This piece is inspired by a friend’s short essay that answers a question he often received.
“Didn’t you study chemical engineering in college?”
For context, he’s a young tech professional working as a Marketing Data Analyst in one of South East Asia’s biggest start-ups.
Perhaps unsurprisingly, I have also received the same question a lot; especially from older individuals that are not fully up to pace with the relational change between one’s education and professional career in the current generation. Not to blame them — in the past, what you study and what you go work for was much more straightforward. There were less interdisciplinary fields (such as FinTech, Gaming, and New Media) that do not have clear written-down paths on what you should study in college in order to be a fit for said industries.
With that said, I’m going to try and map out the reasoning, benefit, and similarities between my degree, Industrial Engineering (IE), and the industry that I’m dabbling in; Investment.
Breadth of Systems
Industrial Engineering is a field of study that focuses on the optimization of complex systems. Traditionally, people thought that Industrial Engineers specifically studied machinery or factory systems, having the typical idea that IE professionals only work on manufacturing-related facilities. As our world grew and so did the field of study itself, the word “system” can now be affiliated with multiple industries. The followings are examples of systems in our modern world:
- The process that happens at Ford’s automobile manufacturing sites.
- The flow of an airport, including its capacity and scheduling.
- The relationship between multiple participants in our healthcare industry.
- The logic behind machine learning algorithms.
- The global economy and financial market.
Take a look at the following charts that IE students typically study in college. Now imagine adjusting the contents with relevant topics in your respective industry, whether it be education, technical sales, healthcare, and many more.
For example, the questions in the flowchart can be replaced by the following when evaluating an investment decision:
- What is its historical performance?
- Is it currently underperforming its sector?
- How’s the supply-demand growth of the asset?
- Does it have a positive cash flow (for stocks)?
- Does it have a positive market sentiment?
- Is it prone to disruption or geopolitical events?
- Is it going to benefit from central banks’ actions?
- What’s the time horizon of the trade/investment?
- How levered should we be?
- What’s the R:R of the trade?
Systems in Investing
The investment industry is an extremely complex system with diverse participants who are continuously trying to get a better understanding of the world. This is because regardless of your investment niche or thesis, knowing what’s happening in the world and calculating any potential implications will always help. However, unlike in engineering related systems, this also means that the investment space deals with problems that in its nature, have a much higher degree of uncertainty.
In most cases, once an engineering problem is solved, it can be improved and optimized with high certainty. By having a better understanding of Tesla’s manufacturing systems, Elon Musk and his engineering team will eventually be able to increase their Model 3 output. This is why successful technology and engineering companies tend to grow their output over time. The stopping point is usually not determined by their technical capabilities, but by business and other economic decisions.
In investing, nothing is ever certain, otherwise, everybody will be as rich as Warren Buffet.
Jim Simons, the founder of one of the best hedge funds in history, Renaissance Technologies, famously said that you only need to be right 51 percent of the time in order to make money in the market. From a glance, it seems to be fairly simple — but unlike engineering calculations with hundreds of years of math and physics history, every single investment models, frameworks, and analysis have a higher probability of being wrong. There are assumptions and external factors that will directly impact the accuracy of your analysis in real-time.
Imagine if every single process that goes into the making of iPhones in Apple’s manufacturing sites has a direct impact on Samsung Galaxy’s output. Imagine that by altering one step in your facility, it might impact the decision and outcome of other company facilities in real-time.
Additionally, what made the industry so notoriously ruthless and difficult is the fact that almost every single trade decision is a zero-sum game. There’ll always be a losing and a winning side of every bet (it’s not always, but I’m not going to the details here). This means that the industry requires a high level of creativity and original thinking because every single publicly available systems or strategies to make money has one general flaw: it is no longer proprietary. To generate alpha, one needs to either be faster or better at understanding these systems and the risks associated with them.
All of the above created a high-intensity, fast-paced environment that requires its participants to always be on their feet. It might not be a fit for some people, but it has never ceased to intrigue my curiosity.