Discovery, Invention, and Stocks

There is a difference between discovery and invention. A discovery brings to light what existed before, but what was not known; an invention is the contrivance of something that did not exist before.
Sir William Ramsay

There was a hidden discipline lying about, unseen from view. I unearthed it. When I did, I discovered HypernomicsTM. Its foundation, the Law of Value and Demand, states that

  1. Features determine Value
  2. Value drives Price
  3. Price limits Quantity Sold
  4. Quantity Sold is a Feature.

Useful by itself, it needed an invention to get results quickly.

That came as HypernomicaTM (formerly MEE4DTM) software, built by Shad Torgerson, Kent Joris, and me. It speeds up the analysis of complex markets.

Just over 44 months ago, we set it on the most complicated market we could find — that of stocks. Using only stocks from the S&P 500, our HypernomicsTM Fund (private, not open to the public) managed to beat it by 2.35X. The likelihood of that happening by chance is very much less than one in a trillion. At the same time, our fund outperformed Berkshire Hathaway A by a factor of 1.39X.

HypernomicaTM is available now; soon, we will begin classes on it. Be among the first to benefit from this discovery and its companion invention.

There’s a preferred way to compare the means of two groups and verify if their differences came about randomly.That is the Student’s t-test. William Sealy Gosset conceived it in 1908 (published under the pen name “Student”). That test (the two-tailed version) reveals that the likelihood that HypernomicsTM beat the S&P 500 by 2.35X over 44+ months was due to chance was 2.01 E-237.That test for HypernomicsTM against Berkshire Hathaway A, where we beat it by 1.39X in the same period, calculates the probability that the result was due to chance as 1.57E-152. In short, our algorithm, built using the HypernomicaTM Software, works. It will work for you, too, whatever your market might be.

Ants, Airbus, And Avoidance

Hit ’em where they ain’t.
Wee Willie Keeler

I finished a run the other day and stretched at the trail’s end. I looked down, and an ant caught my eye. Starting from position A1, it reached A2 and seemed to be going in a circle for a moment. But then, as its path widened to A3, A4, and A5, I realized it was doing reconnaissance! I raced home and found out that ants do that to “avoid hostile conspecific neighbours,” when considering where to set up camp, they use “a weighted additive strategy, the most comprehensive of consumer evaluations, to choose nests with the best combination of attributes.”*

People, of course, do the same thing.

In B, during 2009-2018, the business jet market had a lot of competitors offering various cabin and price combinations. Noticeably, though, there were a few prominent gaps in the market, akin to how the ants saw regions away from their neighbors. In Q1 of 2019, Airbus launched its A220-100 business jet in the most prominent open region (C). Along with the base airliner from which it came, this vehicle has several hundred orders, helping ensure its long-term viability.

When considering where to place your next product, work to avoid local opposition.

How to Lose €10B+

The race is not always to the swift, nor the battle to the strong, but that’s the way to bet.
Damon Runyon

According to Harvard professor Clayton Christensen, nearly 30,000 new products are introduced yearly, and 95% fail.

But there are ways that one can help increase the potential for product success. It involves determining the Value, Demand, and Cost of goods and services before they launch. Buyers reveal how they Value the product features and their Demand. It is up to producers to figure out those parameters, along with their Costs. Not looking at all those variables in advance is a recipe for financial disaster.

You’re bound to fail if you placed a heavy bet on a program with long odds against you– but you went ahead with it, got it into production, and rode it out until it ran out of steam after losing tens of billions of Euros. That is the conclusion I reached for the Airbus A380 in my paper, “CSI EU: Cost Scene Investigation,” for which I won the ICEEA 2023 Best Modeling and Case Studies Track Paper. This step-by-step analysis gives you the framework for creating models that enhance your chances of being that one in 20 product that succeeds. Below is the video of the presentation:

Assumptions vs. Observations: The A380

Assumptions are what we don’t know we are making.
Douglas Adams

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line.  That’s lots of big jets. But, their 20-year goal was 1250.  How did it go so wrong?

Assumptions are what we don’t know we are making – Douglas Adams

Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line.  That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?

Many pundits claim they knew it wouldn’t make its target.  Most appeared when the program floundered late in its lifespan.  What would it take to predict its future in advance?

Projects often use 1) business case analyses and 2) customer polls to “verify it pencils out.”  That works if 1) analysts conceive those cases fairly and 2) buyers convert at or above a target sales figure.

What if we don’t have to rely on those techniques?

To forecast the next 20 years, study the last 20.  As B reveals (summing all model types to base versions), the airliner market had a poorly correlated (Adj R^2 0.458) yet statistically significant (P-Value 0.035) Demand Frontier over that period.  Airbus’s target was nearly ten standard deviations past it.

The A380 took €25B to develop. It didn’t recoup its investment. Take time to model markets in advance. See what a market did to bound what it will do. You may not like the answers, but it beats losing billions.

#A380 #demandfrontier #hypernomics