Predict.fun Invite "92f86" Get 10% Off On Trading Fee

Information and where it comes from

All information used is drawn exclusively from open‑access scientific studies and publicly available abstracts. Whenever possible, we link directly to the full papers, otherwise the abstract is linked. This makes every claim fully traceable to its original source.


Answer

Summary A 10 % reduction in trading fees makes back-lay or similar arbitrage profitable with a smaller odds shift. Lower costs revive incentives to exploit mispricings that Persist because fees normally erode gains—from about 21¢ to 9.1¢ per cycle on PredictIt. As more traders enter to capture these restored profits, order flow rises, liquidity deepens and prices converge faster toward true probabilities, improving overall market efficiency and forecast accuracy.

Cited Sources

  1. Arbitrage in Political Prediction Markets (2020, 2 citations)

    • [1a] 3.1 ARBITRAGE AND MARKET MISPRICINGS : These cases represent examples of successful buy-all-contracts and sell-all-contracts arbitrage, as well as a case in which there is no profitable arbitrage trade possible. An actual observed example of a mispriced PredictIt market with profitable arbitrage is presented in Table 4. In this case, the nominal mispricing (arbitrage profit before fees) to sell one share of each contract is P nom = bi bi - p =21C/ i N [?] ; with PredictIt's profit fee, the arbitrage profit is reduced to 9.1C/.
    • [1b] 4.1.1 PROFIT FEES : Illustrating the effect of PredictIt's 10% profit fee, the markets remained at the mispriced equilibrium, distinct from the efficient no-fee market equilibrium wherein the price sum equals the payout. The observations given above provide insight into the market design of American political prediction markets and their ability to effectively forecast election outcomes.
    Source Details
    TitleArbitrage in Political Prediction Markets
    Author(s)Andrew Stershic, Kritee Gujral
    Date2020-09-22
    URLhttps://www.ubplj.org/index.php/jpm/article/download/1796/1605
    Citations2
    DOI10.5750/jpm.v14i1.1796
    Cite as: 
    APAStershic, A., & Gujral, K.. (2020). Arbitrage in Political Prediction Markets. The Journal of Prediction Markets, 14(1). https://doi.org/10.5750/jpm.v14i1.1796
    ChicagoStershic, A. and Gujral, K., Arbitrage in Political Prediction Markets, The Journal of Prediction Markets, vol. 14, no. 1, p. , 2020. DOI: 10.5750/jpm.v14i1.1796
    HarvardStershic, A., Gujral, K., 2020. Arbitrage in Political Prediction Markets. The Journal of Prediction Markets 14.. https://doi.org/10.5750/jpm.v14i1.1796
    MLAStershic, A.and K. Gujral. “Arbitrage in Political Prediction Markets”. The Journal of Prediction Markets, vol. 14, no. 1, 2020, doi:10.5750/jpm.v14i1.1796.
    VancouverStershic A, Gujral K. Arbitrage in Political Prediction Markets. The Journal of Prediction Markets 2020;14. https://doi.org/10.5750/jpm.v14i1.1796.
    BibTeX
    @article{2020ArbitragePoliticalPredictionMarketsAPPM,
 DOI = {10.5750/jpm.v14i1.1796},
 ISSN = {1750-6751},
 author = {Andrew Stershic and Kritee Gujral},
 journal = {The Journal of Prediction Markets},
 number = {1},
 publisher = {University of Buckingham Press},
 title = {Arbitrage in Political Prediction Markets},
 url = {http://dx.doi.org/10.5750/jpm.v14i1.1796},
 volume = {14},
 year = {2020}
}

  2. Hedging on Betting Markets (2020, 11 citations)

    • [2a] 4. Hedging on a Betting Exchange : Assume that a bettor bets $1 at the before fee odds of ai = 3 with the fee t = 0.05 and that the bet is won. He has then won $2 on top of his initial stake before the fee. The betting exchange charges a fee of t x $2 = 0.05 x $2 = $0.1, and the money the bettor has won after the fee is $1.90 . We then have the following relation between the before and after fee odds: Lemma 3.
    • [2b] 4. Hedging on a Betting Exchange : (see Appendix A.2 for the full mathematical derivation) This means that the inefficiency in the market caused by having the betting exchange fee manifests itself through imposing a cap such that the odds have to drop (rise) a non-zero amount in order for them to be profitable. The probabilistic interpretation, however, does not change despite the difference in expression, as the proposition is the same as Propositions 1 and 2, with only a difference in expression, as we have substituted the odds for the odds after a fee.
    • [2c] 4. Hedging on a Betting Exchange : Assume that the bettor places a back bet with one unit (normalized) on i at the before fee odds ai. At a later point, the bettor places k > 0 units betting against the outcome occurring. If i occurs, the profit before the fee is (ai - 1 - k), of which the betting exchange takes t, leading to a profit of (1 - t)(ai - 1 - k).
    • [2d] 4. Hedging on a Betting Exchange : While the results above are accurate in terms of a general back and lay market, the most common way of organizing markets with a lay option is through a betting exchange. In this market format, people bet directly against each other such that their positions cancel out, and the betting exchange charges a fee equal to a percentage of the winnings of the bettors in order to make a profit.
    Source Details
    TitleHedging on Betting Markets
    Author(s)Gustav Axén, Dominic Cortis
    Date2020-08-25
    URLhttps://www.mdpi.com/2227-9091/8/3/88/pdf?version=1598349304
    Citations11
    DOI10.3390/risks8030088
    Cite as: 
    APAAxén, G., & Cortis, D.. (2020). Hedging on Betting Markets. Risks, 8(3), 88. https://doi.org/10.3390/risks8030088
    ChicagoAxén, G. and Cortis, D., Hedging on Betting Markets, Risks, vol. 8, no. 3, p. 88, 2020. DOI: 10.3390/risks8030088
    HarvardAxén, G., Cortis, D., 2020. Hedging on Betting Markets. Risks 8, 88.. https://doi.org/10.3390/risks8030088
    MLAAxén, G.and D. Cortis. “Hedging on Betting Markets”. Risks, vol. 8, no. 3, 2020, p. 88, doi:10.3390/risks8030088.
    VancouverAxén G, Cortis D. Hedging on Betting Markets. Risks 2020;8:88. https://doi.org/10.3390/risks8030088.
    BibTeX
    @article{Axen2020HedgingBettingBettingMarketsHBM,
 DOI = {10.3390/risks8030088},
 ISSN = {2227-9091},
 author = {Gustav Axén and Dominic Cortis},
 journal = {Risks},
 number = {3},
 pages = {88},
 publisher = {Multidisciplinary Digital Publishing Institute},
 title = {Hedging on Betting Markets},
 url = {http://dx.doi.org/10.3390/risks8030088},
 volume = {8},
 year = {2020}
}

  3. A study of efficiency in New Zealand election prediction markers (2015, 0 citations)

    • [3a] 4.1.2 Arbitrage in Prediction Markets : Thus arbitrage has the effect of causing prices in different markets to converge when the items are sufficiently similar. The speed of price convergence is a measure of market efficiency: an efficient market should expect a quick price convergence. Transaction costs, taxes, and other costs provide an impediment to this kind of arbitrage, particularly between different markets.
    • [3b] 2.3 The 2011 New Zealand Vote Share Prediction Market : 5 There is a possibility that without the trading fee, trading would have been higher but there is noway to prove or disprove it. % % Figure 2.3: The effect of transaction fee on trading activity The vote share market was set up to have greater liquidity (the sensitivity of 50 and the batch size of 1) than other markets in iPredict.
    • [3c] 4.2.1 Arbitrage Framework: Without bid and ask Spread : A trading fee of $0.0035 per share traded (35 cents per 100 shares traded) was introduced in August 2011. Also, a 1.75% fee on credit card deposits is paid to the bank. This cost can be easily avoided by a manual deposit into iPredict's bank account. Last, a withdrawal fee of 2% or $2 (whichever is greater) is incurred only if the trader has positive earnings on iPredict.
    • [3d] 4.2.1 Arbitrage Framework: Without bid and ask Spread : Next, the assumption of cost-free transactions is relaxed, the condition for an arbitrage to be profitable in Equation (4.3) no longer holds. Instead, arbitrage is only profitable if the gain from arbitrage is sufficient to cover transaction costs, otherwise there is no incentive to arbitrage. Transaction cost varies in prediction markets.
    Source Details
    TitleA study of efficiency in New Zealand election prediction markers
    Author(s)Tram P. Cao
    Date2015-01-01
    URLhttps://s3-ap-southeast-2.amazonaws.com/pstorage-wellington-7594921145/31466615/thesis_access.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIA3OGA3B5WBO5PUAXV/20251219/ap-southeast-2/s3/aws4_request&X-Amz-Date=20251219T042557Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=77777fa61b318a7abcafe72bae115e1d60ff89ba6ff58884e56086cdcec7df7b
    Citations0
    DOI10.26686/wgtn.17011910.v1
    Cite as: 
    APACao, T. P.. (2015). A study of efficiency in New Zealand election prediction markers. https://doi.org/10.26686/wgtn.17011910.v1
    ChicagoCao, T. P., A Study of Efficiency in New Zealand Election Prediction Markers, 2015.
    HarvardCao, T.P., 2015. A study of efficiency in New Zealand election prediction markers.. https://doi.org/10.26686/wgtn.17011910.v1
    MLACao, T. P.. A Study of Efficiency in New Zealand Election Prediction Markers. 2015, doi:10.26686/wgtn.17011910.v1.
    VancouverCao TP. A study of efficiency in New Zealand election prediction markers. 2015. https://doi.org/10.26686/wgtn.17011910.v1.
    BibTeX
    @phdthesis{2015StudyEfficiencyPredictionMarkersSENZEPM,
 DOI = {10.26686/wgtn.17011910.v1},
 author = {Tram P. Cao},
 school = {Victoria University of Wellington},
 title = {A study of efficiency in New Zealand election prediction markers},
 url = {http://dx.doi.org/10.26686/wgtn.17011910.v1},
 year = {2015}
}

  4. On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules (2018, 4 citations)

    • [4a] Stanko Dimitrov : We use the relation between deviation and liquidity to present a systematic approach to help determine the amount of liquidity required for cost-function prediction markets, an activity that up to this point has been described as "art" in the literature. Key words : probability forecast, decision analysis, prediction markets, market scoring rules, risk aversion, cost function market makers, market depth, market liquidity
    Source Details
    TitleOn the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules
    Author(s)Majid Karimi, Stanko Dimitrov
    Date2018-05-08
    URLhttps://dspacemainprd01.lib.uwaterloo.ca/server/api/core/bitstreams/f82b8c0c-11df-4c27-b8e3-34ba4b302089/content
    Citations4
    DOI10.1287/deca.2017.0362
    Cite as: 
    APAKarimi, M., & Dimitrov, S.. (2018). On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules. Decision Analysis, 15(2), 72–89. https://doi.org/10.1287/deca.2017.0362
    ChicagoKarimi, M. and Dimitrov, S., On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules, Decision Analysis, vol. 15, no. 2, pp. 72–89, 2018. DOI: 10.1287/deca.2017.0362
    HarvardKarimi, M., Dimitrov, S., 2018. On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules. Decision Analysis 15, 72–89.. https://doi.org/10.1287/deca.2017.0362
    MLAKarimi, M.and S. Dimitrov. “On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules”. Decision Analysis, vol. 15, no. 2, 2018, pp. 72–89, doi:10.1287/deca.2017.0362.
    VancouverKarimi M, Dimitrov S. On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules. Decision Analysis 2018;15:72–89. https://doi.org/10.1287/deca.2017.0362.
    BibTeX
    @article{2018RoadMakingScoringRulesRMSARBMSR,
 DOI = {10.1287/deca.2017.0362},
 ISSN = {1545-8490},
 author = {Majid Karimi and Stanko Dimitrov},
 journal = {Decision Analysis},
 number = {2},
 pages = {72--89},
 publisher = {Institute for Operations Research and the Management Sciences},
 title = {On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules},
 url = {http://dx.doi.org/10.1287/deca.2017.0362},
 volume = {15},
 year = {2018}
}