Trading asset-based prediction markets vs short-term options
In my career I’ve seen the rise of short-dated options from both the market making and exchange sides. Thoughts on trading binary-style financial prediction markets versus vanilla options, focusing on greeks.
Prediction markets that settle to 1 or 0 based on underlying strike price are simply binary options, a construct that long pre-dates event contracts. In liquid underlyings a binary option can be replicated to a good approximation with a tight call spread. The reverse replication exists too: a vanilla option is a strip of binaries across the strike latter. The comparison ultimately breaks down due to discrete strikes, a truncated strike ladder, and two-sided transaction costs, but it provides a useful framework for deriving binary option properties. Here are a few of those properties, specifically options greeks, that are useful for modeling and trading binary option-style prediction markets:
• The greeks for a binary option are shifted one derivative to the left of vanilla options. Binary option delta looks like vanilla option gamma. It peaks ATM, decays both directions, and spikes toward a delta function at expiry.
• Binary option gamma changes sign at the strike. A long binary option holder is long gamma below the strike and short gamma above the strike. There is no position that can express “buying gamma into an event.”
• Binary option vega also changes sign: OTM holders are long vol, ITM holders short vol, and ATM vega is near 0. All prediction market traders are inherently running a vol book.
• Theta is non-monotone and can flip sign. Unlike a vanilla option, an ITM binary option gains from time passing.
• Fully-collateralized binary options have an embedded interest rate. At current front-end rates, a 6-month contract's "probability" is biased low by ~2.5 points versus the true risk-neutral probability.
The asymmetry between binary options and vanilla options reflect their ideal use cases. Binary options are a better instrument for a pure event view, while vanilla options remain the better instrument for exposure to the whole distribution of underlying prices.
back in the 70s, game theory was hyped the exact same way AI is today. every genius was obsessed with one thing: how to win.
then one guy watched his kids play with legos and realized everybody was completely missing the point.
in short: if you're constantly trying to "beat the competition," you're playing the wrong game entirely.
hands down one of the clearest explanations of strategic game theory applied to real life by @simonsinek
I mapped out the key rules and action points below.
If I were in my 30s or 40s right now and wanted to leverage AI to retire within 10 years, here's what I'd do:
1. Immediately form an LLC company. Not next month. Not once you're 'ready.' This week.
I've worked in IB & PE. Now I run a hedge fund.
So I'm pretty qualified in knowing what good analysis looks like.
One key thing I've learnt in my career is that you *have* to enjoy reading equity research so you don't get burnt out.
But naturally, only a few firms/people are talented enough to put out truly enjoyable, digestable research.
Some of them are hiding in plain sight, right here on @X
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So with that, here's a non-exhaustive list of people on @X I look forward to reading on a daily basis, in the hopes that some of you will too (if you're not already!)
In no particular order:
> @illyquid - mainly Asian related AI semis/hardware research & live analysis
> @damnang2 - in-depth, technical semiconductor research/theses
> @aleabitoreddit - deep thematic research/theses & company/sector analysis
> @PhotonCap - technical photonics & semiconductor research/theses
> @pepemoonboy - mix of macro/company specific comms
> @crux_capital_ - technical photonics deep dives & crucial updates on key players
> @Frenchie_ broad macro commentary & analysis
> @Blinklebloop - data centers / AI value chain analysis
> @KawzInvests - deep AI/tech/space analysis
> @degentradingLSD - broad macro/AI aligned commentary & analysis
> @michaelsikand - photonics/AI aligned research & commentary
> @Kaizen_Investor AI supply chain analysis & other sector specific trades
> @Yeah_Dave - broad macro comms & space/AI specific
> @TheValueist - AI-aligned research & company specific analysis
Dear FinTwit community,
I have open sourced a CVR database, with the goal of gaining more accurate CVR value estimates in M&A transactions.
This data can be difficult to gather. Please DM any CVR payouts you receive, and I'm happy to share the model as it is further refined.