Prediction Markets: A Systematic Review and Meta-Analysis

Prediction markets (PM) have drawn considerable attention in recent years as a tool for forecasting events. Studies surveying and examining relevant the trends of PM using traditional approaches have been reported in the literature. However, research using meta-analysis to review Prediction markets systems is very limited in Management Information System (MIS). This paper aimed to fill this gap by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to study Prediction markets trends over the past decades. Our results are as follows. First, we find that shows that more than 64% of academic studies on Prediction markets are published in top journals such as Journal of the Association for Information Systems, Journal of Consumer Research and Information Systems Research. Second, we showed that Prediction markets applications can be can be divided into two groups: internal use PMS and general public usage. Finally, our significant meta-analysis result show that on average prediction markets is 79% more accurate than alternative forecast methods based

Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements

We examine the impact of price trends on the accuracy of forecasts from prediction markets. In particular, we study an electronic betting exchange market and construct independent variables from market price (odds) time series from 6,058 individual markets (a dataset consisting of over 8.4 million price points). Using a conditional logit model, we find that a systematic relationship exists between trends in odds and the accuracy of odds-implied event probabilities; the relationship is consistent with participants over-reacting to price movements. In particular, in different time segments of the market, increasing and decreasing odds lead, respectively, to under- and over-estimation of odds-implied probabilities. We develop a methodology to detect and correct the erroneous forecasts associated with these trends in odds in order to considerably improve the quality of forecasts generated in prediction markets

Vedonlyöntimarkkinoiden tehokkuus jalkapallossa : Tarkastelussa kertoimien ennustekyvyn kehitys

Tämän tutkielman tarkoituksena on selvittää, kuinka tehokkaasti jalkapallon vedonlyöntimarkkinat toimivat. Tehokkailla markkinoilla sijoitushyödykkeen hinta sisältää kaiken saatavilla olevan informaation, jolloin ei ole mahdollista saavuttaa ylisuuria tuottoja. Vedonlyöntimarkkinat sopivat hyvin tehokkuustesteille, sillä vedonlyönnissä sijoituksen todellinen arvo tiedetään pian vedon asettamisen jälkeen. Tutkielman kerroinaineistona toimivat Pinnaclen ja Veikkauksen kertoimet. Pinnaclelta hyödynnetään myös tietoja kertoimilla saavutettavissa olevista maksimivoitoista. Näiden lisäksi aineisto sisältää joukkueiden otteluohjelmat ja arvioita pelaajista. Kokonaisuudessaan aineisto koostuu kymmenen jalkapallosarjan otteluista vuosien 2015–2019 ajalta. Kertoimista johdettujen todennäköisyyksien tarkkuutta mitataan eri ajankohtina ja eri maksimivoittorajoilla. Tilastolliset testit osoittavat selvästi kertoimien ennustekyvyn paranevan mitä lähempänä ottelun alkua ollaan. Myös maksimivoiton kasvaessa havaitaan parantuvaa tarkkuutta kertoimista, mutta tulokset eivät ole yhtä selviä kuin eri ajankohtia tarkasteltaessa. Pinnaclen kertoimien ennustekyvyn havaittiin olevan tilastollisesti merkitsevästi tarkempia kuin Veikkauksen kertoimien. Vedonlyöntimarkkinat vaikuttavat olevan tehokkaita sekä tunnettujen pelaajien poissaolojen että joukkueiden otteluiden välisten lepopäivien huomioimisessa. Suuria kerroinliikkeitä tarkasteltaessa havaittiin keskimäärin parempia tuottoja lyödessä nousseisiin kertoimiin kuin laskeneisiin kertoimiin. Positiiviset tuotot eivät olleet tilastollisesti merkittäviä, mutta antoivat aihetta jatkotutkimukselle

The relevance of prediction markets for corporate forecasting

Prediction markets (PMs) are virtual stock markets on which shares are traded taking advantage of the wisdom-of-crowds principle to access collective intelligence. It is claimed that the accumulation of information by groups leads to joint group decisions often better than individual participants’ approaches to solutions. A PM share represents a future event or a market condition (e.g. expected sales figures of a product for a specific month) and provides forecasts via its price which is interpreted as the probability of the event occurring. PMs can be used in competition with other forecasting tools; when applied for forecasting purposes within a company they are called corporate prediction markets (CPMs). Despite great praise in the (academic) literature for the use of PMs as an efficient instrument for bringing together scattered information and opinions, corporate usage and applications are limited. This research was directed towards an examination of this discrepancy by means of focusing on the barriers to adoption within enterprises. Literature and reality diverged and neglected the important aspect of corporate culture. Screening existing research and interviews with business executives and corporate planners revealed challenges of company hierarchy as an inhibitor to the acceptance of CPM outcomes. Findings from 55 interviews and a thematic analysis of the literature exposed that CPMs are useful but rarely used. Their lack of use arises from senior executives’ perception of the organisational hierarchy being taxed and fear of losing power as CPMs (can) include lower rungs of the corporate ladder in decision-making processes. If these challenges can be overcome the potential of CPMs can be released. It emerged – buttressed by ten additional interviews – that CPMs would be worthwhile for company forecasting, particularly supporting innovation management which would allow idea markets (as an embodiment of CPMs) to excel. A contribution of this research lies in its additions to the PM literature, explaining the lack of adoption of CPMs despite their apparent benefits and making a case for the incorporation of CPMs as a forecasting instrument to facilitate innovation management. Furthermore, a framework to understand decision-making in the adoption of strategic tools is provided. This framework permits tools to be accepted on a more rational base and curb the emotional and political influences which can act against the adoption of good and effective tools