A computational economics model of managerial compensation is presented. Risk-averse managers are simulated, and shown to adopt more risk-taking under the influence of stock options. It is also shown that stock options can both help a new entrant compete in an established market; and can help...

When running agent-based simulations using ready-made components, one usually faces heterogenity problems both for the agents implementation and for the underlying platform. To circumvent these kind of hindrances, we introduce a wrapper technique for mapping the functionality of agents living in...

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling and flexible specification of design parameters.(...)

Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and...

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector...

One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density...

Motivated by previous findings that discretization of financial time series can effectively filter the data and reduce the noise, this experimental study compares the trading performance of predictive models based on different modelling paradigms in a realistic setting. Different methods ranging...