Internet-based virtual stock markets for business forecasting

Internet-based virtual stock markets for business forecasting (2003)

Cached

Download Links

[www.hsxresearch.com]

by Martin Spann , Bernd Skiera

Citations:

19 - 0 self

Versions

Version History

Metadata Version 1

Datum	Value	Source
TITLE	Internet-based virtual stock markets for business forecasting	INFERENCE
AUTHOR NAME	Martin Spann	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Business and Economics, Johann Wolfgang Goethe-University Frankfurt am Main,	SVM HeaderParse 0.2
AUTHOR ADDR	Mertonstrasse 17, 60054 Frankfurt am Main, Germany	SVM HeaderParse 0.2
AUTHOR NAME	Bernd Skiera	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Business and Economics, Johann Wolfgang Goethe-University Frankfurt am Main,	SVM HeaderParse 0.2
AUTHOR ADDR	Mertonstrasse 17, 60054 Frankfurt am Main, Germany	SVM HeaderParse 0.2
ABSTRACT	The application of Internet-based virtual stock markets (VSMs) is an additional approach that can be used to predict short- and medium-term market developments.The basic concept involves bringing a group of participants together via the Internet and allowing them to trade shares of virtual stocks.These stocks represent a bet on the outcome of future market situations, and their value depends on the realization of these market situations.In this process, a VSM elicits and aggregates the assessments of its participants concerning future market developments.The aim of this article is to evaluate the potential use and the different design possibilities as well as the forecast accuracy and performance of VSMs compared to expert predictions for their application to business forecasting.After introducing the basic idea of using VSMs for business forecasting, we discuss the different design possibilities for such VSMs.In three real-world applications, we analyze the feasibility and forecast accuracy of VSMs, compare the performance of VSMs to expert predictions, and propose a new validity test for VSM forecasts.Finally, we draw conclusions and provide suggestions for future research.	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
CITATIONS	33 found	ParsCit 1.0