We investigate the pricing of convertible bonds on the French convertible bond market using daily market prices for a period of 18 months. Instead of a firm-value model as used in previous studies, we use a stock-based binomial-tree model with exogenous credit risk that accounts for all important convertible bond specifications and is therefore well suited for pricing convertible bonds. The empirical analysis shows that the theoretical values for the analyzed convertible bonds are on average more than 3% higher than the observed market prices. This result applies to both the standard convertibles and the exchangeable bonds in our sample. The difference between market and model prices is greater for out-of-the-money convertibles than for at- or in-the-money convertibles. A partition of the sample according to maturity indicates that there is a positive relationship between underpricing and maturity with decreasing mispricing for bonds with shorter time to maturity.

Tracking Error and Tactical Asset Allocation

We report results from our investigation of the relationship between statistical measures of tracking error and asset allocation restrictions expressed as admissible weight ranges. Tracking errors are typically calculated as annualized second moments of return differentials between a portfolio and a benchmark. In practice, however, constraints on tactical deviations from benchmark weights are often imposed on the portfolio manager to ensure adequate tracking. Simulating various investment strategies subject to such constraints, we illustrate how the size of acceptable deviations from the benchmark relates to the statistical tracking error. An example based on actual market data indicates that imposing fairly large tactical asset allocation ranges produces surprisingly small tracking errors. We also found that TAA restrictions should restrict not only the tactical ranges of the individual asset classes but also, and perhaps even more importantly, the tracking of the individual asset classes. We report our investigation of the relationship between measures of statistical tracking error and asset allocation restrictions expressed as admissible deviations from benchmark weights. This relationship is of significant practical relevance to analysts, investment strategists, and risk managers. The reason is that these practitioners often think in terms of tracking volatility or correlation whereas the actual allocation decisions by portfolio managers tend to be guided by recommendations and constraints on the weights of assets or asset classes in their portfolios. Typically, tracking errors are calculated either as second moments of return differentials between the tracking portfolio and some benchmark or as correlation coefficients. In practice, however, constraints on tactical deviations from benchmark weights are usually imposed on a portfolio manager to ensure adequate tracking and limit the active part of portfolio risk. These bounds define the maximum percentages by which the actual portfolio weights may deviate from the corresponding weights in the benchmark. For example, for an equally weighted benchmark portfolio consisting of five asset classes with strategic weights of 20 percent for each class, an active management contract might allow the portfolio manager to deviate from the weights within a range of ±10 percent for each class. Such a range implies a certain tracking-error range, so the active manager has the chance to earn abnormal portfolio returns. We took a simulation approach to quantifying the relationship between statistical tracking-error measures and constraints on weights: For given tactical asset allocation (TAA) ranges, we identified admissible tactical portfolio combinations and simulated for these portfolios return series based on historical data. We then calculated the correlations and tracking errors for the portfolios as though they had been managed according to various asset allocation strategies. The simplest allocation was static; the allocation remained unchanged for the entire observation period. We also studied three dynamic TAA strategies: random rebalancing each month, rebalancing based on return trends, and rebalancing so as to maximize tracking error while still remaining within the weight constraints. In addition, we investigated allocation strategies in which the individual asset classes were managed actively. In this case, the tracking error arose not only from the tactical asset-class allocation but also from the imperfect asset-class tracking. The asset classes came from international stock and bond markets. The benchmark portfolio for the main study consisted of U.S. stocks, European stocks, Japanese stocks, U.S. bonds, and Canadian bonds. The reference currency is the U.S. dollar, and the full period is 1985 to mid-1998. To test the robustness of the results, we also applied the analysis to different time periods and an alternative benchmark portfolio. The robustness tests confirmed our main findings. For given tactical ranges, we found that the lowest attainable correlation coefficients between the tactical portfolios and the benchmark are surprisingly high. Consequently, imposing a lower bound for admissible correlation between tracking portfolio and benchmark may not prevent portfolio managers from holding portfolios that differ greatly from their benchmarks in terms of asset-class weights. We also found that tracking errors and correlation coefficients are very sensitive to the tracking accuracy of the individual asset classes. Thus, restrictions imposed to control the deviation of TAA strategies from benchmarks should not only restrict the weighting of the individual asset classes (i.e., the determination of tactical ranges), as is often done in practice, but should also control the error arising from the tracking of the individual asset classes. We also applied our tracking-error analysis to the valuation of performance fees. Allowing for a higher tracking error increases the value of a performance fee contract to a portfolio manager because of the greater flexibility for the implementation of active strategies and thus the higher potential rewards. For given tactical ranges, we identified the highest corresponding tracking error in our simulation results and then used a pricing model for exchange options to compute the value of the performance fee contract. We found that the value of the contract is roughly proportional to the width of the tactical allocation ranges.

Product Market Competition, Corporate Governance, and Firm Value: Evidence from the EU Area

This paper investigates whether the valuation effect of corporate governance depends on the degree of competition in the companies’ product markets in a large international sample covering 14 countries from the European Union (EU). Besides providing external validity of previous US-centred studies, this paper uses more comprehensive and reliable measures of both product market competition and corporate governance. Consistent with the hypothesis that product market competition acts as a substitute for corporate governance as competitive pressure imposes discipline on managers to maximise firm value, our results show that corporate governance significantly increases firm value in non-competitive industries only. When investigating the channels through which firm value may be increased, we find that good governance for firms in non-competitive industries leads them to have more capital expenditures, spend less on acquisitions, and be less likely to diversify. Our results are robust to a large number of robustness checks including the use of alternative measures of competition and governance, as well as using alternative regression specifications.

Hedge Fund Characteristics and Performance Persistence

In this paper, we investigate the performance persistence of hedge funds over time horizons between 6 and 36 months based on a merged sample from the Lipper/TASS and CISDM databases for the time period from 1994 to 2008. Unlike previous literature, we use a panel probit regression approach to identify fund characteristics that are significantly related to performance persistence. We then investigate the performance of two-way sorted portfolios where sorting is based on past performance and one of the additional fund characteristics identified as persistence-enhancing in the probit analysis. We find statistically and economically significant performance persistence for time horizons of up to 36 months. Although we identify several fund characteristics that are strongly correlated with the probability of observing performance persistence, we find only one fund characteristic, a strategy distinctiveness index that attempts to measure manager skills and the uniqueness of the hedge fund’s trading strategies, to have the ability to systematically improve performance persistence up to a time horizon of 24 months. The economic magnitude of this improvement amounts to a sizeable increase in alpha by approximately 4.0% and 2.3% p.a. for annual and biennial rebalancing, respectively.

Credit Risk Valuation

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Analyzing Active Investment Strategies

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Performance of Funds of Hedge Funds

Investors want to know the trading strategies an asset manager pursues to generate excess returns. Tracking error variance can be an alternative approach for analyzing the trading strategies used in active investing. Two decompositions of TEV may be used as a measure of activity in identifying different investment strategies. A simulation study testing the performance of different methods of strategy analysis demonstrates how a TEV decomposition can add information. When investment strategies involve random components, TEV decomposition delivers important additional information that traditional return decomposition methods cannot uncover.

The Effect of Market Regimes on Style Allocation

This article investigates the performance of funds of hedge funds. A variety of methods is used to shed more light on different performance aspects. Multi-factor and single-factor models are used to explain excess fund of hedge funds returns. Cross-sectional regression analyses indicate that larger funds of hedge funds exhibit higher returns, lower standard deviations, higher Sharpe ratios, and higher alphas based on a multi-factor model. Performance persistence in fund of hedge funds returns is tested with a comprehensive relative efficiency measure based on data envelopment analysis. A rank correlation test based on the efficiency measure does not indicate any statistically significant performance persistence.

Risk Factors for the Swiss Stock Market

We analyse time-varying risk premia and the implications for portfolio choice. Using Markov Chain Monte Carlo (MCMC) methods, we estimate a multivariate regime-switching model for the Carhart (1997) four-factor model. We find two clearly separable regimes with different mean returns, volatilities, and correlations. In the High-Variance Regime, only value stocks deliver a good performance, whereas in the Low-Variance Regime, the market portfolio and momentum stocks promise high returns. Regime-switching induces investors to change their portfolio style over time depending on the investment horizon, the risk aversion, and the prevailing regime. Value investing seems to be a rational strategy in the High-Variance Regime, momentum investing in the Low-Variance Regime. An empirical out-of-sample backtest indicates that this switching strategy can be profitable, but the overall forecasting ability for the regime-switching model seems to be weak compared to the iid model.