REVIEW OF PREDICTION MARKET RESEARCH: GUIDELINES FOR INFORMATION SYSTEMS RESEARCH

This paper presents an analysis of prediction market (PM) research relevant to information systems. Prediction markets are (online) markets are usually not traded on existing exchanges but on future events. As an emerging research area, prediction markets have received considerable attention from several disciplines, including economics, politics, marketing, computer science, electronic commerce and etc. In information systems research, however, they have been largely ignored. This study reviewed 93 academic articles concerning prediction markets. The analysis reveals that an increasing volume of PM research has been conducted, and that research themes of these studies can be categorized into three groups, namely general introduction, theoretical work, and PM applications. Building upon this work, we argue for the importance of future prediction market research and suggest potential research targets for IS researchers

Making Business Predictions by Combining Human and Machine Intelligence in Prediction Markets

Computers can use vast amounts of data to make predictions that are often more accurate than those by human experts. Yet, humans are more adept at processing unstructured information and at recognizing unusual circumstances and their consequences. Can we combine predictions from humans and machines to get predictions that are better than either could do alone? We used prediction markets to combine predictions from groups of people and artificial intelligence agents. We found that the combined predictions were both more accurate and more robust than those made by groups of only people or only machines. This combined approach may be especially useful in situations where patterns are difficult to discern, where data are difficult to codify, or where sudden changes occur unexpectedly

Prediction Markets:A literature review 2014

In recent years, Prediction Markets gained growing interest as a forecasting tool among researchers as well as practitioners, which resulted in an increasing number of publications. In order to track the latest development of research, comprising the extent and focus of research, this article provides a comprehensive review and classification of the literature related to the topic of Prediction Markets. Overall, 304 relevant articles, published in the timeframe from 2007 through 2013, were identified and assigned to a herein presented classification scheme, differentiating between descriptive works, articles of theoretical nature, application-oriented studies and articles dealing with the topic of law and policy. The analysis of the research results reveals that more than half of the literature pool deals with the application and actual function tests of Prediction Markets. The results are further compared to two previous works published by Zhao, Wagner and Chen (2008) and Tziralis and Tatsiopoulos (2007a). The article concludes with an extended bibliography section and may therefore serve as a guidance and basis for further research. (250 WORDS

DECISION SUPPORT IN CAR LEASING: A FORECASTING MODEL FOR RESIDUAL VALUE ESTIMATION

The paper proposes a methodology to support pricing decisions in the car leasing industry. In particular, the price is given by the monthly fee to be paid by the lessee as compensation for using a car over some contract horizon. After contract expiration, lessors are obliged to take back the vehicle, which will then be sold in the used car market. Therefore, lessors require an accurate estimate of cars’ residual values to manage the risk inherent to their business and determine profitable prices. We explore the organizational and technical requirements associated with this forecasting task and develop a prediction model that complies with identified application constraints. The model is rigorously tested within an empirical study and compared to established benchmarks. The results obtained in several experiments provide strong evidence for the proposed model being effective in generating accurate predictions of cars’ residual values and efficient in requiring little user intervention

Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations

We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud. Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features. We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers. We perform a naive evaluation that assesses the classifiers’ performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures. The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection. We discuss implications for supervisory authorities, industry, and individual users

Information Market Based Decision Fusion

In this paper, we present Information Market based Fusion (IMF), a novel, multi-classifier combiner method for decision fusion that is based on information markets. IMF does not require training or a static ensemble composition, adjusts to changes in base-classifier accuracy, provides incentives for the base-classifiers to present truthful information, and integrates with existing multi-agent system (MAS) coordination mechanisms. We compare the effectiveness of two different IMF implementations to Majority (MAJ), Average (AVG), and Weighted Average (WAVG) schemes, using computational experiments involving 16 datasets from the UCI Machine Learning Repository and 20 different base-classifiers from Weka

Information Market-Based Decision Fusion

Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms that are committing financial fraud. Multiclassifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper, we present information market-based fusion (IMF), a novel multiclassifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market-based approach, IMF can adjust to changes in base-classifier performance without requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, majority, average, and weighted average; for high positive ratios, IMF outperforms majority and performs on par with average and weighted average. When the true classes of all objects are revealed, IMF outperforms weighted average and majority and marginally outperforms average.multiclassifier combination, decision fusion, information markets, software agents