Introducing Alphacution's Strategy Factor Library

“You can’t connect the dots looking forward; you can only connect them looking backward. So you have to trust that the dots will somehow connect in your future. You have to trust in something — your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life.”
Steve Jobs

For years now, Alphacution has been developing its capability to compare groups of trading firms and asset managers by an increasing list of strategy factors. In such a secretive industry, trade data or any other kind of strategic evidence is scarce. So, even the simplest comparisons – performed on seemingly benign, low-update frequency data – can yield a ton of insight. Alphacution first explored and highlighted this concept in our foundational case study, “The Context Machine” (published April 2018).

In this space, context is king. The key is to maintain the tie between whatever trade data can be found and the entities associated with that trade data. Turns out, regulatory disclosures provide most of the connections between trade data and entities. Each data source that we use – mostly regulatory data – offers a unique subset of strategy factors with monthly, quarterly, or annual update frequencies. From there, contextual evidence helps us solve the rest of what amounts to a “Sudoku puzzle…”

Years into the exploration, Alphacution has yet to find an end to the connections and the contextual insights that can be discovered. In many ways, we are still very much at the beginning…

Some of Alphacution’s most notable early multi-company comparisons come from Feed posts like “Ranking Strategy Speed for Top Quants, Market Makers” (July 2019), as illustrated below, and…

“Renaissance Technologies: Discovering the Omnitrade” (March 2020), as shown below. Both of these examples were based on the quarterly 13F dataset…

Other historical examples of multi-company comparisons can be found in our case study, “The Robinhood Effect” (published July 2021), where Alphacution first compared wholesale market makers along a series of strategy factors provided by the Rule 606 dataset, and the multi-part Feed series, “Heart of the Price Improvement Mechanism” (published beginning March 2023) and “Under 100 Milliseconds” (published beginning April 2023), both of which relied upon the Rule 605 dataset to make comparisons among various market centers – in this case, mainly single dealer platforms (SDPs) – owned by sophisticated proprietary trading firms. [Note that the 606 dataset provides monthly data, quarterly update frequency, and up to 30-day lag. The 605 dataset currently provides monthly data, monthly update frequency, and up to 30-day lag…]

In Alphacution’s most recent example, we go wider and deeper than ever before – including the development of new analytical infrastructure – as part of our unexpectedly lengthy case study on US equity option markets and option market makers. Using the quarterly 13F dataset as its foundation, Alphacution compared up to 19 option market making entities on 84 strategy factors; from the simplest factors – like scope of securities universe or number of positions by product class – to some of the most sophisticated comparisons that Alphacution has ever attempted relating to portfolio comparisons to estimate the number of short (cash) positions by market maker or to rank market makers by an estimate of average position delta…

The tables, below, represent the first version of what will be known going forward as Alphacution’s Strategy Factor Library:

[Note that Alphacution’s naming conventions and other classification methods are subject to change and that subsequent versions of the Strategy Factor Library may include derived data from other sources.]

One of the most fascinating factor groups to emerge from this exercise has been in the portfolio comparison category where we estimate the magnitude of short stock and short ETF securities – aka the “shorts universe” – for each option market maker. This factor calculates the difference between the number of unique cash securities and the number of unique option underlyings in each quarterly 13F filing.

Since option market maker positions typically have a cash position as part of their ongoing delta hedging efforts, scenarios where total unique option underlyings are greater than total long cash securities indicates the maximum number of short cash securities. Similarly, scenarios where total long cash securities are greater than total unique option underlyings indicates the absence of short cash securities…

In either case, any insight into short positions – from data about long positions only – tells us something valuable about the core underlying trading. And though we’ve applied this factor group to option market maker it could easily be applied to other groups of market participants, like managers of mid-frequency / market neutral strategies…

Anyway, as a sample of some of Alphacution’s latest discoveries, the chart below shows a roster of 19 option market makers ranked by the “shorts universe” factor…