Finally, after all the diet programs I tried failed, I knew I had to find a different way. As if I needed more motivation, I suddenly remembered something: over the course of my career at investment banks and hedge funds, three of my colleagues (two younger and one older, none of them fat) had died of sudden heart attacks while at work.
Did you know that top tier investment banks have defibrillators on every trading floor? That was a brutal shock, my subconscious mind letting me know I had better do something.
So I developed an analytical approach that worked. It worked because I was patient and dedicated time to research the topic and set up an objective function with constraints that met my specific needs. I took a top-down approach to the problem, an approach that was compatible with my Quant mentality. I did my own analysis and quantification of the components, figured out which ones worked and which ones didn’t, and applied the model to myself. But before trying to lose those 60-plus pounds of fat I was carrying around, I had to keep track of things and accumulate data in order to do some sort of objective analysis. I needed a baseline to measure my progress.
I used a macro view to see where I was in relationship to others, how my condition could affect my life, and find out if my case was normal or abnormal. I asked these questions:
- Q: What’s the average life expectancy in the US versus other countries? A: Higher than most countries
- Q: What single factor (aside from genetics) has a major contribution to increased life expectancy in general? A: higher GDP per capita
- Q: What’s the main factor currently decreasing my potential life expectancy? A: Obesity
- Q: Why am I getting obese? A: Sedentary lifestyle, bad eating habits, ignorance, stress, natural aging, black swan events
- Q: Is my obesity normal or abnormal? A: Both. Normal if you consider there is a tendency in the US for the population to become obese over time (there is a motion chart about this in my first post). So in a way I was simply ‘going with the flow’. Abnormal if you consider that, as human beings, we should take actions that extend our lives, not shorten them. Clearly, obesity is not a healthy condition.
Back to the quant way of analyzing things. A picture’s worth a thousand words, and The Quantitative Method’s information-dense graphs are worth a million. That’s the quant’s way: figure out elements that provide high-quality information in an efficient manner, emphasizing solutions to specific problems instead of detailed modelling. Questions 1 through 4 are answered in the charts below. Question 5 was answered in a previous post.
Note: The chart below is very data intensive and might take a while to load in your computer, depending on factors such as speed of your internet connection, time of day, traffic in Google’s cloud, etc. If you don’t see a chart, please hit reload in your web browser and wait a few seconds. Thank you.
200 Years of History in 20 Seconds – Life Expectancy vs GDP per Capita
The info clearly shows that the life expectancy of the world’s population is improving due to higher per capita income. (Notice that the US seems to be ‘slowing down’, while China and India seem to be ‘speeding up’. I’ll write more about that in an upcoming post.) Look at the dynamics of the countries during the industrial revolution. The US is clearly leading the pack from early 1900 to the late 1990s, with a big advantage right after World War II. Interesting, isn’t it?
That was a little diversion from the main topic. Below is a chart more relevant to the weight problem I was facing three years ago, as well as two interesting cycles (the dotcom bubble and what I call “The Shadow Banking Belle Époque”) that I’ll discuss in future posts: Since 1993 when I got my first Quant job, the S&P had been moving up, and so was my visceral fat and risk of heart attack.