E-Finance Lab Team

This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profi ts systematically in a betting market. We present the results of an empirical study that uses data from 678–837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting-based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule-based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state-owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profi ts if punters exploited the information from prediction markets and bet only on a selected number of games.

Newly launched products in the consumer goods and services markets show high failure rates. In order to reduce the failure rates, companies can integrate innovative and knowledgeable customers, the so called ‘lead users’, into the new product development process. However, the detection of such lead users is difficult, especially in consumer product markets with very large customer bases. A new and potentially valuable approach towards the identification of lead users involves the use of Virtual Stock Markets, which have been proposed and applied for political and business forecasting, but not for the identification of experts such as lead users. The basic concept of Virtual Stock Markets is 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 Virtual Stock Market elicits and aggregates the assessments of its participants concerning future market developments. Virtual Stock Markets might also serve as a feasible instrument to filter out lead users, primarily for the following two reasons. Firstly, a "self-selection effect" might occur because sophisticated consumers with a higher involvement in the product of interest decide to participate in Virtual Stock Markets. Secondly, a "performance effect" is likely to arise because well-performing participants in Virtual Stock Markets show a better understanding of the market than their (already self-selected) fellow participants. So far, only limited information exists about these two effects and their relation to lead user characteristics. The goal of this article is to analyze the feasibility of Virtual Stock Markets for the identification of lead users. The results of our empirical study show that Virtual Stock Markets can be an effective instrument to identify lead users in consumer products markets. Furthermore, the results show that not all lead users perform well in Virtual Stock Markets. Hence, Virtual Stock Markets allow identifying lead users with superior abilities to forecast market success.

Using the wisdom of the crowds for generating and evaluating new product ideas.

The interactive nature of the Internet promotes collaborative business models (e.g. auctions) and facilitates information sharing via social networks. The Name-Your-Own-Price (NYOP) mechanism is such a collaborative business model where prospective buyers bid for a specific product against an unrevealed threshold price set by the seller. Prospective buyers have strong incentives to learn more about the secret threshold price in NYOP markets, thereby relying on their own network of friends or digital networks of users with similar interest and information needs. This has led to the development of communities sharing the latest experience with bids on products in these markets. Information sharing in digital environments, both person-to-person or via communities can change bidding behavior and can thus have a strong impact on seller profit. However, previous research has so far neglected information diffusion in NYOP markets. The goal of this paper is to determine the impact of information diffusion on bidding behavior and seller profit in NYOP markets for different communication structures in social networks. Thereby, we first develop a normative model for the effect of shared information on bidding behavior. We then empirically test the validity of our model to represent actual behavior in a laboratory experiment with induced valuations. We determine the effect of information diffusion on seller profit for various structures of communication links in social networks via an agent-based simulation. Finally, we test the impact of information diffusion on bidding behavior and seller profit in a field setting with real purchases.