Option Analytics For All
Moontower Munchies #73
Friends,
I got a chance to do something super neat…go on the amazing Flirting With Models podcast with Corey Hoffstein.
Corey's influence is all over my writing (esp the MoontowerMoney project which inches along glacially). Corey has been a role model for thinking and communicating since I discovered him many years ago.
In other words...I was quite nervous.
I make a joke about feeling like a monkey-on-a-unicycle-interlude since this podcast is frequented by brilliant quants but Corey gave me space to have fun with this and tell stories. I’m definitely more comfortable behind a keyboard. When I hear myself I just think “you can take the kid out of Jersey, but you can’t…”
Anyway, I hope this fun for you:
🎙️Life Through a Volatility Lens (Spotify, Apple)
A smattering of what we cover, often in the context of specific stories:
How the floor accelerated learning rates and how to recover that
The meaning of discretionary trading in the gap between structured and unstructured data
Placing yourself in a pecking order
“Flippers” vs “warehousers”
What you can deduce from understanding trading firm lineages
Transitioning from market-maker to buy-side and why the definition of a “good trade” changes
What’s required to run the highest amount of risk for a given level of edge
Spot/vol correlation assumptions
Understanding the premium to NAV in USO when oil went negative and the metagame of figuring out how to trade it
The proto-concept for moontower.ai was imagined almost 15 years ago despite coming to market this year (it’s a story that started while bouncing around all the pits in the NYMEX building)
Having a vol lens and assuming the underlying is fair vs having an opinion on an asset and assuming the vol market is fair
Who moontower.ai is for
Game recommendations
Ok, back to writing. My paywalled Thursday posts typically get into the practical and technical side of using options. But the options market can be useful to investors who have no intention of strapping on a call or put.
Option Surfaces As A Unique Source of Market Intel
The typical use of option analytics
Before you buy or sell a stock you probably don’t just look at the price. You have reasons for the trade. And those reasons are informed by metrics. If you are a fundamental investor those metrics might include information you’d find on an income statement such as gross revenue. If you are a technical investor the information comes in the form of charts or indicators such as momentum. In either case, you have a dashboard that serves you information you find relevant to your decisions.
The same is true for options. You wouldn’t just look at the contract specifications and the price. You have a set of analytics that influence what and when you trade.
A key difference between stocks and options is stocks are investments. Options are derivatives. They derive their value from properties of the underlying stock. The simplest of such properties is the moneyness or distance of the stock price from the option strike. A call option that is “in-the-money”(ie the stock price is greater than the strike price) is always worth more than a call option with the same expiration date that is “out-of-the-money”.
A more abstract input into an option’s value than moneyness is the stock’s volatility. If a stock moves 5% a day than a 15% out-of-the-money call is more likely to pay off than if the stock only moves 1% per day.
Option analytics, like stock analytics, help traders and investors choose which options to trade. Investing with a capital “I” is not really a choice. If you stick your money under a mattress you’ve still made a choice.
Options however are very much a choice.
We’ve written before about being clear about your motivation to pull an option arrow out of your quiver. They are unforgiving in the sense that they are priced for specificity. Option premiums are priced with a specific implied volatility to a specific expiry. The offsetting benefit is you are highly levered to being right.
Options can be used to:
hedge (reduce risk)
speculate (increase risk in search of a return)
trade volatility (this is an advanced strategy for trading an option for its own sake — you think the implied volatility is mispriced)
You can use options to make fixed bets on where a stock might expire. In this case, you already know the maximum loss is the premium you spend. This feature can enable you to stay with the trade even if it’s marked against you.
Options can also be used for path-dependent bets. You can profit from flipping a call option if you buy it before the stock rallies and sell it before the stock falls again. In this use, you may have had no intention of holding the option until expiry but you thought the stock could rise quickly.
In short, the primary benefit of trading options is by sharpening your bets to closely match either your investment thesis or insure your portfolio against specific scenarios. If you have a specific edge in your investing whether it’s security selection or timing then options are an invaluable tool. As author and trader Agustin Lebron recommends in Laws of Trading: Take the risks you are paid to take. Hedge the others. This allows you to concentrate when you have an advantage.
Regardless of how you use options, the value of option analytics is self-evident. You wouldn’t drive without a speedometer or fuel gauge. But what’s less obvious is how the instruments on an option dashboard are useful for investors who don’t use options.
Intelligence from options markets
Because options are so specific, their reward for being right is highly levered. Options are like parlays. Just like you are offered great odds on specific sequence or co-incidence of sports outcomes, options pay gaudy returns if you get direction, magnitude and timing correct.
This attracts smart money. Options offer the greatest reward for seeing the future. This is so well understood that the SEC makes a special effort to detect suspicious options trades because options are a honey-pot for insider trading.
Option prices, and by extension their analytics, can offer information that is either absent from the underlying shares or too fine-grained to be detected by stock analytics. Metaphorically, we say options are the true underlyer because they offer a fuller view of what is embedded in a company’s valuation than a simple share price.
To understand this consider the following toy example:
There are 2 stocks labeled simply as A and B. They are both trading for $100. What you can’t see is that A is trading for $100 because it is equally likely to be worth $120 or $80, while stock B is trading for $100 because it’s 20% to be worth $500 and 80% to be worth $0.
The flat share price of $100 masks the distribution of the possible stock returns (which we’ve made binary and simple for exposition). You cannot tell from a single data point that these companies have vastly different risk profiles.
But if there were options listed on both of these stocks smart investors would bid $20 for the 200 strike calls for B (those calls have a 20% chance of being worth $300 so they are worth far more than $20). Nobody would be willing to pay even $5 for the 200 strike call on stock A. Even if there was a low-information investor who tried, smarter investors would compete to sell those options much cheaper than $5.
The options market not only reflects the share price but its distribution. In fact, it reflects the consensus distribution for specifically for each option expiry date! Option surfaces are aptly named. They are not fuzzy 2D depictions of a stock’s future but a rich surface of possibilities through time.
A Brief Detour into the World of Indicators
Investors monitor a wide array of metrics to divine what the market is telling them.
Does the inverted yield curve foretell a recession?
The VIX as a fear gauge
The Baltic Dry Index as a barometer of global economic activity
TIPs rates aggregate inflation expectations
CAPE ratios and credit spreads signal the health of public corporations
Investors may have no intention of trading these securities directly. Instead, they are tracking them as indicators. The market’s vital signs. The allure of molding your own crystal ball by blending indicators is seductive. Which means many speculators try to do it.
But like any competitive endeavor, most people will fail.
At the highest level, it’s possible to fail because the game pushes back — the competition is just better than you and first or even second-order inferences are too naive. If everyone adopts the same crystal balls they cease to work because prices bake in what has become “common knowledge”.
But many will not even get to this level.
Some will succumb to a lack of discipline far earlier. The person with a bearish bias that cherry picks the indicators that confirm their view. Some will not understand how to interpret an indicator because they fail to consider the difference between a risk premium the utility of a price) and an expectation (the probability of a price). This failure tends to happen when an investor lazily onboards an indicator without appreciating the liquidity and flows that set the prices it summarizes.*
To give people at least a fighting chance of making better investing decisions, I’ve hammered the idea of measurement not prediction. Compiling, cleaning, and formulating data into indicators and analytics requires deep expertise in the underlying market’s price formation mechanism. Before you can predict you need to see the present clearly. This was the foundation of every option trading business I’ve built and the motivation behind moontower.ai
Option analytics as indicators for non-option users
We like to say moontower.ai is “option analytics with a point of view”. A mission that comes to life not just in presenting option surfaces but comparing them in relevant, actionable contexts. The application’s design follows its function by taking the user through a progression.
Investors who don’t trade options can look to option surfaces for unique insights just like investors who look for indicators in adjacent markets. The VIX as a fear gauge is the simplest, albeit most followed, example.
To be clear, combining insights from the option surfaces with the specialized knowledge of your market is artful work. But the tedious, expert work of “measuring” the options market is an effort best outsourced.
Examples of information option markets contain:
Expected volatility over various periods of time including discrete events such as earnings, crop reports, economic releases, Fed meetings
Expected volatility between 2 points in time
Implied distribution
Daily changes in expectations
Correlations between asset prices and volatility
Implied borrow rates and dividends
Average cross-correlation between names in a sector or index
moontower.ai is on the first steps of its journey helping option users make better trading decisions. But we understand the potential for option analytics to deliver unique messages because of how volatility surfaces “complete” the market.
If you are interested in how option analytics can complement your strategies or business, hit us up.
*Discerning how much of a price is a probabilistic expectation vs a risk premium is more art than science.
To demonstrate why, consider the cost of fire insurance. Some component of it is the actuarial chance of loss. But the remainder depends on the cost of servicing the policy, marketing the provider, regulatory/legal administration, and the competitive dynamics — how many providers are there, how efficiently can they manage the risk via pooling and diversification, and finally at the bottom of it all — what hurdle or opportunity cost return floor characterize the current economic landscape?
The question is a fascinating intersection of economics, human behavior, and the theory of replication that underpins derivatives pricing.
See:
Real World vs Risk-Neutral Worlds
Discerning Risk Premium From Expectation
**The permalink for this essay: https://blog.moontower.ai/option-analytics-for-all/
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the way you walk through this is fantastic. I would take the idea of options being the real underlying one step further: all investment vehicles are derivatives of the enterprise being financed. For example, AAPL is a derivative of Apple Inc. AAPL shares derive their value from the intrinsic value of Apple Inc. (which is not always a known or knowable thing...fundamental analysts try) and the extrinsic value that the market assigns to AAPL. Financial intermediation is all about creating vehicles to make capital formation more efficient. Those vehicles tend to take on lives of their own once they are set free in the markets and create a feedback loop to the enterprises they are financing. We are watching this play out in real time today with SREIT and BREIT.