We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the...

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We analyze the learning process of purely and impurely altruistic...

This chapter surveys research on agent-based models used in finance. It will concentrate on models where the use of computational tools is critical for the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.

Dieser Beitrag gibt einen Überblick über Ansatzpunkte und Möglichkeiten zur Integration von Lernprozessen in volkswirtschaftliche Fragestellungen. Es werden alternative Methoden vorgestellt, individuelle aber auch gesellschaftliche Lernenvorgänge in ökonomischen Modellen zu erfassen.

In rare event simulation, we look for estimators such that the relative accuracy of the output is 'controlled' when the rarity is getting more and more critical. Different robustness properties have been defined in the literature, that an estimator is expected to satisfy. Though, those...

We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility...

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy...