Now that Q3 earnings season it is time to return to the fundamentals. In light of the current turmoil in the middle east, today we will talk about globalization. One of the most interesting things about global financial markets is that they are all connected in one way or another. I like to compartmentalize globalization into two general categories supply chains and political influences. In today’s episode, we will focus on supply chains.
When providing context into supply chains, I do not think there is a better example than Apple’s iPhone. Of course, the iPhone was designed back in Cupertino, CA, but the supply chain runs into multiple different countries. The complexity is mind-boggling, there are over 175 individual components. Additionally, the design and assembly of these components also happen in distinct parts of the world, in the US and China. The complexity is all done in an attempt to reduce costs by outsources cheap parts and labor but it also comes with associated risks. If one link in the chain breaks then the probability of success is essentially broken. A perfect example of this is the automotive industry. Following the reopening of countries around the world due to the COVID-19 induced shut down, assembly lines around the world were closed. As things ramp back up, a delay in a single component, no matter how minimal, can cause significant disruptions.
Another inlet to the supply chain is shipping channels. Back in March 2021, there was an incident with a cargo ship that became barged in the Suez Canal. If you are unfamiliar with the Suez Canal, it significantly reduces travel time from the Indian Ocean to the Atlantic. The bargain is not only delayed the individual ship but every ship behind it. This created delays throughout the global shipping community that results in months and millions in delays. This is example is not limited to boats but can be seen in any transportation medium, such as the hacking of the Colonial Pipeline which created petroleum shortages across the East coast. Supply chain disruptions could hit any industry at any time.
The most significant feature of this trend is that shipping rates have been rising for over 30 years and there is no end to the trend. Even in the COVID-induced recession shipping rates rose. As companies optimize costs through increased globalization, the risk of supply chain disruptions also increases a counterproductive tradeoff.
Personally, I like and own the company $GSL which will benefit from the crosswinds either way. The company owns and operates shipping vessels around the world. As volumes increase their pricing power expands. In the unlikely scenario where volumes begin to decrease they will still maintain market share and become cash flowing machine. Additionally, if the industry consolidates then decreasing shipping rates will incentivize companies to continue to invest abroad. The company had a couple of problematic years due to the increased political tension during Donald Trump’s tariff-friendly administration but has been on the rise ever since. If there is a continued globalization-friendly administration the stock could continue to outperform. In particular general performance to the DJT (Dow Transportation Index), it has been a laggard. Potential for mean-reversion?
When providing context into supply chains, I do not think there is a better example than Apple’s iPhone. Of course, the iPhone was designed back in Cupertino, CA, but the supply chain runs into multiple different countries. The complexity is mind-boggling, there are over 175 individual components. Additionally, the design and assembly of these components also happen in distinct parts of the world, in the US and China. The complexity is all done in an attempt to reduce costs by outsources cheap parts and labor but it also comes with associated risks. If one link in the chain breaks then the probability of success is essentially broken. A perfect example of this is the automotive industry. Following the reopening of countries around the world due to the COVID-19 induced shut down, assembly lines around the world were closed. As things ramp back up, a delay in a single component, no matter how minimal, can cause significant disruptions.
Another inlet to the supply chain is shipping channels. Back in March 2021, there was an incident with a cargo ship that became barged in the Suez Canal. If you are unfamiliar with the Suez Canal, it significantly reduces travel time from the Indian Ocean to the Atlantic. The bargain is not only delayed the individual ship but every ship behind it. This created delays throughout the global shipping community that results in months and millions in delays. This is example is not limited to boats but can be seen in any transportation medium, such as the hacking of the Colonial Pipeline which created petroleum shortages across the East coast. Supply chain disruptions could hit any industry at any time.
The most significant feature of this trend is that shipping rates have been rising for over 30 years and there is no end to the trend. Even in the COVID-induced recession shipping rates rose. As companies optimize costs through increased globalization, the risk of supply chain disruptions also increases a counterproductive tradeoff.
Personally, I like and own the company $GSL which will benefit from the crosswinds either way. The company owns and operates shipping vessels around the world. As volumes increase their pricing power expands. In the unlikely scenario where volumes begin to decrease they will still maintain market share and become cash flowing machine. Additionally, if the industry consolidates then decreasing shipping rates will incentivize companies to continue to invest abroad. The company had a couple of problematic years due to the increased political tension during Donald Trump’s tariff-friendly administration but has been on the rise ever since. If there is a continued globalization-friendly administration the stock could continue to outperform. In particular general performance to the DJT (Dow Transportation Index), it has been a laggard. Potential for mean-reversion?