Conifers 2020-01-24

During the week ending January 24, 2020, we have a conifer with a 29x return on the call-option side, and a 36x return on the put-option side. For names that are S&P 500 component companies, there were 4 companies on the call-option side, and 12 companies on the put option side. For calculations, I assumed buying the option on Tuesday Jan 21st (no data from Monday, options markets must have been closed for MLK holiday) and closing out the option on Friday Jan 24th. Below is a table grouping the calls vs. puts, and S&P 500 names vs. other.

Call OptionsPut Options
S&P 500Arista Networks (ANET)
Broadcom (AVGO)
Consolidated Edison (ED)
Intel Corp (INTC)
AbbVie (ABBV)
Alexion Pharma (ALXN)
Amgen (AMGN)
Bristol-Myers Squibb (BMY)
Carnival Corp (CCL)
CF Industries (CF)
Dow (DOW)
Edwards Lifesciences (EW)
Hess Corp (HES)
Marathon Oil (MRO)
Merck (MRK)
The Mosaic Company (MOS)
Other SLM Corp (SLM)Herbalife Nutrition (HLF)
Momo (MOMO)
Nutrien (NTR)
ProShares Ultra Crude (UCO)
US Oil Fund (USO)
ViacomCBS (VIAC)

A few quick observations. More names on the put-option side vs. call-option side. Most-represented-quadrant is the S&P 500 put options. Least-represented-quadrant is the Other (Non-S&P 500) call options. When a quadrant is over-represented in 10x options returns, then I wonder if that’s the area with the most market surprises. If so, the surprises of the week were S&P 500 names on the downside. Let’s go through these quadrant-by-quadrant.

S&P 500 Component Put Options

Figure 1: S&P 500 Component Put Options

Other Put Options

Figure 2: Other Put Options

S&P 500 Component Call Options

Figure 3: S&P 500 Component Call Options

Other Call Options

Figure 4: Other Call Options

Displaying the data in this spreadsheet format is probably easier to read, and is definitely much easier for me to publish. Also I’m developing ideas on how to leverage cloud computing and big data applications to dig deeper on the conifer observations. A typical problem well-suited to be solved with big data applications, is dealing with a large dataset where individual calculations are well-defined and repetitive but expensive to scale across the entire dataset.

Disclosure: This article is not a recommendation. Investors should make their own determination of whether or not to buy or sell stocks or options based upon their specific investment goals, and in consultation with their financial advisor.