13. On the other side of the money spectrum: Greeking out with Equity Index Futures Options

Dear Co-creator;

[I wrote this and two days later Bloomberg reports this: https://www.bnnbloomberg.ca/zero-day-options-boom-is-spilling-into-7-4-trillion-etf-market-1.1971370, an ETF Fund that will sell put options that are priced either at-the-money or up to five percent in-the-money (i.e., higher than the current market price). If the Fund sells an option that’s priced above the current market price, the Fund may profit if the Index increases in value above its current price. This opportunity exists until the option’s expiration date, which is typically the next day. WOWWWW!]

As few of my friends and I are fleshing out some actual use/customer cases for our money selling company (“purveyors of fine money”), I wanted to keep you entertained with the other side of the spectrum when it comes to money; the Capital Markets.

I will give you an inside look into one of my current favorite trading algorithms. It is one of the many components of large-scale asset management system that I employ but can be enjoyed all by itself. But first some some basic orientation into options, futures, indices, and of course the total combo of these all; namely equity index future options. Let’s start with our Markets for Dummies (and end up at Nobel-prize level). (EDIT: Apparently I wrote 2,300 words so gonna be long and geeky, FYI).

We have talked about valuations etc. in the past. Value can be simplified as the total future profits of the company discounted by risk. Price, on the other hand, is simply what the buyer and the seller agrees on and can be close to or even equal to value. Most likely not, as the buyers and sellers do not agree all the time, and a negotiation ensues. So they buy and sell agreeing on a public price based on their private assessment of value. Since this representation of value involves all kinds of future predictions about the company, and its economic environment, it is usually wildly divergent among the traders. So far so good?

Let’s call these buyers and sellers traders, and let’s also have them trade on not just the companies, but also various attributes of such companies. One can bunch up the largest 500 of these companies together because they are stronger together, call it an index; and trade pieces of this index. That would be an attribute / derivative of the “company.” Why not? As you know S&P 500, the Standard and Poor's 500, is a stock market index tracking the stock performance of 500 of the largest companies listed on stock exchanges in the United States.

They can trade on a promise made by the company. That would be a bond. They can trade on whatever they Goddamn please, i.e.. derive whatever attribute they want from any economic activity and buy and sell such attribute. An option, for instance, is just that: right to, but not an obligation to buy (or sell) something. I give you some money today so that you promise to sell me the shares of company if I want to buy it in the next three months. If I don’t want to buy it, then you keep the money. But if I want to buy it, then I pay you the agreed upon price and you have to sell the shares to me.

As you can see these attributes and derivatives can be wildly varied with all kinds of rules attached to them. Every rule (time, agreed upon price, aggregation, segregation, whatever rule you want) introduces new components to the representation of “value.” Apple stock as of September 8, 2023 is $178. A promise to give you an Apple stock at $180 on September 15, 2023 is priced at $1.66. A promise to give you an Apple stock at $180 price on September 20, 2024 is $22. (These are called call options by the way). You see the attribute, “promise,” has buyers and sellers all of a sudden. How do you value a “promise” though? Getting complicated, no?

Unless there is a significant news/event during the day, intraday market movement is random walk, Brownian motion. Equity markets historically tend to drift upwards, most likely because we are a progressive species, but you wouldn’t necessarily know that on any given day.

This data shows that random walk, and it also shows something else: volatility; a measure of dispersion of price change around its mean. You can see easily that 2023 volatility has been quite low compared to the years before. Almost no 2% moves so far. As you know the theoretical price of an option is determined by volatility, time, and (it has been forgotten until now) interest rates. Here is Black-Scholes  formula in all its absolute elegance (I learnt it from the very person who invented it, and thought about getting it tattooed):

So let’s see what’s going on here and tie the abstraction back to the human condition. You have companies that make money. You can put value to that company by looking at the money it will make over time. Then you have buyers and sellers who want to buy and sell anytime they want and they have to agree on a price which may or may not be tied to a common understanding of value. Then one seller has a brilliant idea of not just selling a share of the company, but a promise to sell a share of the company. Then some professor figures out that the value of the promise is really dependent on the rate of “change” of the agreed upon prices in the past (volatility of agreements, if you will), time frame, and agreed upon price (and gets a Nobel for it). And, wait for it, this very math makes it possible that the derivative of the stock is now traded more than the stock itself. We buy and sell the “promise” more than the thing it is based on. Ponder that a bit more before I continue.

The beauty of the options markets is that the largest players stick by these house rules, eg. mathematics. All their profit depends on being slightly right on actual vs. theoretical volatility across all the vehicles they make markets. Their price prediction on the option of Apple stock needs to be accurate (contrast that to they not really caring about the price of Apple stock itself).

Another large trading block are retail traders. Some play the options casino, some are day traders, some are enticed by the pseudo certainty of the options jargon. Individual investors made up 27% of all activity in options as of June, up from 23% at the start of 2020, according to Bloomberg Intelligence. They use technical analysis, “rules,” tea leaves, etc. Surprisingly the gamblers (usually call buyers), and “technical” traders (usually premium earners) tend to cancel each other out. In poker parlance, gamblers would be the idiots who’d play any hand with occasional luck, and tech traders would be those who only play the top hands (at least they can calculate odds) and would literally bleed out over time.

In the middle are those who have to use options for hedging other parts of their portfolio (super legitimate use). A university endowment can select the best funds to pick stocks like Warren Buffet, venture capital opportunities, etc. and bet smartly on the up and up. But they are tasked to cover a portion of the school budget via their returns with no tolerance for any bad market. For that eventuality, they’d buy index put options or futures as an insurance, a hedge against their mostly “long” bets on the progress of human species. Same is true if you were an oil producing country. You are long on oil, so you’d buy insurance for those times when oil prices go down.

And then there are those few “will makers” who flow with the system in precise formations to edge out wrinkles in their favor. The famous 2020 Gamma Squeeze by Softbank is a rare but fantastic example of enough capital forcing the house rules against itself while marshalling retail frenzy for the needed larger capital to manipulate the shit out of the market. You see a blackjack dealer is governed by rules; she must continue to take cards until the total is 17 or more, at which point the dealer must stand. Market makers, like Citadel and Goldman Sachs are in the same position; they sell when prices go up, and buy when they are down to add liquidity.

Let’s Greek out a bit. Delta is the rate of change of an option price given the rate of change of the underlying’s price. A market maker always wants to be delta neutral meaning that they don’t care if the price goes up or down. If someone buys call options on Apple from the market maker, then the market maker would go and buy Apple shares to balance out the delta. That purchase, at scale, would increase the price of the Apple stock, which in turn increases the price of the call the market maker is short. But the market maker looks at the entire universe at the same time. It would not just buy Apple shares to decrease its delta. It would reach out and grab some other delta reducing opportunity from millions of available instruments.

Market maker uses more math to abstract such universe. Gamma is the rate of change in an option’s delta per the rate of change of the underlying’s price. A market maker would want to have Gamma neutrality (zero Gamma) at Delta neutrality (zero Delta). In fact, market maker looks at the change of gamma per price (called Color), and all kinds of other second and third order derivatives (my favorite is Volga, change of Vega with respect to implied volatility).

So what Softbank did was to concentrate on FAANG stocks that they held at scale, bought short dated calls on such stocks, encouraged the retail crazies (r/WallStreetBets) to jump in, and created a perfect wall against the market makers who sold the calls, but couldn’t easily buy back the shares at favorable prices. Given FAANG dominance in S&P 500, NASDAQ and US Equities in general, these market makers weren’t able to hold high order Greeks neutrality via other instruments. The whole episode goes much deeper, but the trade pubs call it the largest Gamma Squeeze in financial markets. r/WallStreetBets took down other smaller hedge funds in other fantastic Gamma Squeezes also (AMC, GameStop). Funky times.

Gamma can therefore be manipulated into a good, rough estimate for the flow. Technical analysis uses algebra such as a moving average (MA) or any other simple price-based indicator. While this may have been fine 100 years ago, and an MA has absolutely no prediction power whatsoever other than predicting the demise of its users, better math can do a bit better today. Mostly because market participants use such better math. Market makers do NOT use MAs, RSIs, or any other ancient stuff (some funds used to use them to do the opposite of what retail would with such tools), but they use Gamma.

So let’s use Gamma for our trading algorithm. Now let’s select something to use it on.

CME Group, the largest derivatives exchange in the world, has futures on the S&P 500 called ES (E-mini S&P 500). Each ES Contract is 50 times S&P500 Index (around $223,000) and traded on an almost continuous basis with quarterly contract months with incredible liquidity (over 1.5m contracts a day = $400 billion). Compared that with the most liquid ETF that tracks S&P 500, SPY. SPY has a price of around S&P500 Index divided by 10 and a daily volume of 80m ($40 billion).

CME has weekly and monthly options on ES futures which are also almost continuous, deep, liquid and with tight spreads and. For these reasons, ES Future Options are my go-to vehicle for US equities speculation and hedging.

Once again, what I am about to describe is one strategy out of many interconnected in a complete system with one vehicle out of many over different time horizons, etc. But it also works pretty well as a stand-alone at the moment, so it does have real value besides educational.

Option Alpha ran an analysis on “0 days to expiration” strategies that are employed by technical retail traders (remember my poker analogy). They actually make some cash as they have an edge on gamblers. They usually sell iron condors or butterflies which are directionally neutral, entering into a position right after the market opens and they exit in a few hours either through stop loss, profit taking or just closing the position outright. On average, iron butterflies returned 4.62% of the initial premium received. Iron condors had a slightly higher return of 7.94%. That’s a quite nice daily edge for strict rule following (playing only the top hands). Shorter-dated options, expiring in five or fewer days, accounted for about half of all options-market activity as of August, according data provider SpotGamma, up from around one-third three years ago. For popular one-day options tied to the broad S&P 500 index, individual investors made up around one-third of all trades, according to exchange-operator Cboe Global Markets. Lots and lots of “marks” as they say at poker tables ;-).

Surely we can do better using Gamma based strategies than pure time-based or profit/loss-based techniques. Here are two examples of Gamma walls using SPY as an example (thank you Quant Data). What do you think will happen to the price later in the day when you are looking at the following?

First picture, I see that there are no real walls at the Put side, and there are elevated Call walls at $412/413 calls.

This one, you see at Put wall at $405, and some elevation at Calls $411/412.

Would market makers gravitate/flow towards these walls? More often than not, yes, adding precious few percentage points to returns over the long run.

Here is the price action for the first one, and sure enough the move is towards the Call Gamma wall.

The second one records a move towards the Put wall.

Could one use the Gamma walls for the Iron Butterfly pricing? Of course. Backtesting this simple strategy shows that such gamma exposure pricing may alleviate the need for profit/loss and time-based rules especially with ES.

Enough Greeking out. Hope this wasn’t too bad. See you with better analysis soon on the purveyor of fine money business.

Best, Oltac

ps. Re-reading what I wrote, most, if not all of you will hate this piece. I don’t blame you. If, for the chance that you want to see this simple strategy in action, DM me and I will be more than happy to show you how. I ain’t no market maker but I do love big data trading.

ps2. AI Options Trader Monochromatic.