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.

Sports Analytics

In late September I took a trip to Las Vegas for a friends birthday, and because I am too good at math to enjoy gambling in the casinos, I had to figure out a better plan.  So I decided to take some of the code I have created and try to apply it to betting on football.  In early September I spent a few hours putting together two models, one for NCAA football and one for the Pros.

My NCAA model looks at the top 25 ranked teams, and applies various factors to the team and their opponent, then spits out a prediction on whether or not the spread is too high.  I placed some mock trades the first two weeks of the season, and had a success rate of 60% or higher each week.

My pro football model was a little different.  I do a confidence pool each week with a bunch of friends where we assign a weight to each team we think will win, giving our most confident pick a 16 and our least confident pick a one.  I had eight years of data from this pool, ran a few regressions, but didn’t find any strong correlations to find a winner.  I finally decided on the method of comparing the consensus picks each week from this pool, comparing those to the spread and betting on the game with the highest divergence.  Using this method, the first couple weeks performed pretty well – in the 60 – 70% accuracy range (against the point spread).   To be clear, nobody in this pool is a clear expert (definitely including myself), so it is an interesting sample of football fan sentiment.

Off I went to Vegas, armed with a spreadsheet of recommended picks.  The results:

  1. For the top 8 NCAA games that I thought were most incorrectly priced, I won 5 out 8 (62.5%).  Not great, but still positive factoring in the 10% casino take.  Even better, of the 5 games I actually bet on using my spreadsheet, 4 of them were winners (80%).  One of my games  I arrived too late to bet, and two others  I laid off because I wasn’t at all familiar with the teams.  I also made two ‘hunch bets’ on the under of two games, won 1 and lost 1 of those.  So my spreadsheet did outperform my hunches.
  2. For the pro games, my model picked 5 games that were considered most mispriced by the spread.  Of those games, it was correct on 4 out of 5 (80%).  The good news for me was I only made 4 bets and won 4 out of 4 (100%).  I laid off the one loser only because I predicted in our confidence pool against my spreadsheet, so I didn’t trust the spreadsheet.  The bad news is I made another ‘hunch’ bet on a game, and it was wrong, so I still ended up 4 out of 5 (80%).

What did I learn from this experience?   I am not sure.  I only spent a few hours of spreadsheet work coming up with these formulas, so I find it hard to believe after that I found the magic formula to sports betting riches. And the sample set of 3 weeks is too small to make any firm determination.  However, I do think its quite possible that the sports betting market is much more inefficient than the stock market, so with more analytics it may be possible to come up with a consistent winning strategy.  I would guess most sports bets are made on emotion and hunches.  During my research I found a whole lot of data that could be used to create algorithms to find patterns, and finding games where the spread is distorted by emotion.

So for now, I am going to toss the task of a sports betting model on the pile of software projects in my personal backlog.  But you never know, it could be a whole new career ahead of me.

VFS Invest Update: Activision / Blizzard In

I have been watching the video gaming stocks, since before my post on eSports, and in early September I finally took the plunge and took a small position in Activision/Blizzard (ATVI).

With my disposal of Starbucks, I decided  I wanted another Consumer Discretionary stock with solid management.  With the recent weakness in ATVI doe to lowered guidance, I figured maybe now is the time to get in.

Interestingly (and incorrectly, I believe), ATVI correlates most strongly with the technology sector:

Yes it is a software company, but it seems more tied to discretionary spending than technology.  So I think the market is wrong about that one.  And since the discretionary spending sector is pretty hot right now, I am hoping the market figures that out.

Final point is that the CEO of ATVI is Bobby Kotick, who has been at this a long time, and I believe has a vision of where he wants to take this company.  I have historically done well investing in companies with standout CEO’s and I think I have to put Kotick in this category.  Kotick is more of a businessman than a gamer, which I think is what you want from an investors point of view (but perhaps the opposite from a games point of view).  Lots of opportunities for gaming in the coming years, from Virtual reality to E-Sports, and I am placing my bet that ATVI and Kotick will be the winner.

Stock Market Model Update

I have once again been busy in the Stock Market model laboratory, looking to optimize things and get a better handle on market trends.  Over the past couple years my model has been one dimensional, basing predictions on a momentum / buy the dips strategy incorporating yield curve information.  This model has worked out OK for Asset classes and individual stocks, but not meaningful for stock market sectors.

So the big improvement I just incorporated was to integrate multiple datasets into my model. What this means is I now look at historical technical data across multiple datasets, triangulating historical trends to come up with a prediction.

Lets walk through an example. Rocky Brands (RCKY) is a stock I have and it has some great correlations across 3 datasets:

  1. RCKY’s movement appears to coorelate inversely with the 10 month moving average price of oil.  If RCKY moves up slower than the price of oil, its odds of outperforming the following month are increased:I am OK with this seemingly odd correlation, because it is a maker or work boots used by oil field workers.  This influence is often called out in the earnings call, so its likely a valid coorelation.
  2. RCKY’s movement correlates with the inverse movement of the Vanguard Consumer Discretionary Sector index, on a 4 month rolling average basis:A slight coorelation, but if on a 4 month rolling average basis RCKY performs worse than the index, the next month shows a slight outperformance.
  3. RCKY’s movement coorelates with the inverse movement of the US Microcap Stock Index on a 4 month rolling average basis:Again, a slight coorelation, but RCKY is a Microcap stock, so it does tend to catch up if underperforming for a 4 month period.

 

Are these meaningful regressions?  Thats a valid question.  But if you add all these together, here is a summary regression for RCKY:

I think a 35% overall regression is meaningful.  At least it should be better than guessing.  Note that even though two of the three regressions show a current negative score, the overall score is still positive (2.45), because the index with the strongest coorelation was positive.

There are still some flaws to this model, and also still finding minor bugs on a weekly basis.  But I am constantly making improvements to this model, and have a long roadmap of scheduled enhancements.  I will continue to post updates on ideas I have and changes I implement.  Any thoughts on my approach or questions are welcome.

 

VFS Portfolio Update: A Change in Biotech

I think managing ones own stock portfolio as a non-professional can best be compared to an art rather than a science.  Case in point,  over the last few months, I have decided to increase my exposure to biotech stocks.  When making this decision, the first question I asked myself was – what to I know about biotech stocks?  And the answer is very little.  So what should I do?

The obvious solution would be to just buy an index fund of biotech stocks.  However,  before I do that I thought I would try using my timing / momentum model to try to beat the index.  In order to do this, my prerequisite is to have a good measurement in place to make sure my strategy is working.  So a few months ago, I picked up Bristol Meyers Squibb (BMY) because my model showed it to be attractive on a timing basis, and my model has been reasonably accurate on this stock based on back testing:

In August my timing model went negative on BMY after it had gone up a bit, so I decided to swap out of it, and swap into Seattle Genetics (SGEN) as my model flagged that stock as attractive.  In addition, the model has historically been pretty accurate on SGEN too:

Before investing in SGEN, I reviewed the financials and checked out their product pipeline, but to be honest I can’t say that this information played a big part in my decision.  There is no way I can compete with Wall Street on delving into the pipeline and building out revenue models on each drug in the pipeline, and weighing the odds of drug success.  So for me, I have to use the timing model, because this fits the one advantage I have.  My advantage is I invest such a small amount, I can get in an out of stocks without impacting the stock price.  When those Wall Street guys make a decision based on their model, the money they move can really set the price moving against them.  My other advantage is if my model is at least as good as right 50% of the time, I will be ahead of the game as I am not paying a .5 % fund expense fee.

I am not necessarily sold on this approach, and may just start buying an index if my performance is underwhelming.  After a few months Ithen review the results to see if this strategy looks promising.  Assuming this strategy is not a disaster after a few months,  I will keep going with it as I don’t like to rely on short term performance to prove or disprove a theory.  But sometimes that is all you have.

 

VFS Invest Portfolio Update: Starbucks Out

After a long holding period,  I finally dropped Starbucks out of the VFS Invest portflolio.  Even though  I had to take a capital gain,  I decided to remove it because of two reasons.  One, given its recent drop on earnings concerns, my momentum model flagged it as under-perform.   Two, my thesis for holding Starbucks is crumbling.  My main reasons for holding Starbucks was strong management in Howard Schultz, and a good China play.  Well, with Schultz leaving, that strikes one reason, as well as introduces another negative I watch.  My gut has told me over the years that when a CEO leaves, the stock under-performs for awhile. This may be because the CEO always likes to leave on a high note.  Alternatively, this could be because of the turmoil a change in leadership at the top can cause subsequent quarters to underachieve, because of writeoff’s or change in direction of new management.

The second concern is China.  I still think China is a huge opportunity for Starbucks long term.  Short term,  I have a concern about China’s economic performance overall.  Given the tariffs and slowing growth in China, I think its conceivable that the long predicted China crash could happen in the next several months, and I would think Starbucks could get hit by this.

Finally, a third concern is revenue growth is already slowing significantly.  Same store comp revenue is now hovering around 2%, and even factoring in revenue from new stores, growth is slowing significantly:

So for now, I am on the sidelines with Starbucks.  I just don’t like the risk/reward.  I hate selling after a big drop, but I also hate holding a stock because I hope it will go back up.  I will watch and wait on the China story, as well as give the new management a couple quarters to prove to me that the leadership is still high quality.

The Biggest Battery Of Them All

I still think the best solution to power storage is hydro.  It seems a lot more scalable than lithium-ion or other chemical oriented solutions.   That’s why I was heartened to see what Scotland has planned as discussed in this article. Given Scotland’s growing wind power sources, building battery farms doesnt seem to make sense. So just use Loch Ness as your reservoir.  Here is a great view of the plan:

This seems like an obvious solution to the biggest problem with renewable energy, which is its inconsistency in generation.  This solution allows the ‘banking’ of energy in the upper reservoir, so when the sun isn’t shining or the wind isn’t blowing, power can still be generated consistently using the hydro generators.

I hope this is just the start of this movement as renewal energy projects grow, and this becomes the de-facto standard for energy storage and distribution.