Th is last month, I officially established my consulting business, Plans and Clues LLC, with the state of Oregon as well as a US Fedral EIN. At least one of those things puts my business name and address in the public domain … the state of OR. So there are either people or bots scanning the public domain for new businesss so they can contact you …
They sent me this very official sounding and looking document that could easily be mistaken for a state or fedral agency requiring a fee be paid.
But if you read the top paragraph on the right side, if clearly states that this same documentation that they are asking you to pay for is available for free from the state of OR.
This is a scam that they are intending you do NOT read and just go ahead and pay. If not, how many businesses will stay in business if the EXACT same product is available for free from the government?
A conceptual inference struck me the other day as I was considering buying additional Apple stock for my ‘invest and almost never sell’ portfolio – there’s a 5-10 year horizon for this capital. This go around I sold PUT on Apple stock and if it gets assigned, I will be content – there is no FOMO as i can repeat this. But maybe in the inference path I used will be helpful for thinking about weighing investments leveraging competency in machine learning which I believe will be a major investment thesis over my target time horizon – 5-10 yrs.
Here are my assumptions wrt machine learning capabilities
The larger the data set available for the machine learning and correction, the more accurate the machine learning – basically the faster and better it learns and is then able to execute
The cleaner the data set the more efficient, effective and timely the same machine learning
If you buy those, then looking at Apple there a couple of like companies with such a broad and large data set that can be leveraged to provide better and better (i.e., demanded) user services and products, e.g., Apple, Amazon, Google, Microsoft are the most relevant. There are others that may have large data sets but I do not think they have the same breadth and quality, e.g., Facebook, Comcast, ATT, Verizon.
Apple’s data may not be the largest, but i think it is the most controled and hence the cleanest. Microsoft is probably the second cleanest data set but not as broad across device types, usage models and connectivity modes. While Google and Amazon probably have the largest and most diverse data sets, neither have the same control over data quality that Apple and Microsoft have.
Based on this inference thread and my current position in Apple, I will continue to add to my position until the narrative changes or my allocation of Apple exceeds 10% of my managed portfolio. Microsoft will be added to my target list with an upcoming deep dive on 5-10 year investment thesis and entry.
There are two companies that I am currently invested in – I own shares in both companies, and will most likely increase holdings over the next 12 months. Both companies met w/ analysts recently and both had very clear strategy narratives, imho. They were explicit, easy to understand and map to my IOT product experiences.
I presented at short view on “Plan for Proft” for a local Chamber of Commerce Lunch event this week. A framework to help people start thinking about the needed clues to create a plan to meet their unique and individual profit goals. http://plansandclues.org/files/PlanforProfit-2.pdf
Caveat: These slides are not that helpful for people who were NOT in the talk itself – this is not a “leave-behind, stand on its own” collateral.
I looked at some scenarios based on current prices as of Friday, Nov 2 to identify if there is a compelling covered call platform to derive medium risk income. I selected 3 companies that I would consider owning for their long term prospects, but would not cry a river if the position was taken at a profit: HBAN, CY and NOK. Other criteria included: a good dividend, predictable, sufficient option volume and stable pricing over the next 3-6 months (as the market goes so will these).
Here is the simple comparison
HBAN Price at analysis 14.29 Cost basis 500 @ 14.29 ($7145) Possible 1. $14 Call Dec 21, 2018 $0.62 a. Outcome with 500 shares (5 contracts) i. A - Calls sold but not assigned - $310 gross (4.3%) ii. B - Calls sold and assigned - $155 gross (2.1%) 2. $15 Call Dec 21, 2018 $0.25 a. Outcome with 500 shares (5 contracts) i. A - Calls sold but not assigned - $125 gross (1.7%) ii. B - Calls sold and assigned - $480 gross (6.7%) CY Price at analysis 13.51 Cost basis 500 @ 13.51 ($6755) Possible 1. $14 Call Dec 21, 2018 $0.60 a. Outcome with 500 shares (5 contracts) i. A - Calls sold but not assigned - $300 gross (4.4%) ii. B - Calls sold and assigned - $545 gross (8.0%) 2. $15 Call Dec 21, 2018 $0.27 a. Outcome with 500 shares (5 contracts) i. A - Calls sold but not assigned - $135 gross (1.9%) ii. B - Calls sold and assigned - $1424 gross (21.0%) NOK Price at analysis $5.80 Cost basis 1000 @ 5.80 ($5800) Possible 1. $5.00 Call Dec 21, 2018 $0.85 a. Outcome with 1000 shares (10 contracts) i. A - Calls sold but not assigned - $850 gross (14.6%) ii. B - Calls sold and assigned - $0 gross (0.0%) 2. $6.00 Call Dec 21, 2018 $0.17 a. Outcome with 1000 shares (5 contracts) i. A - Calls sold but not assigned - $170 gross (2.9%) ii. B - Calls sold and assigned - $320 gross (5.5%)
The CY purchase and $15 Call with these assumed prices appears the best idea. This idea fits my objectives of increasing greatest cash flow in worst case scenario (i lose the position) as well as the probability of losing the position (lower w/ CY @ $15) … not a recommendation to execute this trade, but a look at how it might be done and how I came up with the idea.
A very short thought on data analysis and the use of data to make decisions. I often hear people in different domains reference “data based decision making”, “data driven business”, and the list goes on …
But what so many people follow is the “Spray and Pray” approach of data analysis. They collect as much data as possible, they spray it on the wall for everybody to see and pray that something meaningful surfaces.
What they miss is that any data analyst worth their salt would do the following:
Identify the most important data
Explain why that data is important
Relate what that important data means and its shortcomings / limitations
Recommend what actions should be taken based on that data (non action is appropriate response as well)
A recent experience and additional observations have created a very jaundiced view of a group of traders who take advantage of less experienced folks. They are poachers with all the negative connotations of that term. Let me explain with an example
I had a short position in RUTH. I stupidly placed a stop lost / limit order to protect my capital from a price increase above my limits. I absent-mindedly left that stop loss order open over night … it was snapped up first thing when market opened at about $0.25 above the next trade. I looked at RUTH trading history and there are several of these very odd very early trades way above the market … Poachers taking advantage of stupid people like me.
Not to self: Never leave stop loss orders open overnight and especially in thinly traded stocks like RUTH.
After a very difficult trading month so far in October and a couple of interesting SA posts (Jeff Miller’s weekly trading post) and a new author for me posted I have finally figured out a way to describe the change: the tides have turned and the winds have shifted. Mary Poppins like …
Previously, the trading tides were pushing upward so trades were easily identified with consolidation at support points and falls from strong resistance. The winds were blowing upward so lower values on dips made sense … and consolidation support points held – they were the floor.
It seems that the tides have turned now after watching support levels break down consistently … the dips are now hard to stop (supports are like false bottoms). The trading tactic now looks to be waiting for consolidation bottoms and upward lifts with volume and strength through weak resistance points. This may reduce potential gains on the upside (longs) … shorts are also complicated as there are no support levels below (what can i logically expect for exit and profit)?
While I fully admit that i am not an expert technician and use fundamental DCF valuations as the filter for my trades, for me at least, the tides have turned.
A provoking post on Seeking
Alpha this morning that prompted deep thinking on the money flows from the
large changes in oil prices over the last 5 years. The basic gist which
totally makes sense is that the oil profits are not as important as the consumers’
total cost for oil. The lower their costs, the more they spend elsewhere
in the economy. The inference then is that higher oil prices are totally
deflationary – they reduce broader consumer purchasing … it doesn’t really
matter if the oil profits end up in US, Canada, Russia or Arabian Peninsula.
I have been talking about
this for several months, maybe even a year or so, but not to the technical
depth that these two people are this past week. I think this is worth
considering and baking into your long-term risk management variables.
My pontifications have been more cultural evolution derived but these
guys are helping me understand the investment implications.
GDP ‘3rd’ estimate was released this morning. One paragraph i found most interesting: “The acceleration in real GDP growth in the second quarter reflected accelerations in PCE, exports, federal government spending, and state and local government spending, as well as a smaller decrease in residential fixed investment. These movements were partly offset by a downturn in private inventory investment and a deceleration in nonresidential fixed investment. Imports decreased after increasing in the first quarter.”
I bolded the points i focused on. Government spending was a key catalyst in the figures – debt spending in great degree (?)