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.

Abstract—Traditional movie gross predictions are based on numerical and categorical movie data from The Internet Movie Database (IMDB). In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help people to predict movie grosses. By analyzing two different models (regression and k-nearest neighbor models), we find models using only news data can achieve similar performance to those using IMDB data. Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant. I.

This paper surveys the field of financial analysis using news data, and also our own considerable work regarding movie gross analysis to show the predictive power of news data. Since the 1990s, linguistic sources such as news have been continuously proved to carry extra and meaningful information beyond traditional quantitative finance data, and thus they can be used as predictive indicators in finance. In the first half of this paper, we analyze and compare important previous research in terms of targets, methodologies, and results. In addition, we discuss financial news analysis with Lydia, a system for large-scale news analysis. In the second half of this paper, based on the news data generated by Lydia, we build three different models (regression, piecewise, and k-nearest neighbor models) and achieve good prediction results for movie grosses. Therefore, we believe that Lydia data could be used in forecasting other financial variables as well, for example, stock returns, volatilities, trading volumes, or firm earnings.

Industry symposium for their comments and insights. The first author would also like to