Category Archives: Investing

UFPI – To Sell or Not to Sell

The other day I was looking over my portfolio to make changes based on what my valuation model suggested. One of the stocks in my top 10 holdings, Universal Forest Products, Inc (UFPI), came up as a stock to underweight based on relative valuation. UFPI is a pretty inexpensive, low growth stock, and it had a small run in the last few months.

As I often do with stocks in which I have a short term gain, I give some pause to selling. My model predicted a .44% increase in May, so its not like I was bearish on the stock, just thought there might be better alternatives in other securities. So I took a quick look to see if it might make sense to sell a covered call against my position, collect some extra income since I don’t think the stock is going to move much in May. So I pulled up the options chain, and saw an interesting tidbit:

It is interesting to see that the volume on most UFPI options is zero. This is a small 1.89 billion market cap stock, not followed by many people. But curiously, in this list of zero options, someone tossed down a 40 contract trade betting that the stock would go from its current price ($31) to over $35 by May 17th. Granted that is only a $400 investment (40 contracts @ $.10 * 100 shrs a contract), so this isn’t likely the so called ‘smart money’, but it is curious. UFPI releases earnings Tomorrow (April 24th) after the bell – perhaps some small roller is ‘playing a hunch’ and trying to earn some extra cash.

At any rate, this raised my curiosity enough for me to hold onto my position until after the earnings release. I am not in any big hurry to sell, and maybe this is telegraphing some good news around the corner.

The Trouble with Boeing

I have owned Boeing stock, but sold out of it last year due to a couple reasons. First, its a very large cap stock that grew a lot, and I didn’t understand why it jumped so much. My current opinion is on large cap stocks, you are competing more against institutional money that really models the company and the profits, so I don’t know if I want to try to outperform those companies with those resources.

The second reason I unloaded Boeing was more anecdotal. A group of guys I socialize with have and had worked for Boeing for the last 30 years. They were always very positive on Boeing and its management – until a few years ago. All of these employees were disgusted with management, and felt things were going downhill. I try to make a point in investing in superior management, and so this change in sentiment was enough to convince me to stay away.

With the recent drop in the price of Boeing stock due to the whole 737 Max events, I gave some thought to re-entering Boeing stock. The consensus had been that the problem is a software problem, easy enough to fix with software update. But more info is starting to come out, and now, I am beginning to think weak management is showing its hand.

This article on Quartz provides a great overview as to the issue surrounding the 737 Max. To summarize, when Airbus came out with a fuel efficient A320, Boeing had to quickly adapt the 737 to stay competitive. And to adapt the 737 to the new engines, they had to move the engines on the wing. This led to some handling problems, so they created software ‘as a kludge’ to offset the handling problem. So this is much more than just a software fix – the need for the software is due to a design flaw, which is additional complexity in the airplane. It seems this need to rush out a new plane for competitive needs may have led to deadly consequences. Also hitting the news is headlines about the approval process and the training provided to the airlines, none of it positive for Boeing.

So now, given this complexity and the headline risk, why would an airline pick the 737 Max over the A320? It seems this puts Boeing at a real competitive disadvantage. Indonesian airline Garunda already cancelled their orders for the Max – I would guess more will follow. Perhaps unsurprisingly, Boeing received no new orders for the 737 Max in March, and only 10 orders in the first quarter of 2019. Add on congressional investigations into the FAA certification process, the extended groundings, passenger perceptions of the airplane, and pilots concern related to the training, and I think the story gets worse before it gets better.

So if your bull thesis on Boeing is based on this 737 Max problem just being a software fix – think again. I think this could be the sign of cracks in the company culture and missteps by management. I think this plane that management forced out for competitive purposes could end up being a long term disaster for Boeing.

10,000 Millionaires

An interesting thing to ponder based on this article –

Thousands of New Millionaires Are About to Eat San Francisco Alive

With the Lyft IPO yesterday, this kicks off the creation of a new wave of an estimated 10,000 silicon valley millionaires. In addition to Lyft, Uber, Airbnb and Pinterest are expected to IPO in 2019, and given the compensation structures of San Francisco startups, this will result in a huge wealth realization for all those employees who hold stock options.

Should this number of newly minted millionairres materialize, it raises some interesting social issues. What happens to a city when a large amount of money floods a city (San Francisco is approximately 7 square miles)? The supply and demand of housing and premium services is going to be off the charts, likely worsening the homeless crisis currently in San Francisco.

It will also be interesting to see this wealth reverberate up and down the west coast. Historically housing prices in the Pacific Northwest correlate to California, so I would assume we will see ripples of this wealth bomb.

I fear this will also make the growing divide in the US between the technology disrupters in Silicon valley, and the declining income of those unskilled workers who lose their jobs to the disruption of the technological revolution. With all that money from the stock IPO’s going into those companies, it will only speed up the progress of the disruption.

So it will be interesting to see what happens in 2019. The impact on San Francisco will be interesting to watch, and hopefully it will drive some discussion about how to deal with this continued disruption across the country.

The Stock Market and the Fed

2019 has started out as an interesting year for the stock market. The year was set up with a disastrous December 2018, leading to some gloom and doom over the holidays. I recall telling friends in December (following the 5% Christmas Eve drop) that I thought there was a decent chance January would be positive, and if so, take that as a selling opportunity as the rest of the year will be crap.

I was right about January, as the market (as measured by the S&P 500) was up a solid 8%. Time to sell? Now… I am going to answer maybe not. In December the Federal Reserve was signalling 3 rate hikes, and there is an old Wall St saying – ‘don’t fight the Fed’. The saying refers to when the Fed is raising rates, be cautious. Now it turns out, in January the Fed has signaled that it may go more cautiously, presumably because the stock market threw a tantrum. Now there are predictions of a rate cut in 2019, even though the economy is presumably as strong as it has been in recent years.

In my opinion a rate cut may be economic malpractice – but even worse (from a fiscal responsibility perspective) there is now talk that the fed may slow quantitative tightening. This you may recall is the selling off of all the securities the government bought up during and after the 2008 market crash:

Federal Reserve Balance Sheet Expansion

Of course we were told that when good times return, the government would sell off those securities and shrink down the balance sheet. And maybe it’s no coincidence that the stock market had a down year in the first year of quantitative tightening. But now there is talk of stopping this drawdown in 2019 or 2020, presumeably over concern that the economy is slowing too fast. The Federal Reserve now appears to be overly sensitive to the movements in the stock market, which for stock investors I guess is a good thing. This removes one big negative factor for the market I had going into 2019.

If the Fed has our back and is working to protect shareholders, I guess I am less negative than I was over the holidays. Other negative factors still exist, such as slowing growth in Europe, continued governmental dysfunction (i.e. the past and potential future shutdown), and Brexit. So I am still not calling for a great year, maybe just not as bad as I thought. I still think we have some major economic reckonings in the future, it’s just that the Fed is now willing to ‘run up the credit card’ to delay the economic downturn.

eMagin – Still Just Potential

My latest article for Seeking Alpha was just posted – titled eMagin Is Still Just Potential. This covers a company I followed for a long time -eMagin. They make high quality micro-displays, and have been trying to crack the virtual reality market for a long time. I have been waiting / hoping for them to find success, but so far it has been elusive. Recent developments have knocked the stock price down below $1, at a time where it would appear it is starting to perform. It’s an interesting story (I think..).

VFS Rating – RIP

After 9 years I have decided to replace the old Puget Investor / Invest.vfsystems.net VFS Rating.  The VFS Rating uses various fundamental stock metrics to try to determine if the the stock is under or overvalued.  It has never been as accurate as I had hoped, for a variety of reasons.

As of 9/30/18, here are final results of the VFS Rating:

Note that over the life of the VFS Rating, on stocks rated 10 would on average outperform stocks rated 1 by .09% a month (.01x above represents the monthly premium, so on average a vfs 10 rating would equate to .10% a month return, where a 1 rated stock would return .01%).  There is high variability in these numbers, so I think this might be overstating the accuracy of the rating, but at least it is a positive correlation.

Note that on average the stocks in the portfolio outperformed the market by .12% a month, which annualized is over 1% a year over the S&P 500, which I like.  However, that number cannot be credited to the VFS Rating, that number is more a function of the stocks I chose to add to the portfolio.  I would like to think the over 1% number is due to my brilliance in picking stocks,  but I wonder what that number would look like had I not included Amazon in my portfolio during that time.

I am now taking what I have learned from the calculation of the VFS Rating, and using some more modern programming techniques to improve it.  Instead of a VFS Rating, I hope to come up with a target valuation of a stock, from which I can discern the discount or premium the stock market has applied to the stock based on fundamental indicators.  This is very tough, because the valuation of a stock (in my current opinion) is primarily based on the growth prospects over the next 10 years – which is a very subjective (and inaccurate) forecast.  But I have some ideas, and if I set up my measurements right I should be able to see if it can be done successfully.

In the meantime, I plan to rely on my technical indicator that is on the invest.vfsystems.net site.  This indicator has shown some promising early results. Since November 2017,  the performance of this indicator can be described as Y=.077x + .294.  This formula is a little convoluted, but my technical score goes from a -10 to a 10, so a stock with a score of 10 out performs a stock with a score of -10 by approximately 1.5% a month.  Again, this is a small sample set (13 months), but it is promising.

Finally, to repeat what I have told my friends – if you do plan to invest in individual stocks, the most important thing is to get your measurement systems set up.  There are so many wrong strategies out there its important to measure your strategy and make sure you are beating whatever index you would invest in as an alternative.  I have a number of measurements set up, so hopefully now as I embark on this new formula, it will show positive results.  If not.. it could be back to the drawing board once again.

Nordstrom’s 21st Century dilemma

One of the biggest question marks in investing over the last 10 years will be what happens to retail in the online age.  I have been following Norstrom’s off and on over the last 20 years as it is a high quality retailer with dedicated family management.  However, in the last few years I have been out of Nordstrom’s as  I have been avoiding retail all together until more clarity as to the future of retail is visible to me.

One of the statistics that most impressed me about Nordstroms was it was growing it’s web presence at something like 20% a year for the last few years.  It seemed to be one of the few retailers that has a good online strategy, and is keeping itself Amazon-proof.

However, that may be a curse in disguise.  As per this article on Business insider, this expanding retail presence is causing a problem, with digital sales now accounting for 30% of Nordstrom’s sales.

So they may be successfully fighting off Amazon (for now) – but it is clear they are cannibalizing their in store sales.  Online sales grew 20%, while overall sales grew around 3%.

Is this bad?  In Nordstrom’s case, I am now changing my mind, and I think this is a sign of trouble.  In my opinion, Nordstrom has differentiated itself by having premium stores, and excellent customer service.  Translating this differentiation to the web is a whole new challenge, other than the oft mentioned ease of returning things to Nordstrom.  Competing for a premium experience on the web is going to be difficult, especially with Amazon and other competitors (i.e. upstart Stitchfix) building a model around personalization.

My prediction is that Nordstrom’s will have to reduce their average store size, as it probably no longer makes sense to have the same level of inventory in store when people are increasingly buying online.  If this is the case, I think the cost of shrinking and retrofitting stores will exceed the (assumedly) reduced rent cost for the next few years, all the while spending to fight off online competitors.

So while I think Nordstrom’s is still a great brand and premium shopping experience, I am staying away.  I think the moves the company needs to make in the future may further cloud how brick and mortar retailing needs to change in order to survive in an online world.

The Super Bowl Indicator

I have been spending a lot of time lately analyzing regression and related factors.  As my stock market model creeps toward the realm of artificial intelligence, making decisions based on related data sets, the biggest decision as a programmer / data scientist is, how do I know a coorelation between data is predictive vs. just a coincidence?

So if you are reading this post to try to find the answer to that question, you can stop reading now, because I have nothing for you.  Right now I am just using my gut (Feeding a computer datasets is probably a lot like parenting – only tell your children the things they need to know, don’t confuse them with too much data as they build their moral compass).

Anyway, as I think about all these issues, every time I think I find a meaningful correlation,  I think back to the Super Bowl Indicator.  Are you looking for a sure-fire predictor of stock market performance as measured by the S&P 500, with a track record of being correct 80% of the time in last 50 years?

Then maybe your strategy should be if an AFC team wins the Super Bowl, go all in.  If the NFC wins, take all your savings out of the market.  Of course I am not serious, but it is curious that this is one of the best predictors out there.   Since the year 2000, it has a 66% accuracy

I bring this up because this is a great example of the problem faced by software developers/data scientists such as myself.  As artificial intelligence drives more and more software in our world, we will expose new flaws in the software development process.  The programmer’s dilemma will be to decide what data to expose to the computer to help it make decisions.  Intelligence (either artificial or human)  is built up off experience and/or data available.  If  I just built my stock model off the Super Bowl indicator, rather than looking at other technical or fundamental factors, I would have easily outperformed myself and just about every other financial advisor out there.  Maybe I should spend more time trying to justify that it is a meaningful coorelation, rather than just a coincidence.  As of this writing, I still can’t do it, and I am ignoring it.

So don’t consider this post investment advice, just a thought about how machines will be making decisions in the future, and the flaws that will be programmed in to future software based on the decision by the programmer about what data is meaningful.  There will be some mistakes made.

And if you see an investment adviser who touts an 80% success rate over the last 40 years,  ask him or her if they know who won the Super Bowl last year.

The RSI Experiment

Awhile ago a wrote a post regarding whether or not RSI looked like a meaningful indicator. After writing that,  I decided to do an experiment to see if I could come up with a strategy based on the relative strength index (RSI) of a stock.

First off, there are many ways to calculate RSI – short term, long term mid term, but I decided to look at the short term 10 day moving average RSI – for now particular reason other than I had that data available.  The strategy I used was when a stock goes above 70 on the RSI sell a call or buy a put with the assumption that it is likely to perform poorly going forward, and when the RSI falls below 30 buy a call assuming it will outperform the market going forward.

I set up a process that each week, my stock model sends me an email with candidate stocks where the RSI is above 70 or below 30, and I would pick from that list likely candidates and record it in a spreadsheet.  An important point:  Anytime  I try out a new strategy I do it via ‘paper trades’ first – instead of actually making the trade, I just write it down or put it in a spreadsheet.  I figure if I stumble across a winning strategy, it will work for years to come so I can afford to wait awhile before I actually put it in place.  Since I tend to stumble across more losing strategies than winning strategies, this practice has served me well over the years.

The results?  See below:

In a word..  Ouch!  Had I actually pursued this strategy it would of been brutal.  Note that for calls I sold (those were hypothetical covered calls), my profit calculations include the opportunity cost.  For instance, for the June 23rd sell of Costco calls  I would of collected $1000+, but at time of expiration the stock was $884 over the strike price + call price, so I missed out on that (assuming I had the stock to begin with).

For now,  I have set this strategy aside, and am going to think about what I learned for a few months.  Maybe I stumbled upon a successful strategy by doing the opposite of what I originally hypothesized.  Perhaps anytime a stock goes above the 10 day RSI, you should buy, and when it goes below sell a call on it.  Or maybe the time premium of options just makes it too hard to be successful.  I don’t know.  What I did reinforce for sure is the value of practicing via paper trades before committing to a strategy.