Christian Slamka
Goethe University Frankfurt
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Featured researches published by Christian Slamka.
Negotiation, Auctions, and Market Engineering. International Seminar, Dagstuhl Castle, Germany, November 12-17, 2006, Revised Selected Papers. Ed.: H. Gimpel | 2008
Stefan Luckner; Jan Schröder; Christian Slamka
Accurate forecasts are essential in many areas such as business and sports forecasting. Prediction markets are a promising approach for forecasting future events and are increasingly used to aggregate information on particular future events of interest such as elections, sports events, and Oscar winners. In this paper, we present the results of an empirical study that compares the forecast accuracy of a prediction market for the FIFA World Cup 2006 to predictions derived from the FIFA world ranking and to a random predictor. We find that prediction markets for the FIFA World Cup outperform predictions based on the FIFA world ranking as well as the random predictor in terms of forecast accuracy.
IEEE Transactions on Engineering Management | 2013
Christian Slamka; Bernd Skiera; Martin Spann
The use of prediction markets (PMs) for forecasting is emerging in many fields because of its excellent forecasting accuracy. However, PM accuracy depends on its market design, including the choice of market mechanism. Standard financial market mechanisms are not well suited for small, usually illiquid PMs. To avoid liquidity problems, automated market makers (AMMs) always offer buy and sell prices. However, there is limited research that measures the relative performance of AMMs. This paper examines the properties of four documented and applied AMMs and compares their performance in a large-scale simulation study. The results show that logarithmic scoring rules and the dynamic pari-mutuel market attain the highest forecasting accuracy, good robustness against parameter misspecification, the ability to incorporate new information into prices, and the lowest losses for market operators. However, they are less robust in case of noisy trading, which makes them less appropriate in environments with high uncertainty about true prices for shares.
Archive | 2012
Stefan Luckner; Jan Schröder; Christian Slamka; Markus Franke; Andreas Geyer-Schulz; Bernd Skiera; Martin Spann; Christof Weinhardt
This chapter presents previous fields of application of prediction markets. Subsequently, we discuss several field experiments in more detail. We start with a description of the 2006 FIFA World Cup prediction market called STOCCER. Moreover, we also present the political stock market PSM and the Australian Knowledge eXchange AKX. At the end of the chapter we give an outlook on how prediction markets can be used to generate and evaluate innovative products and services.
Archive | 2012
Stefan Luckner; Jan Schröder; Christian Slamka; Markus Franke; Andreas Geyer-Schulz; Bernd Skiera; Martin Spann; Christof Weinhardt
Before studying more advanced applications of prediction markets, it is necessary to gain a basic understanding of their key design elements. Like any market, prediction markets have to be designed and implemented very carefully in order to ensure that they are suitable for aggregating traders’ information (Weinhardt et al., 2003, Weinhardt et al., 2006a). The key design elements comprise the specification of contractstraded in a prediction market, the trading mechanism, and the incentivesprovided to ensure information revelation (Spann and Skiera, 2003). Moreover, diversity of information is required in order to provide a basis for trading (Wolfers and Zitzewitz, 2004). Heterogeneous expectations about the future among traders are desirable and the selection of tradersis thus also considered a key design issue (Tziralis and Tatsiopoulos, 2007b). The following subsections describe these design elements in more detail.
Archive | 2012
Stefan Luckner; Jan Schröder; Christian Slamka; Markus Franke; Andreas Geyer-Schulz; Bernd Skiera; Martin Spann; Christof Weinhardt
After a short history of prediction markets in Section 2.1, we define prediction markets as markets that run for “the primary purpose of aggregating information so that market prices forecast future events” (Berg and Rietz, 2003) in Section 2.2. The theoretic foundations of prediction markets are found in Hayek’s analysis of market-based economies and in the rose of information in Fama’s efficient market hypothesis in Section 2.3. The interaction between incentives for trade information revelation by trading transactions and the resulting adaption of prices is illustrated by a hands-on example in Section 2.4 on the operational principle of prediction markets.
Journal of Forecasting | 2012
Christian Slamka; Wolfgang Jank; Bernd Skiera
The Journal of Prediction Markets | 2008
Christian Slamka; Arina Soukhoroukova; Martin Spann
Archive | 2011
Stefan Luckner; Jan Schröder; Christian Slamka; Markus Franke; Andreas Geyer-Schulz; Bernd Skiera; Martin Spann; Christof Weinhardt
european conference on information systems | 2008
Christian Slamka; Stefan Luckner; Thomas Seemann; Jan Schröder
Export und Internationalisierung wissensintensiver Dienstleistungen. Hrsg.: H. Krcmar | 2010
Andreas Geyer-Schulz; Felix Kratzer; Stefan Luckner; Jan Schröder; Stefan Seifert; Bernd Skiera; Christian Slamka; Martin Spann; Christof Weinhardt