The Case for Prediction Markets
|May 10, 2020||3|
You are probably a prediction market trader. We say that because (mostly) know who reads Flip Incoming. Maybe you’re a just a part time hobbyist looking for entertainment… or maybe you’re a political insider, a poker whiz maxing EV, a crypto-turned-everything-trader, or a Wall Streeter stepping on the 99%. Because you’re a trader, you’re probably here for our price breakdowns and market analysis.
Maybe you don’t care too much about the movement you’re a part of.
We do. Prediction Markets have the capability to revolutionize major industries and change our world. Flip Incoming is here to make the case for them.
Don’t worry, the market analysis part of Flip isn’t going anywhere, but we’re also here to help speak for an infant industry.
Do you care about Fake News? Prediction Markets
Tired of clickbait headlines, sensationalized non-stories, and quotes intentionally taken out of context?
It’s not really the media’s fault — they’re in a desperate fight for our attention and struggling for their own survival. The problem is all of ours. We as readers are driven mostly by outrage. We demand from the media the things we claim to hate.
We prefer to consume things in narrative form, and so we also demand our media takes that form. But we don’t have time to read every news article about every topic and evaluate the credibility of each author. We can’t make sense of the varied methodologies, scopes, time periods, and geographies that different narratives about the same topic employ.
And so readers don’t have the time to tell the difference between quality writing and garbage. When that’s true, and when and garbage is cheaper to produce, is it any wonder that the whole market trends towards garbage?
Prediction markets have two key roles in news, and both directly address these problems.
A Prediction Market has only one price, and it’s visible to everybody.
A single price cuts through the bullshit. Reading an article about the topic and want to know where things really stand? Look at the price.
Traders who are first to a story make money and so sometimes the number moves before any story is even visible. Just this week, we’ve seen two examples on PredictIt (NY Primary Market and Michael Flynn Clemency Market) where the market moved prior to the initial post by any major news agency. Traders were watching the primary sources — in both cases, the respective court filings themselves — and reacted instantly. The incentive to be first is so great that it becomes worth it for traders to dedicate considerable time to making sure that the number reacts when it should.
Prediction markets on PredictIt don’t seem to exhibit severe random walks of pricing like the stock market. The price could be relatively stable for a month before moving in response to a change in the situation.
A price movement suggests we should pay attention to a market. It’s a beacon for our strained attention, telling us when something meaningful is happening, or not.
Traders make money by being right: the number has to be accurate.
In the non-prediction market world, it is perfectly possible — and in fact the default — for a single future outcome to have multiple prominent predictions exist simultaneously with radically different conclusions. Whether it’s about the economy, the environment, human health, or any other cause, pundits can publish their view with no meaningful consequences for being wrong and no meaningful discussion about the quality of the varying opinions.
Filter bubbles exist because for any topic of consequence, people can find predictions that support their existing viewpoints. We aren’t forced to reconcile our differing opinions or to ever acknowledge when we were wrong.
Prediction markets force us to do that. This is the ‘Sense Making’ aspect of prediction markets — they don’t just react to news, they must determine how much to react to news. What is significant new information and what is not? Who is a credible source and who is not?
People can and will continue to disagree about important topics — that is natural and important — but prediction markets force them to acknowledge the current consensus and whether their input is persuasive.
By forcing us to sense-make, prediction markets destroy filter bubbles.
Do you care about ‘Wall Street vs. Main Street’? Prediction Markets
In years and decades past, we felt comfortable equating Wall Street performance with the health of our economy. That narrative has fundamentally broken over the past twenty years.
The number of public companies has shrunk due to the availability of private financing at high valuations (“Unicorn start-ups”) even as the regulatory burdens of being public have increased; Wall Street is literally less representative of our economy than it used to be
Increased wealth inequality as low-skilled jobs compete with globalized overseas labor and technology has greatly magnified the impact of high skilled workers. This translates into fewer Americans having a significant stake in the smaller number of public companies that do remain
Significant bailouts of public companies in 2008 and 2020 and the printing of money to finance debt markets that only large companies can access
The third point is an expression of the problem. Why does Wall Street get singled out for assistance? Because:
Wall Street is the only meaningful prediction market we have.
We Manage What We Measure
We have three major prediction markets in the U.S.:
The stock market
Interest rates (e.g., 10-year treasuries)
And that’s it.
We track all sorts of other values that better represent the economy than the stock market: unemployment rates, GDP growth, energy stocks/flows, etc. But we look at those in the rear view mirror.
Forward looking forecasts get put out by various institutions or individuals, but are consumed based on their alignment with our pre-existing beliefs rather than their quality. Don’t like a prediction by Institution X? Just claim Institution X is politically biased against you and ignore them. Who will really remember when they turned out to be right?
And so forward looking forecasts as they exist now don’t have the impact they should. They lack prediction markets’ surfacing and sense-making qualities.
Why don’t we have prediction markets for education, human health, 4th quartile income? If we we did, and those markets dropped significantly in response to an event, do you not think we’d see more effort to address it?
There’s more to consider above, and we’ll explore it further in future posts. But for now, let’s summarize:
We manage what we measure, and we don’t measure nearly enough.
Do you care about unbiased institutions? Prediction Markets
Prediction markets have a unique characteristic: they require a third-party source of truth to provide the market resolution. As an example, the polling markets at PredictIt are frequently resolved by RealClearPolitics, which takes an average of polls run by other organizations.
Different third parties will be used for different domains and markets. Critically, those sources of truth will need to be accepted by market participants, who by definition will differ in opinions about the market. Over time, institutions resolving large prediction markets will gain influence and power, creating an incentive to become the resolving institution for any given domain.
And the main competitive axis for resolving institutions will be objectivity.
For new markets created in new domains, resolving institutions will need to be seen as reliable by participants. If there aren’t any suitably unbiased institutions, prediction markets will force their creation.
Do you care about:
More effective government
Enabling new business models and economic activity
A healthier political dynamic
Better modeling and estimation of the future
Still Challenges Ahead
You might be able to tell that we’re excited about the road ahead for prediction markets. Still, there are challenges ahead. As advocates of prediction markets, we believe that acknowledging and addressing these challenges head-on will build a healthier community, drive regulatory change more rapidly, and ultimately make markets more robust. Among the problems:
There are real concerns around providing economic incentives for bad outcomes. Betting against the U.S. education system doesn’t make you seem like a great citizen. Early legislative attention was driven around markets for likelihood of terrorist attacks.
Designing markets and contracts correctly to avoid manipulation, allow for clear resolution, and drive the right behaviors is hard and will take time.
Open prediction markets are still not legal in the U.S. Effective advocacy is needed to show that these markets are more than a new outlet for gambling.
These and other challenges are real, and we recognize that as a community they must be addressed thoughtfully. We will explore them in depth in future posts.
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