Decentralizing Prediction Markets with Blockchain
Imagine a scenario where price forecasts are made are forecast accurately, predictions about property markets and rental rates are predicted accurately, and the news is fact-checked thoroughly, making fake news a terrible memory. This scenario could seem too good to be true, but it could be something that could become a reality, thanks to the emergence of Decentralized Prediction Markets (DPMs).
So What Is A Prediction Market?
To understand what a prediction market is, we need to look at the definition of a market. A market is simply a group of people that buy or sell things. These can be physical things like a grocery store or a wholesale market, financial assets like banks and stock markets, or cab aggregator services like Uber.
Similarly, a prediction market is a marketplace to buy and sell predictions. A prediction market operates like a stock market. Just like how you get shares in a stock market, you get shares in the outcome of an event. This could be any event, the weather, financial prediction, or predictions about the price of utilities increasing or decreasing.
Understanding How A Prediction Market Works
A prediction market has two types of shares, YES shares, and NO shares. YES shares are long shares, while NO shares are short shares. The payout depends on if the event in question occurs or doesn’t occur. For example, a YES share will pay out a Dollar if the event occurs, and if it doesn’t, then it won’t pay anything. The same applies to a NO share. If the event in question does not occur, then the NO share will pay out a Dollar, and if the event occurs, then it won’t payout.
The share price depends on how much buyers pay and how much the sellers are willing to accept. The price in a prediction market is equal to the probability of the event occurring. If a YES share costs 60 cents, then the market believes a 60% chance of the event or outcome occurs. Similarly, if the NO share costs 60 cents, then the market thinks a 60% chance of the outcome is not happening.
What Are Some Existing Prediction Markets?
Prediction markets have proven to be a useful prognosis tool as they can represent various opinions. Some examples of prediction markets are Intrade, BetFair, and Iowa Electric Market. The Iowa Electronic Market is one of the pioneers of prediction markets. It was established in 1988 and was used to predict who would win the presidential elections. Augur is another example of a prediction market. It is a decentralized prediction market based on the Ethereum blockchain.
Rise Of Prediction Markets
Blockchain technology has made ownerless, peer-to-peer prediction markets possible. Ethereum, in particular, has enabled prediction markets to realize their full potential through the power of smart contracts. Smart contracts are lines of code that are executed automatically when certain predetermined conditions are met.
Decentralized prediction markets are smart contracts that stipulate who gets paid how much when certain predefined conditions are met. Decentralized prediction markets are dApps that substitute centralized control with code and cryptography. Decentralized prediction markets are in a very nascent stage of their evolution. They have the potential to revolutionize trading and investing.
What Are The Problems Faced By Prediction Markets?
Prediction markets let traders and individuals leverage their knowledge to forecast outcomes for specific events or real-world scenarios. The currency generation of prediction markets suffers from some fundamental problems. There is an apparent lack of markets and a lack of liquidity in prediction markets. There is also a lack of traders and the presence of duplicate markets. They also face legal issues and a lack of decentralization, severely limiting their user base resulting in a drop in prediction quality due to a less diverse crowd.
The total prediction market volume averaged only $1 million per day through 2020. This figure was lesser in previous years. At such low volumes, traders cannot hedge against outcomes and take large positions in prediction markets. The low volumes also contribute to low liquidity, leading to low fees and less active traders. This is the problem that plagues existing prediction markets, and they end up with hardly any daily users or daily volume.
Which Are The Best Decentralized Prediction Market Platforms?
Developed in 2014 by the Forecast Foundation, Augur aims to incentivize a network of computers to maintain a prediction market platform on Ethereum. Augur forecasts the outcome of any event by using the “wisdom of the crowd” principle. This method collects information from the crowd and averages it into the most realistic possibility and predicting the most probable outcome.
Gnosis was founded in 2015 by Stefan George and Martin Koppelmann. Gnosis was one of the first projects that were backed by the Ethereum focused ConsenSys. The Gnosis platform uses insights from capital markets and data science to enable users to forecast events. Users can also build their own decentralized prediction applications. Gnosis also offers users of the platform a multisignature wallet. Gnosis had an ICO on 24th April 2017 and raised $12.5 million of Gnosis (GNO).
Polkamarkets is a DeFi powered prediction market which can be used for trading and cross-chain information exchange. Users can predict and take positions on the outcomes of real-world events and scenarios. Polkamarkets is based on a decentralized and interoperable platform on Polkadot.
Prediction markets represent a variety of opinions and have proven to be a useful prognostic tool. Companies like Google also utilize prediction markets. The current economic, cultural and political environment has increased the demand for prediction markets. Prediction markets have slowly moved from the private domain to the public domain. The availability of data from multiple sources should improve estimation methods and bring about the problem of data manipulation. However, as prediction markets become more mainstream, the markets’ effectiveness will improve, and ethical and human biases will be adjusted.