Periodic Table of Commodity Returns (2019 Edition)
Commodities are an interesting asset class to watch.
In certain years, all commodities will move in price together in an obvious and correlated fashion. This is a representation of the cyclical characteristics of commodity markets, in which macroeconomic factors align to create a tide that lifts or sinks all boats.
At the same time, however, each individual commodity is incredibly unique with its own specific set of supply and demand circumstances. In the years when these supply or demand crunches materialize, a certain commodity can surge or crash in price, separating itself from the rest of the pack.
A Decade of Commodity Returns
Today’s visualization comes to us from our friends at U.S. Global Investors, and it tracks commodity returns over the last decade.
More specifically, it takes a closer look at individual commodities (i.e. corn, gold, oil, zinc) to show how performance can vary over time. With a quick examination of the graphic, you can see years where commodities moved together – and some years where individual commodities stole the show unexpectedly.
Palladium: A Perennial Winner
The best performing commodity in 2018 was palladium, which found itself up 18.6% – just enough to edge out corn, which jumped up 17.9% in price last year.
Interestingly, palladium has also been the best performing commodity over the 10-year period as well:
Palladium has finished in first place in four of the last 10 years, including in 2017 and 2018 – it’s also impressive to note that palladium has only had negative returns twice in the last decade (2011, 2015).
A Crude Awakening
The worst performing commodity in 2018 was crude oil, which fell -24.8% in price.
Like palladium, this wasn’t a unique occurrence: crude has actually been the worst performing commodity investment over the last decade:
As you can see, crude oil has been the worst (or second worst) commodity in three of the last five years.
Further, as our chart on how all assets performed in 2018 shows, crude oil was outperformed by every other asset class, and the energy sector had the poorest performance out of all S&P 500 sectors last year.
20 Common Metal Alloys and What They’re Made Of
You can’t find stainless steel, brass, sterling silver, or white gold on the periodic table. Learn about 20 common metal alloys, and what they are made from.
Every day, you’re likely to encounter metals that cannot be found anywhere on the periodic table.
You may play a brass instrument while wearing a white gold necklace – or maybe you cook with a cast iron skillet and store your leftovers in a stainless steel refrigerator.
It’s likely that you know these common metal alloys by name, and you can probably even imagine what they look and feel like. But do you know what base metals these alloys are made of, exactly?
Common Metal Alloys
Today’s infographic comes to us from Alan’s Factory Outlet, and it breaks down metal and non-metal components that go into popular metal alloys.
In total, 20 alloys are highlighted, and they range from household names (i.e. bronze, sterling silver) to lesser-known metals that are crucial for industrial purposes (i.e. solder, gunmetal, magnox).
Humans make metal alloys for various reasons.
Some alloys have long-standing historical significance. For example, electrum is a naturally-occurring alloy of gold and silver (with trace amounts of copper) that was used to make the very first metal coins in ancient history.
However, most of the common metal alloys on the above list are actually human inventions that are used to achieve practical purposes. Some were innovated by brilliant metallurgists, while others were discovered by fluke, but they’ve all had an ongoing impact on our species over time.
Alloys with an Impact
The Bronze Age (3,000 BC – 1,200 BC) is an important historical period that is rightfully named after one game-changing development: the ability to use bronze. This alloy, made from copper and tin, was extremely useful to our ancestors because it is much stronger and harder than its component metals.
Steel is another great example of an alloy that has changed the world. It is one of the most important and widely-used metals today. Without steel, modern civilization (skyscrapers, bridges, etc.) simply wouldn’t be possible.
While nobody knows exactly who invented steel, the alloy has a widely-known cousin that was likely invented in somewhat accidental circumstances.
In 1912, English metallurgist Harry Brearley had been tasked with finding a more erosion-resistant steel for a small arms manufacturer, trying many variations of alloys with none seeming to be suitable. However, in his scrap metal heap – where almost all of the metals he tried were rusting – there was one gun barrel that remained astonishingly untouched.
The metal alloy – now known to the world as stainless steel – was a step forward in creating a corrosion-resistant steel that is now used in many applications ranging from medical uses to heavy industry.
How AI and Big Data Will Unlock the Next Wave of Mineral Discoveries
Mineral exploration produces massive amounts of data. With AI, geologists can produce geological insights from this data to make the next discovery.
How AI and Big Data Will Unlock the Next Mineral Discovery
Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly proving their value across many industries.
Today’s infographic comes from GoldSpot Discoveries, and it shows that when this tech is applied to massive geological data sets, that there is growing potential to unlock the next wave of mineral discoveries.
Mineral Exploration: Fortunes Go to the Few
Discovering new sources of minerals, such as copper, gold, or even cobalt, can be notoriously difficult but also very rewarding. According to Goldspot, the chance of finding a new deposit is around 0.5%, with odds improving to 5% if exploration takes place near a known resource.
On the whole, mineral exploration has not been a winning prospect if you compare the total dollar spend and the actual value of the resulting discoveries.
Measuring Discovery Performance by Region (2005 to 2014)
|Region||Exploration Spend||Estimated Value of Discoveries||Value/Spend ratio|
|Australia||$13 billion||$13 billion||0.97|
|Canada||$25 billion||$19 billion||0.77|
|USA||$10 billion||$5 billion||0.48|
|Latin America||$33 billion||$19 billion||0.57|
|Pacific/SE Asia||$8 billion||$4 billion||0.49|
|Africa||$20 billion||$23 billion||1.19|
|Western Europe||$4 billion||$2 billion||0.42|
|Rest of World||$27 billion||$8 billion||0.32|
|Total||$140 billion||$93 billion||0.57|
Figures in 2014 dollars. (Source: MinEx Consulting, March 2015)
Aside from the geographic insights, on the surface this data reveals that mineral exploration does not pay for itself. That said, there are still significant discoveries worth billions of dollars – it’s just the returns go inordinately to a few small players that make big finds.
Much of the money spent on exploration may not have produced the next great discovery, but you can be sure it created massive volumes of data that could be used for further refining of exploration models.
So, What is the Problem?
Every exploration failure or success produces geological insights. The mineral exploration process is the source of massive amounts of data in the form of soil samples, chip samples, geochemistry, drill results, and assay results. Each drill hole is a tiny snapshot into the processes that form the earth.
A single drill hole can create 200 megabytes of data and when there are many drill holes coupled with other types of information, an exploration project can produce terabytes of data. If you wanted to compare your one project to hundreds of others to find the best insights, the amount of data becomes dizzying.
All these data points are clues that can be used to find new mineral deposits, but to sort through them is too much for even an entire team of capable geologists.
Luckily, using today’s technology, this data can now be used to train computers to spot the areas showing similar patterns to past discoveries.
The true power of AI will be in its ability to empower technically trained professionals to make decisions in an increasingly complex and data-driven world.
Professor Ajay Agrawal, a noted academic in AI and founder of the University of Toronto’s Creative Destruction Lab, categorizes human activities into five categories:
- Data collection
- Information retrieval
He concludes that machines should do the first three and that humans – such as geologists, doctors, lawyers, investment bankers and others – should make the judgment calls and take the actions based on predictive capabilities of AI.
The mineral exploration industry presents a good example of how AI and big data can help technical professionals make discoveries faster, with less money, using a wide variety of data inputs created.
Opportunity Generator and the AI-friendly Future
AI can take the large amounts of data from many different projects in order to spot the right opportunities to further explore, building on decades of geological data from projects around the world.
The right technology can help reduce the risk inherent in exploration and lead to more mineral discoveries on budget, rewarding those that deployed their data most effectively. Companies that are able to harness this power will tip the scales in their favor.
As a result, mineral exploration is no longer so much an art of interpretation – but instead, it becomes closer to a pure science, giving geologists a whole-field perspective of all the data.
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