Optimal Investment Horizons

In stochastic finance, one traditionally considers the return as a competitive measure of an asset, {\it i.e.}, the profit generated by that asset after some fixed time span $\Delta t$, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established that the distribution of returns exhibits ``fat tails'' indicating that large returns occur more frequently than what is expected from standard Gaussian stochastic processes (Mandelbrot-1967,Stanley1,Doyne). Instead of estimating this ``fat tail'' distribution of returns, we propose here an alternative approach, which is outlined by addressing the following question: What is the smallest time interval needed for an asset to cross a fixed return level of say 10%? For a particular asset, we refer to this time as the {\it investment horizon} and the corresponding distribution as the {\it investment horizon distribution}. This latter distribution complements that of returns and provides new and possibly crucial information for portfolio design and risk-management, as well as for pricing of more exotic options. By considering historical financial data, exemplified by the Dow Jones Industrial Average, we obtain a novel set of probability distributions for the investment horizons which can be used to estimate the optimal investment horizon for a stock or a future contract.Comment: Latex, 5 pages including 4 figur

Inverse Statistics in Economics : The gain-loss asymmetry

Inverse statistics in economics is considered. We argue that the natural candidate for such statistics is the investment horizons distribution. This distribution of waiting times needed to achieve a predefined level of return is obtained from (often detrended) historic asset prices. Such a distribution typically goes through a maximum at a time called the {\em optimal investment horizon}, $\tau^*_\rho$, since this defines the most likely waiting time for obtaining a given return $\rho$. By considering equal positive and negative levels of return, we report on a quantitative gain-loss asymmetry most pronounced for short horizons. It is argued that this asymmetry reflects the market dynamics and we speculate over the origin of this asymmetry.Comment: Latex, 6 pages, 3 figure

Pilot implementation Driven by Effects Specifications and Formative Usability Evaluation

This chapter reports on the usability-engineering work performed throughout the pilot implementation of an Electronic Healthcare Record (EHR). The case describes and analyzes the use of pilot implementations to formatively evaluate whether the usability of the EHR meets the effects specified for its use. The project was initiated during the autumn of 2010 and concluded in the spring of 2012. The project configured and implemented an EHR at a Maternity ward at one hospital located in a European region and then transferred this system to another ward at another hospital in the same region. DOI: 10.4018/978-1-4666-4046-7.ch010 Copyright ©2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Pilot Implementation Driven by Effects Specification