Daniel Giamouridis

Revisiting Mutual Fund Performance Evaluation

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark results in different measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence fund performance evaluation utilizing stock selection and timing measures that incorporate the self-reported benchmark. We introduce a new factor exposure based approach for measuring the – static and dynamic – timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be misstating skill because they ignore the managers’ self-reported benchmark in the performance evaluation process.

Predicting European Takeover Targets

This article extends the Palepu (1986) acquisition likelihood model by incorporating measures of a technical nature, e.g. momentum, trading volume as well as a measure of market sentiment. We use the proposed model to predict takeover targets in a large sample of European and cross-border merger and acquisition deals and validate its performance on an in- and out-of-sample basis. The robustness of the proposed model is investigated across several dimensions. In addition we explore the ability of the model to form the basis of successful takeover timing investment strategies. The results of our empirical analysis suggest that the proposed model predicts European takeover targets with relatively high accuracy and is able to determine portfolios that earn significant returns which are not explained by conventional risk factors.

The Relation Between Return and Volatility in the Commodity Markets

In this article, results indicate that the relation between return and volatility in the commodity markets is inverse of that observed in the stock markets. The implication is that if the commodity market returns are negatively correlated with those of traditional financial assets, the introduction of commodities in those portfolios may result in the diversification of risk. This may also allow fund managers to hedge their investment portfolios with commodities, thus avoiding the use of more complicated instruments such as options.

Inferring option-implied investors' risk preferences

Risk preference functions across the wealth domain are estimated from option prices and asset realized returns using: (a) a semiparametric probability model, the Edgeworth Series Expansion model, and (b) a new data set consisting of eurodollar and WTI oil markets’ data. The empirical preference functions are examined and found consistent with the market conditions of the period under study. The risk aversion estimates are also similar to these found by alternative methodologies.

Asymptotic Distribution Expansions in Option Pricing

The previous article compares different methods for estimating the empirical probability distribution for asset returns. This article looks at methods for approximating an implied risk neutral density. Constraining it to be lognormal is common practice, of course, but does not give a very good fit to actual option prices. A unique feature of this article is to examine American options, for which the implied densities must produce prices that satisfy certain bounds, without being pinned down to exact values. Some of the same procedures as described by Jensen and Poulsen, like Hermite approximation, have been explored, but Giamouridis and Tamvakis find that these techniques and several others, including a mixture of lognormals, do not work as well as an Edgeworth Series Expansion (ESE). The ESE technique relaxes the constraints on skewness and kurtosis imposed by the lognormal, without a proliferation of parameters to estimate. The ESE model is then compared in terms of performance to methods examined in 12 other studies, and is found to behave comparably, with densities that are close to a median structure across models.

Some Insiders are Indeed Smart Investors

Not all insiders are the same; some are more effective than others in processing the information they have access to, and invest their own wealth accordingly. We used a database with transactions from the U.K. market to identify insiders with superior market timing abilities. For the period 1994 to 2006 we showed that informative insider trades can be identified ex-ante through certain characteristics of the transactions and the firm itself. Moreover we showed how outsiders could benefit from this information. We created a long-only portfolio strategy that generated an economically and statistically significant return. In addition, we showed that the insider transactions signal can be used to construct a satellite strategy that enhances the returns of a typical quant investment portfolio.

A Risk-Oriented Model for Factor Timing Decisions

Alpha factors are built to perform well over time, on average. There are instances when they do not, and knowing these instances ex ante can be a significant source of added value for investors. The authors argue that factor failure is a function of its broad risk, and propose appropriate variables to measure it. They adopt a nonparametric model that predicts instances of likely factor failure, based on these variables, demonstrating that an implementable dynamic strategy based on our analysis generates a reward-to-risk ratio approximately four times that of a static approach, and about one and a half times that of an alternative dynamic approach based on momentum.

Risk Reduction in Style Rotation

Dupleich Ulloa, Giamouridis, and Montagu investigate the potential improvement in the implementation of style rotation strategies by techniques addressing estimation errors. They select two approaches that have recently stood out in the statistics and econometrics literature and have been applied to portfolio construction. One approach builds on regularization methods, addressing estimation error by focusing on the weights of the constructed portfolios.The second method pools forecasts that are obtained across different observation windows, thus focusing on minimizing estimation error in the moments of the return distribution that may arise due to structural breaks. The authors conclude that overall benefits are derived by foregoing naive approaches, which in their dataset can be as significant as an improvement in the Information Ratio of about 54%; that is, improving from 0.65 (naive) to approximately 1 (dynamic).

Hedge Fund Return Predictability in the Presence of Model Uncertainty and Implications for Wealth Allocation

This paper studies hedge fund return predictability in a multivariate setting. Our research design and analysis is motivated by the empirical observations that a specific forecasting model that is going to perform well is not known ex-ante and that modelling time varying return covariances/correlations improves our ability to construct optimal hedge fund portfolios. We employ a multivariate GARCH specification to model time-varying covariances/correlations of hedge fund returns and we develop a stochastic search algorithm to compute posterior model probabilities for alternative predictive specifications. Our empirical analysis indicates that introducing dynamic covariance/correlation modeling improves the out-of-sample performance of optimal hedge fund portfolios. Moreover, introducing predictive factors provides incremental additional portfolio outperformance.

The role of commodities in strategic asset allocation

We shed new light on the role of commodities in asset allocation for investors with and without liabilities who (a) believe that asset returns are time varying and predictable (b) have short and long term horizons and (c) have access, in addition to a standard passive commodity portfolio, to commodity portfolios based on equal weights, momentum and the basis. We document significant benefits, in- and out-of-sample, from investing in factor-based commodity portfolios. We also confirm and extend the evidence on the negative role of commodity investments based on commonly used commodity benchmarks for investors with long horizons and liabilities.