Mathematica work-flows. Retrieving, processing and presenting data. #2 Processing

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The State’s in a fiscal hole: will they raid their insurance trusts?

“The State’s in a fiscal hole: will they raid their insurance trusts?” can be found here. Please bookmark my new site.

Retail sales: no boom, just doom and gloom

The U.S. Census Bureau released the Advance Monthly Retail Trade and Food Services Survey late last week and on the face of it it seems like good news. By that I mean good news that IS good, whereas “less bad” has often been taken as being good in recent times. Seasonally adjusted retail sales were up 1.3% from the previous month and 1.9% year-on-year. Of course this is a survey of retail sales, with a reported error margin of ±0.5%, not a tally of actual data. So how reliable is it?

At the risk of sounding like I wear a tin-foil hat, should we believe government surveys? I figured that the best way to satisfy myself that this survey gives a credible picture of main street was to compare the survey data with states sales tax receipts. To do this I downloaded all the data via Mathematica and used Mathematica to analyze the data and make the plots shown below.

Quarterly sales tax data reported by the states is collected by the US Census bureau and can be found here. Historical survey data is available here.

The first step in making the comparison was to convert the monthly survey data into quarterly data. The next chart plots total quarterly sales taxes and quarterly retails sales survey data. Note that I’ve included items such as motor fuel sales taxes, and taxes on alcohol and tobacco, in the total sales tax number.

Trade data points to L shaped outlook.

At first glance the contraction in the trade deficit for October looks positive. I’m generally wary of seasonally adjusted data — not because seasonal effects don’t exist but because I prefer to see the raw data and the algorithm used to do the smoothing. Non seasonally adjusted data for the trade in goods is available here.

A seasonal plot of the trade deficit, derived from 22 years of data, is shown in the next chart.

From that chart we see that October is typically a larger deficit month. That should mean that a reduction in the month-on-month deficit in October 09 is good news. However October (along with March) is typically the biggest month for exports, and October is the biggest month for imports. While exports of goods grew 9.7% in October, compared to an average October growth of 7.4% from 1987-2008, imports were up 3.9% rather than the 8% seen on average from 87-08. Does that signal weak consumer demand? That interpretation seems consistent with what we know about retail sales and rail freight movements.

Outbound and inbound container movements from the ports of Los Angeles, Long Beach and New York/New Jersey are shown next. Note the clear seasonal pattern in the inbound container data.

The outbound container data, while oscillating a bit, seems fairly flat.

Is this data positive? At best we need to see how the data tracks between now and the end of Q1 2010 but to me it looks more indicative of an L shaped recovery than an indication of a V shaped rebound that many are wishing for.

(Mathematica was used for all retrieving, processing, and presenting of data in this article)

Rail freight: proof of an economic train wreck?

Total carloads and intermodal rail freight data are plotted in the two charts below as 4 week moving averages. The improvement we’re seeing needs to be viewed in the context of a trending increases in freight that occurs throughout the year through to October. The data certainly lacks the characteristics of a V shaped rebound. At this stage L shaped looks like a better letter to describe the economic path.

China has now become the biggest risk to the world economy (?)

While the Australian economy is doing better than most at the moment, it still has its problems — while unemployment is low, a large number of people were forced into part time work during the short recession, and has its concerns — economic growth is too heavily dependent on China.

Interesting coverage of a tour of Australia by a former member of the China Central Bank Committee can be found here:

http://www.theaustralian.com.au/fears-prompt-an-rba-rates-rethink/story-e6frg8zx-1225804353146

On a related note an interesting article “China has now become the biggest risk to the world economy” from the UK:

http://www.telegraph.co.uk/finance/comment/ambroseevans_pritchard/6575883/China-has-now-become-the-biggest-risk-to-the-world-economy.html

Some valid points/concerns in both articles. I’ve never understood why we have a World Trade Organization that acts as a global policeman/adjudicator, why tariffs and subsidies are generally banned, why dumping of goods is banned, yet currency manipulation by countries is allowed. It doesn’t make sense … to me anyway.

Commodity charts show no signs of much economic recovery

These are buoyant times for (some) commodity investors, and (some) commodity producers, what does it mean in terms of a global recovery?

A common feature among people drawing conclusions about the economy from commodity markets tends to be a sole focus on price, and even then only prices in exchange traded commodities. If buyers out number sellers prices rise, so rising prices mean a return of global demand which in turn means global recovery — right? Well not quite, despite the enthusiasm displayed in many articles pushing that line. The flaw in this line of thought is that the buyers who are out numbering sellers in commodity markets need not be end users of the commodity. There can be speculative demand from speculative buyers just as there can be economic demand from industrial buyers. From the point of view of the markets, or the commodity producer, it doesn’t matter who the buyer is: more buyers than sellers means rising prices. The problem arises when you use market price — at face value — to draw conclusions about the economy. Since most of the people who connect price to economic recovery without digging deeper tend to be economists, I’m assuming economic theories don’t allow for speculators.

Let me focus on metals and minerals (e.g. iron ore, coal). People don’t eat these (unlike food commodities), they are used as inputs for industrial production. As global demand picks up we would expect to see increasing consumption of metals and minerals. When buyers out number sellers prices rise. This is clearly what is occurring at the moment, you only need to look at price charts–which I haven’t reproduced here because we’re all familiar with them. But what if the majority of buyers are not end users of the commodity and do not take delivery, i.e. speculative buyers? If the buyer doesn’t take delivery then you would expect stockpiles to rise. In other words if speculators are out numbering industrialists you would expect to see quantities of the commodity that are available, i.e. stockpiled, to increase. In the first chart we have the base metal stockpiles on the London Metals Exchange.

Copper: What the bellwether metal tells us about a recovery.

A recent article from Reuters titled “Economic optimism lifts US copper to 13-month peak” provides an example of the leap of faith many are taking when they observe rising copper prices and conclude that global economic prospects are brighter. The article included a lot of bullet points, but most related to matters unrelated and unconnected to economic activity.

Some of the discussion related to charting. Terms such as “double bottom neckline” and “resistance level” may have meaning among charting traders, but those terms should be confined to discussions relating to where people betting/gambling/speculating on the copper price see the price heading. In other words speculators who are long copper because of a chart pattern shouldn’t lead a journalist or sub editor to frame the article around economic matters. The predictions being made based on “double bottom neckline” are unrelated to whether or not economic optimism exists and are unrelated to economic data. For clarity I am not saying a trader can’t profit from these things, but merely that they are what they are, and what they are is independent of economic fundamentals.

Another bullet point related to housing sales. The assumption is presumably that, as a so-called bellwether metal, a rise in housing sales means a rise in construction, which means a rise in copper consumption, which means an expectation of a rise in demand. Below is a chart of US copper consumption this decade. For reasons unclear to me consumption has been falling all decade. The International Copper Study Group (ICSG) has forecast consumption in the USA, Europe and Japan to be down approximately 17% this year.

US copper consumption

US trade data shows a limp recovery after crash

An interesting article over on SeekingAlpha recently “Baltic Dry Index’s fall misleads investors.” The author pointed out that while the BDI is down, there is also a surplus of ships, so that the BDI weakness is not entirely due to lower levels of trade. Among the discussions that followed was a comment/question about the seasonality of imports and exports. I collected data from the Port of Los Angeles and trade the Census Bureau. The chart below shows US exports of goods (not services) plotted against port activity at Los Angeles. Not surprisingly a good correlation but I’m not sure if you can glean any seasonal trends from the data (and the data pre-2000 is much the same).

US goods exported and outward container TEUs

The import data correlation is not quite as good as the export data but it is tempting to conclude that some sort of seasonal pattern exists with import lows earlier in the year. Incidentally for a very clear example of a seasonal trade pattern have a look at imports from China.

US goods imported and inward container TEUs

So part of the rebound in import activity could be viewed as a seasonal pattern. We can assume that the decline in imports reflects the fall in consumer demand and/or a fall in anticipated consumer demand by importers.

Pretty much every metric you can find collapsed (overshot?) early in 09. The data shows an increase in containers being moved since the numbers collapsed, and an increase in trade in (declining) dollar terms, but also shows that we are some ways off from a true recovery.

Iron ore and coal. Why it is all about China.

We hear a lot about the BRIC countries. The plot below — world crude steel production this decade as of end of August — pretty much sums up why it is all about China, the “C” in BRIC, when it comes to iron ore and metallurgical coal demand. The plot shows the enormous growth in Chinese steel production this decade, compared to the low growth in the other three BRIC group and the rest of the world.

world crude steel production

Iron ore producers took a hit in pricing negotiations this year–admittedly from high price levels in 2008. The pick up in Chinese steel production this year is good news for iron and coal producers for 2010 pricing if this recovery in Chinese steel production is sustainable.