Friday, October 30, 2015

What’s Dragging Down Core Retail Sales?

One of the biggest questions in corporate earnings is how the US consumers are doing. We know housing continues to recover, if in a sluggish fashion. We know auto sales are doing well, driven by light trucks. How about retail sales? 

Headline retail sales have continued to increase but showing signs of deceleration. This number is heavily influenced by auto & auto parts though –which we already know is strong. On the other hand, gasoline prices have dropped significantly and that drags down the headline numbers even though it should benefit consumers.

So I consider “core” retail sales – excluding auto and gasoline sales – as a more accurate reading for U.S consumers. The growth in this number is shown below.

Notice that post 2008, growth in “core” retail sales have hovered around 3-5%, slower than the 5-7% range pre-crisis. So I dug a little deeper to see which sector is dragging this down.

The Census Bureau report divides retail sales into 13 categories - motor vehicles, furniture, gasoline, building materials, and so on. Going through the data, I found the biggest drags come from 2 sectors: general merchandise stores and building materials.

The contributions of these 2 sectors to core retail sales growth are shown below.

The declining growth in general merchandise stores might have something to do with offsetting growth in online shopping (although apparently not enough to offset overall sales since the headline number does include e-commerce.) .

The other major drag on growth is the “building materials & garden equipment & supplies dealers” category. Growth has recovered but has not yet reached pre-crisis levels. The good news here is that new home sales – as shown by the link in the first paragraph – should have plenty of room to grow.

Looking out the next few years, I can see a housing pick up driving core retail sales up toward pre-crisis levels. Gasoline prices will also stabilize and be less of a drag on headline growth.

Friday, October 16, 2015

Which Securities are Most Likely to Trend?

The title really should be “finding the least efficient markets – Part I”. But that’s such a broad subject I will just focus on one little part of that here.

Greenblatt’s book “You Can Be A Stock Market Genius” outlined some intuitive ways to find inefficient markets – spinoffs, M&A, bankruptcies…etc. But one of the most common forms of inefficiency is right there on the stock chart – the trend.

If you visualize the price chart of a perfectly efficient security, what would that look like? I imagine it would have sudden gaps up or down as new information comes out, followed by flat lines in times of no news. This is because in a perfectly efficient market, rational investors absorb and digest the same information instantaneously, and that should immediately be reflected in prices.

But often we observe security prices that trend. Prices go up for 3 days in a row, a week in a row…and so on. To me that is proof that the market is not perfectly efficient - there are delays in information dissemination, interpretation, and actions on the parts of investors. Whatever the causes, the trend presents good trading opportunities.

The trend is your friend. But how do you identify securities that are most likely to trend? We need ways to quantify “trendiness”. Here is one simple way to do it: count the number of times where prices move in the same direction (“sequence”), divide by the number of times when prices reverse (“reversals”). This is called the “Cowles-Jones ratio” (CJ ratio).

For example if you have price time series data that goes like this: 1, 2, 1, 2, 1, 2. That’s 5 reversals and not a single “sequence”. The CJ ratio would be 0. On the other hand, if your data is this: “21, 22, 23, 24, 19, 18” That’s 4 times where price moved in the same direction and 1 reversal. (22, 23, 24 all moved in the same direction, then a reversal on 19, and finally 18 moved in the same direction as the last number). In the latter case the CJ ratio would be 4 / 1 = 4. So if you go long the security whenever price first ticks upward, your chance of winning is 4 times that of losing.

Calculating this number for SP 500 components from 1/1/2013 to 9/30/2015, I find the average CJ ratio to be 97%. I expected a number close to 1 so this is reasonable. Below are the stocks that with CJ ratios that are 2 standard deviation above the mean – i.e the trendiest stocks since 1/1/2013.

So the “trendiest” stocks have CJ ratio around 1.1 – 1.2 range. A simple strategy would go something like this: go long whenever you see prices shift directions and go up; and short if prices reverse and go down. Your win percentage would be better than 50/50.

Now check out the common currency pairs. Total trading days are more than those for stocks because the stock market get various holidays off.

Note that even the least trendy FX pair is more likely to trend than the trendiest of stocks! That makes sense to me. The forex markets are full of non-economic players like central banks and commercials for whom profit maximization is not the top priority. Then you also have mom and pop participants. When my dad wants to buy some NZD he literally goes to the local banking branch and buy them! That surely creates lags and opportunities not seen in the stock market.

These numbers change depending on what time period you use. But in general I do find currencies to be trendier than stocks.

There are other ways to measure trendiness – perhaps one can quantify autocorrelations, or run backtests using simple moving average crossover rules and then rank the results. As I learn more ways to detect trends (and get more mathematically skilled) I will post my discoveries.

Friday, October 2, 2015

Notes on Archer-Daniels-Midland (ADM)

ADM is one of the largest grain processors in the world. The 4 major segments are 1) Agricultural Services - which store/transport/trade commodities, 2) Corn Processing – this turns corn into sweetners and starches. Importantly this segment also includes ADM’s ethanol operation. 3) Oilseeds Processing – where ADM does crushing & origination. Soybean related products are key here. 4) Wild Flavors – a new segment that does specialty food ingredients.

Why am I looking at this in the first place? My bull hypothesis is as follows: 1) As a processor that takes soybean and corn as inputs, weakening commodity prices should help ADM’s margins. 2) Despite the cyclicality, over the long run demand for ADM’s products should be very stable. 3) As the world consumes more proteins, ADM’s volume should grow at higher than GDP.

I also think ADM is a good company to track due to availability of related data. I can monitor data from commodity futures and USDA to constantly update, prove, or disprove the above hypothesis. Things like soybean crush spread, ethanol prices, currencies are available in real time and inform one’s view on ADM.

Segment Contribution and Drivers

Here is a quick rundown of each segment’s contribution to operating income (last twelve months), as well as their drivers:

Oilseeds processing (42% of operating income)

  • The key grain here is soybeans so I compared current soybean crush spread against historical levels. Current industry crush spreads (see appendix at the end of the article) are near all-time high but declining, which points to downside for ADM. My first bull hypothesis – that declining commodity prices should help ADM’s margins –thus appears already played out and in fact fading. 
  • However, ADM’s oilseeds margins (in terms of $ per ton processed) were stable the past few years even as industry crush spreads fluctuated. My guess is ADM will be relatively stable when industry spreads decline as well. But I certainly would not count on much upside from margin expansion. 

Corn processing (24% of operating income)

  • Operating profit from ethanol was down ~$220mm in 1H2015. Some of that will be recoverable as ethanol margins recover, creating upside in the segment.
  • However, it seems that analysts are already building in ethanol price recovery (perhaps due to expectations of oil price recovery). Consensus has EPS of 3.08 and 3.40 for 2015E and 2016E. This 10% growth will be hard to come by without ethanol at least stabilizing. Besides, the days of $100 oil are over and ethanol margins are unlikely to fully recover to past levels.

Agricultural services (27% of operating income)

  • This is storage/transportation/trading…etc. Historically profit here relies on level of US exports, which is a function of how competitive US is versus say, Brazil. With USD strong and the Brazilian real weak, this segment is unlikely to see much advantage. The near upside here is 1) El Nino somehow destroys Brazilian crops, and 2) US has a great crop. 

Wild Flavors (6% of operating income). This could be a growth area for years to come but not enough to offset the importance of the other segments.

Upside Assessment and Current Action Plan

Looking through the main segments, I just don’t see much earning upside beyond those already factored into the consensus. Thus returns will have to come from multiple expansion, which will likely happen if ethanol stabilizes and exports don’t fall apart. In terms of valuation, right now it trades at 11.9x TTM vs 10yr median of 13.3x. That’s a 12% upside from recovery in multiples. In terms of downside, soybean crush margin can weaken, ethanol stabilization can come later than expected, and Brazil and China can enact policies that hurt US competitiveness and export levels.

This is not enough for me to get in. For now I will sit on the sidelines and wait for fundamentals to get better.

Appendix: Historical soybean margins

I calculated these from front month futures data and the formula Soybean crush = soybean oil (in cents/lb) * 0.11 + soybean meal (in $/short ton) * 0.022 - soybean prices (in $/bushel).  The crush spread is still higher than historical because soybean meal is a larger contributor than soybean oil, and soybean meal prices has not came down as much as the others.

historical soybean crush spread ($/bushel)

soybean complex historical data