Search

This article discusses the notion of perceived predation (the idea that a vis-a-vis wants us harm, by surprise) and reveals some of the key findings following a longitudinal study conducted with three different groups in 2011. The study shows that untrained people invited to negotiate financial transactions with another party naturally tend to adopt a strong stance, one that can be perceived as predatory, whereas those with training in negotiation adopt somewhat of a more conciliatory attitude. The important implication of such findings is that portfolio managers who attempt to maximize their client’s wealth in an unrestricted manner may well, consciously or not, turn against the very interests of those clients by using techniques that minimize perceived predation.

The findings presented in this article improve the existing methods for estimating financial models of returns and especially for estimating the parameters that are relevant for portfolio managers, such as the Jensen alpha, a popular measure for stock selection, and beta, a well-known systemic risk measure. The authors focus on the presence of measurement errors in these models. For instance, the risk factors in the Fama and French models or the market model are biased by measurement, or specification, errors. These measurement errors are related to the use of proxies for measuring risk factors, such as the risk premium, and other risk factors, which are approximated by mimicking portfolios. To tackle these specification problems, the authors propose new Hausman-based estimators lying on cumulants optimal instruments. Using these newly generated strong instruments in a generalized method of moments (GMM) setting, the authors obtain new GMM estimators that they call GMM-C and its homologue GMM-hm. They also extend the methodology to the standard two-stage least squares (TSLS) framework using new optimally generated instruments. These procedures improve the existing method of moments for estimating, or calibrating, the parameters of a financial model and, more generally, for treating the problem of endogeneity often encountered in financial studies. Moreover, this study leads to a new indicator that signals the presence of specification errors in financial models. The authors apply a battery of tests and estimators to a sample of 22 HFR hedge fund indices observed monthly over the period 1990–2005. Their tests reveal that specification errors bias parameter estimation of financial models of returns and that the ranking of hedge funds is very sensitive to the choice of estimators.

This article proposes new estimations of conditional versions of the Fama-French model in order to deal with the alpha puzzle that emerges from hedge fund studies. Its originality lies in resorting to a new form of GMM, the GMM-C, which uses the cumulants of the explanatory variables as instruments in doing the estimations of each hedge fund strategy in panel. The estimations reveal that the new estimator is robust and is preferable to the usual OLS estimation of conditional models, which does not account for specification errors as does the GMM-C. This article also shows that the success of a conditional model in solving the alpha puzzle is related to a judicious choice of the conditioning information.

The authors consider the dynamic market strategies of hedgefunds by using the Kalman filter. There are many studies onthe behavior of conditional alphas and betas of hedge funds,but the dynamics of these coefficients is studied within thetraditional regression framework: The resulting conditionalalphas and betas are hence for a great part arbitrary becausethey do not result from a dynamic optimization process. Inthis article, the authors try to correct this problem in partby writing transition equations for the alpha and the betawhose explanatory variables are market financial variables.The alphas of hedge fund indices appear quite difficult tocontrol, a result in line with the market efficiency hypoth-esis. Besides, the betas are much more controlable, their reac-tion to market variables being significant.

This article uses a new set of instruments based on higher statistical moments to discard the specification errors that might be present in the Fama and French model. It shows that the usual instruments perform quite poorly in comparison to higher moments. It estimates the Fama and French model on a sample of 22 HFR hedge funds indices and 111 HFR individual hedge funds over the period 1990-2005. To do so, it compares many instrumental variables methods as the two-stage least squares and the generalized method of moments. The results show that there are few problems of specification errors on the side of the indices but this level of aggregation hides the errors. Indeed, the estimations of the sample of 111 funds reveal specification errors for the loadings of the market premium and the factor SMB which seem understated. The message to retain is that the individual investor must consider specification errors when modelling the returns of hedge funds as he does not buy the indices but the stocks of individual hedge funds. This article shows an investor how to correct the traditional measures of performance, which are alphas and factor loadings, to guide him towards a better decision process.

The authors propose a new framework for analyzing past financial crises by building on the concept of predatory marketing. They argue that the financial world can be seen as an ecosystem in which the provider of financial services acts as a financial predator and the client as prey. This way of describing the financial world may provide further explanations as to why financial crises happen. The authors propose possible solutions to limit financial fraud.