Annalisa Fabretti

Delegated portfolio management with socially responsible investment constraints

We consider the problem of how to establish compensation for a portfolio manager who is required to restrict the investment set, for example, because of socially responsible screening. This is a problem of delegated portfolio management, where the reduction of investment opportunities to the subset of sustainable assets involves a loss in expected earnings for the portfolio manager, compensated by the investor through an extra bonus on the realized return. Under simple assumptions on the investor, manager and market, we compute the optimal bonus as a function of the managers risk aversion and expertise, and of the impact of portfolio restriction on the mean-variance efficient frontier.

Delegated portfolio management under ambiguity aversion

We examine the problem of setting optimal incentives for a portfolio manager hired by an investor who wants to induce ambiguity-robust portfolio choices with respect to estimation errors in expected returns. Adopting a worst-case max-min approach we obtain the optimal compensation in various cases where the investor and the manager, adopt or relinquish an ambiguity averse attitude. We also provide examples of applications to real market data.

Calibration of an Agent Based Model for Financial Markets

Agent based model are very widely used in different discipline. In financial markets they can explain very well known features called stylized facts and fit statistical properties of data. For such a reason in predicting future price movements they could perform better than standard models using gaussianity. At this scope calibration and validation in order to choose the model and the model parameters are very essential issues. However calibrating such models is a hard issue to tackle and not yet very well considered in literature. The present paper presents the attempt to calibrate the Farmer Joshi model by a Nelder Mead algorithm with threshold. Different objective function are considered in order to identify the best choice.

An Agent-Based Model to Study the Impact of Convex Incentives on Financial Markets

We investigate by means of agent-based simulations the influence of convex incentives, e.g. option-like compensation, on financial markets. We propose an agent based model already developed in Fabretti et al (2015), where the model was build with the aim of studying convex contract effect using the results of a laboratory experiment performed by Holmen et al. (2014) as benchmark. Here we replicate their results studying prices dynamics, volatility, volumes and risk preference effect. We show that convex incentives produces higher prices, lower liquidity and higher volatility when agents are risk averse, while, differently from Fabretti et al (2015), their effect is less evident if agents are risk lovers. This appears related to the fact that prices in the long run converge more likely to the equilibrium when agents are risk averse.

Convex Incentives in Financial Markets: An Agent-Based Analysis

This paper uses agent-based simulation to analyze how financial markets are affected by market participants with convex incentives, e.g. option-like compensation. We document that convex incentives are associated with (i) higher prices, (ii) larger variations of prices, and (iii) larger bid-ask spreads. We conclude that convex incentives may lead to decreased stability of financial markets. Our analysis suggests that the decreased stability is driven by the fact that convex incentives pushes agents towards more extreme decisions. Furthermore, while risk preferences affect agent behavior if they have linear incentives, the effect of risk preferences vanishes with convex incentives.

Active Management of Socially Responsible Portfolios

We consider the problem of an investor who wishes to allocate her wealth according to some socially responsible (SR) criteria. The reduction in the investment set opportunity produces a cost for the investor which we call “cost of sustainability”. On the other hand, the investor is aware that the financial performances of some actively managed SR portfolios may be better or comparable to those of conventional portfolios. For this reason, the investor decides to entrust her wealth to a portfolio manager able to produce accurate forecasts of SR asset returns. The investor’s task is threefold: (a) hiring a manager who can offset the cost of sustainability; (b) setting a bonus to compensate the manager for the investment restriction; (c) attracting only the best and more motivated managers. We provide a solution to these problems and apply our results to data consisting of S&P500 firms screened by KLD scores.

Markov chain analysis in agent-based model calibration by classical and simulated minimum distance

Agent-based models are nowadays widely used; however, their calibration on real data still remains an open issue which prevents to exploit completely their potentiality. Rarely such a kind of models can be studied analytically; more often they are studied by simulation. Among the problems encountered in ABM calibration, the choice of the criteria to fit can appear arbitrary. Markov chain analysis can come through to identify a standard procedure able to face this issue. Indeed, Izquierdo et al. (J Artif Soc Soc Simul 12(16):1–6, 2009) show that many computer simulation models can be represented as Markov chains. Exploiting such an idea classical minimum distance and its simulated counterpart, i.e., simulated minimum distance, are discussed theoretically and applied to Kirman model, which can be reformulated as a Markov chain. Comparison with approximate Bayesian computation is also addressed.