Hypernomics Fund

“Price is what you pay, value is what you get” – Warren Buffett

In the last post, as we studied the Aerion AS2, we found out how
Hypernomics could help avoid losses.

Someone asked me how Hypernomics could be used to make money. A stock fund is one good example.

Value analysis is a critical component of this new discipline. As Mr. Buffett is quick to point out, we need to distinguish a product’s price versus its value. In today’s red-hot real estate market, an underpriced house would be sniffed out by anxious buyers quicker than a drunken gazelle tripping into a bar full of lions. Comparable sales of several homes sold down the street take care of that.

Stocks are more complex. With hundreds of financial metrics for each company and thousands of competitors, it is easy to get lost in all the data. Hypernomics sorts through that information to find and buy undervalued stocks.

Below are metrics comparing how we’ve done since we began trading with our algorithms nearly a year and a half ago, all growth-indexed from the same starting point beginning 2/20/2020.

This fund is not open to the public.

#hypernomics #stock #stockmarket #innovation #value #valueanalysis

The Utility Case Vs. Value Analysis

Every time you spend money, you’re casting a vote for the kind of world you want – Anne Lappé

Recently, long-dead Jeremy Bentham took a cross-campus trip. Shunning a wooden cupboard he long occupied in the Wilkins Building at University College London, he moved, greatly aided, to a shiny new booth in UCL’s Student Centre (A). Famously, his penultimate journey was to a UCL council meeting, where they recorded him as “present, but not voting.”

Bentham’s ideas about utility theory still hold sway. Many firms used it to figure cell phone prices (B). Such studies queried participants’ willingness to pay for features, to which researchers assigned utils, a utility measure. Then analysts converted utils to dollars.

Hypernomics notes respondents gave hypothetical answers; they weren’t buying phones, that util value varies widely, and that this method ignored available relevant communication data at their peril.

We display our willingness to pay to connect when we put up cash to make towers (C), walkie-talkies (D), or car phones (E). Value Analysis considers past and present market states to predict the future.

Jeremy may not vote, but we do.

The best utility case holds its inventor.

#value #utility #hypernomics #innovation #marketanalysis

DeLorean CSI

In October 1982, the US government charged John DeLorean with cocaine trafficking in a deal he thought would stave off bankruptcy for his self-named company.

How did it all go wrong?

His DeLorean Motor Company sports car (A) came with several sales features. It had a rear-mounted engine, brushed stainless steel body panels, and its iconic gull-wing doors. Its original designation was the DMC-12, the “12,” reflecting its price, in thousands. But, when it came time to start taking orders, DeLorean dropped the name and raised the price.

The renamed DeLorean entered the market with 130 horsepower, priced at $25,000. As we see in B, no car with that amount of power came close to its price. The 1981 Audi 5000 Turbo, with the same horsepower, sold for $7,000 less.
Statistics reveal the sustainable prices for 1981 cars were a function of their horsepower and units sold (both P-values < 0.01).

As shown in C, the DeLorean’s predicted sustainable price was $15,500; its posted price was nearly three standard deviations too high.

To sell all 7,500 units it produced for $25K, D shows us its installed horsepower should have doubled to 262. As 1981 ended, it only sold 3,000.

Moral of the story: do market math.

#innovation #marketanalysis #valueanalysis #pricing #cars

Visualizing Value

In Multidimensional Economics, Value is a sustainable product price based on its features.  Producers set Prices.  Customers determine Value.  When they don’t match, problems arise.  Buyers pay no mind to cost when considering Value.  If you paid $1000 for a laptop, you don’t care if its cost was $1900, $900, or $90.  You just know it satisfied your Value proposition.  How do markets establish Value?

Value is whatever the market says it is.  For business jets, Fig. A shows us there is a positive correlation between speed and price.  The faster the planes go, the more buyers who can are willing to pay.  Note, though; there is high variation in A near 560 MPH, reflected in the Mean Absolute Percentage Error in D.  Fliers like to be able to take people along with them; thus, it makes sense in B that buyers pay for added capacity.  No one wants to be cramped, either, so observe in C that taller cabins fetch more money than shorter ones.  As we add features B & C, we lower errors in D.

Aircraft speed, capacity, and comfort value terms are analogous to those for computers. Laptop buyers want processor speed, short- and long-term memory, and easy to read screens.

Analysts should consider all features markets find useful.

#business #value #marketanalysis #price #innovation

Fantasy Football: More Real Than Imagined

You’d think that NFL salaries would be performance-based. But, those predictions for running backs seldom exceed R2s of 60%.

Happily, fantasy football gets it right.

In A and B, we predict the value for two NFL running backs. If we take a database of 84 of them and filter out those with no catches or touchdowns in 2019, we get 55 players represented by the A-B points. The equation for their total points has an adjusted R2 of 97.7%, based on their 2019 rushing yards, receiving yards, and touchdowns (P-values 1.66e-27, 8.65e-16, and 9.94e-15, in that order).

Leonard Fournette and Todd Gurley had nearly equal scores but took different paths to success. Fournette, in A, had many rushing and receiving yards as he scored 3 TDs. His actual score (183.4 points) exceeds his prediction (164.2). Gurley (B) had fewer yards but scored more often, and his exact number (188.4 points) mimics the prediction (186.4).

Christian McCaffrey and James White contributed much more than this equation suggests. Both had nearly 3 standard deviations worth more points than their predictions. Multiple different variable combinations would offer more insight into player contributions.


#nfl #fantasyfootball #players #player #value #football #nflnews

Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement

NFL Wideout Valuation: Go Faster

In 1968, Rocky Bleier joined the Pittsburg Steelers.  After the season, once drafted, he volunteered for duty in Vietnam.

When he came #price to the team camp in 1972, he posted a 4.6-second 40-yard dash.

With part of his right foot blown off.

His previous best was 4.8.

What’s the value of added speed for veterans?  If we remove the rookie contracts and draft halo effects by looking at pros in the league for six or more years (thanks, Jem Anderson!), we can find out.  A shows us the total compensation for NFL wideouts goes up with receptions per game.  At the same time, their value falls dramatically with age.  Speed plays a role too. A 28-year old receiver with 4 catches a game running a 4.65 40-yard dash is worth about $6 million per year (note the equation forming surfaces A and B has P-values of 6.43E-07, 0.41%, and 3.6% for catches/game, age, and 40-yard times, in that order, and 2.32E-06 for the entire equation).

In B, we take another 28-year old with 4 receptions/game, but this one runs the 40-yard dash a quarter second quicker.  The extra speed adds another 2/3 to his compensation, bringing it to $10 million.

It’s hard to improve speed.  But if you can do it in the NFL, it pays off.

#value #valueproposition #generalmanager #worth

NFL Wideout Valuation

If you’re into the game, you probably have a rough idea about how the National Football League assigns values to its players. A little analysis provides unexpected insights.

Each dot in A and B denotes one of 106 NFL wide receivers in 2019. A’s plane shows the value the league assigns to them. It’s a function of their league years, receptions per game, and draft round (P-values of 7.70E-17, 5.84E-08, 0.02%, respectively, and 6.61E-25 for the entire equation, with an adjusted R^2 of 66.7%). Here, we’ve set the round to 1, years to 4, and receptions per game to 3.04. For those valued features, the NFL awards a wideout with $5 million/year.

B shows us how others can get the same. If we keep league years at 4, we find that if we increase the receptions to 6.09, a 3rd-round wideout (note lower plane) can get as much as a 1st-rounder.

That’s twice the receptions for the same salary.

Knowing this informs decisions. If too-high valuations for 1st-rounders come from long contracts too often, perhaps GMs should seek shorter terms. If a 3rd-rounder receives an extended period offer at a low rate but knows he can perform, maybe he should negotiate for bonuses for his excellent work.

#nfl #nfldraft2020 #players #playervaluation #valuation #value #generalmanager

Features Determine Value

In every market, buyers determine Value, the sustainable prices for products based on their features.  This phenomenon is never more evident than in stock markets.

Consider the S&P 500 from one day in July 2019, as shown below.  After filtering out those stocks with negative figures for book values, earnings per share, and returns on assets, we have 411 stocks left.

At left, the plane running through the data reveals how the market rewards market capitalization (showing larger companies draw larger prices) and book value per share (how the market rewards a measure of safety if the company were to dissolve), given earnings per share (EPS) of $2.  We could imagine stockholders consider EPS as part of their Value calculation as well, and if we increase it from $2 to $20, as shown at right, we see how the market rewards that feature.

Book value per share, market cap, and earnings per share (with P-values of 0.58%, 1.88E-67, and, 1.17E-11, respectively, where P-values measure the chance a variable contribution is due to chance) are parts of an equation with more contributors to Value as a part of it.

What else might add Value?  Check in to the next post for some answers.

#prices#stocks#value#sustainable#market

What Holds Up Prices?

Price formation often seems steeped in mystery.  “Seeing what the market will bear” is a mantra for many, but why would we want to leave prices to chance if we could avoid it?

What supported the prices for 2013 electric cars?  As shown below, we could make a statistically significant (9.3E-09) estimate of prices using a surface running through the 18 electric car models (as green spheres) that made up the market that year.  That surface reflects that after buyers paid about $6,500 to enter the market, the Price went up $102 for every horsepower and $172 for every added mile of range (P-Values, 0.00038, 4.19E-07, respectively).  Models priced above the surface may be overpriced, those below may be under-priced, or some other significant Features may be at work.

The diagram & the market math behind it demonstrates the first 2 adages of the Law of Value and Demand, which are:

  1. Product Features (as horsepower, range) determine Value
  2. Value determines Price

The green region is Value Space.  How does it relate to Demand?  Read the next post for an answer.

#prices#value