Tag Archive for: markets

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Lost In Place

Danger Will Robinson!
Lost In Space TV series

Hypernomics wants to know: Where are we?

Concerning geolocation, it’s an easy question today. Google can map your global spot within a couple of feet. Thanks to modern navigation techniques, going through the western end of the English Channel is trivial now. But it wasn’t always so.

On October 22, 1707, Sir Cloudesley Shovell led a British fleet to that opening, hove-to, and tried to get his bearings. It was about 4:00 PM. Clouds obscured the sky as the seas rolled in a heavy storm, making latitude readings from an astrolabe inaccurate. Most of his compasses didn’t work. The chronometers eventually used to help fix longitude were years away. Not recognizing his true position was far from his calculated one, he ordered the fleet to sail at about 6:00 PM. Two hours later, the Scilly disaster took his life and 1400 to 1800 others. It led to a series of Longitude Acts, which developed sea-worthy timepieces to avoid such fiascos in the future.

When your life depends on it, you need to know where you are.

When your product is at stake, you need to find what promises clear sailing. Hypernomics helps you find your proper heading and bearings.

#hypernomics #innovation #position #markets #location

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Bounding Problems

In countless games, the parameters, once set, never vary. There are only a given number of spaces on the chessboard. American football always uses an elliptical spheroid built to strict specifications. All NBA basketball rims are the same diameter, ten feet in the air.

Markets seem different. They don’t have instruction manuals. At first glance, it would seem you could do anything you want in them.

Still, they have rules. Not understanding them can sink a project.

In addition to DeLorean not understanding the value of horsepower (see the last post), they also failed to appreciate the Demand Frontier they faced in 1981. As A shows us, DeLorean thought they could exceed that limit by nearly two standard deviations, despite no one else beating it by half that much.

In this and many other markets, Product Market Demand Curves form. Always flatter than the overall Demand Frontier they build, they describe the product price limits as quantities sold increase. These curves set boundaries producers must consider before they enter any market. Ignoring them can have disastrous consequences.

How do Product Market Demand Curves compare to their Learning Curves? Look to the next post for answers.

#markets #demand #pricing #boundary #innovation #business

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Demand By Proxy

You can’t always get what you want…

Finding data can be hard. Say you needed to model demand for Japanese bullet train travel, or the London to Paris run. Ideally, both firms would post these figures, and you could reduce that to insight. While you can locate that information for NYC cab data (see one of my previous posts), you won’t for The Shinkansen or the Chunnel. What to do?

Let’s suppose train operators match the number of seats offered by class to their demand. Why wouldn’t they? If they had too many high-priced seats open, they’d either drop the price or change the seating arrangement. Eventually, they would come to a configuration that works most of the time.

Below, we see the number of seats by price for Japanese (A) and European (B) high-speed rail services. By themselves, neither has enough data to form a viable study. Together, in (C), they reveal collective thinking from different sides of the planet – and it’s much the same. Yes, that’s only two routes. More would be better. But this shows us we can gain an understanding of a market using the files we have instead of the ones we want.

…If you try sometimes, you just might find…you get what you need (Mick Jagger/Keith Richards).

#innovation #demand #markets #marketanalysis #strategy

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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

Laffer Curve Quantified: Pot Taxes Get Too High

The Laffer Curve is the relationship between tax rates and revenues.  For income, taxes of 0% or 100% produce no tax revenue.  Maximum tax receipts lie in-between.

The study of this phenomenon has mainly been theoretical.

The recent rush of states legalizing recreational marijuana gives us a real-world example.

In 2014, Colorado and Washington legalized recreational pot.  Other states followed suit, all with different tax rates.  If we exclude the results for Oregon and California in 2019 (in red), the remaining six blue points form most of the Laffer curve for cannabis.  This blue power curve is highly negative (exponent -1.55) and significant (P-value 1.96E-03).  It explains why Nevada, in 2019, made over 30 times as much per cannabis user as did Washington State in 2014.

In 2019, California, with nearly 13 times the population of its neighbor Nevada, made barely half of the receipts of The Silver State.  California struggles mightily with the cannabis black market because of its tax policy.  There’s a lesson here: Never turn a market analysis problem into a legal one.  If someone blows smoke your way arguing for high marijuana taxes, don’t inhale.

#Laffercurve #markets #marketanalysis #cannabisnews #cannabistax #taxpolicy

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Interest Rates And Currency

Last time, we examined how the amounts of currency and foreign exchange reserves drove currency prices.  There are more forces at work here.

A country’s prime interest rate is one of them.

Below we examine the linked effect of interest rates and currency value from July 12, 2019.  The rates in the study vary widely and are part of what supports the price of money.

At left, we see how the world reacts to the Volume of money, the foreign exchange reserves, and the prime rate, here set to 2% (Sweden’s at that time).  If we change that loan figure to 63% (which Brazil had then), we get the picture at right.  Note the Value response plane is lower.

While the statistics for this analysis are significant (P-Values of 3.30E-12 for the equation, 4.96% for Prime, 3.06E-12 for Volume, 0.01% for Foreign Exchange Reserves), the Mean Absolute Percentage Error (MAPE) is high, at 117.5%, meaning there is more work needed to decompose this market.

#currency #prices #markets #price #investing

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What Supports Currency Prices?

Several factors determine the price of any given country’s currency.  A 4D analysis helps you visualize those influences.  Here, we examine what held up those values on July 12, 2019.

As the red Demand Plane shows us, as the amount of currency issued increases, its price generally falls.

We can (and, in this case, must – we can’t get a functional equation without it) use this influence with others to predict sustainable currency prices in USD.  In the left Value Space, the plane running through the data indicates currency value goes up with added Foreign Exchange Reserves and down with Volume.  The P-Value for this equation is 3.30E-12.  The chance it accidentally predicts the data is that low.

The case manifests The Law Of Value And Demand, which states:

  1. Features determine Value
  2. Value affects Price
  3. Price influences Quantity sold and
  4. Quantity sold is a feature.

The equation explaining the plane in Value Space uses the Prime Rate, set to 2%.  What happens if we set the Prime Rate to 63%?  Check the next post for the answer.

#demand #currency #prices #markets #currencytrading

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Production Possibility Curves Are Real

If you search Production Possibility Curves, you’ll get charts trading off product pairs such as wheat and steel, pizza and sugar, or guns and roses.  There are at least 3 problems here.  First, these charts are uniformly hypothetical.  Second, these trades involve disparate markets.  Most firms don’t play across the markets selected.  US Steel doesn’t harvest wheat.  Domino’s doesn’t compete with C&H Sugar.  Smith and Wesson don’t sell in flower auctions.  Third, producers don’t need conjecture but want the specific tradeoffs in their industries.

We can instead derive actionable production possibility curves based on real data.  As shown for the 2018 electric car market in A below, a curved surface describes how the market values horsepower and seat count.  As we set three price targets as horizontal planes, they intersect the curved surface as curved lines, as shown in B.  Those lines overlay open spaces in the market, revealing product feature pairs with economic distance between them and existing models.  In 2018, with horsepower as the first feature and seats as the second, new models with (255, 6) or (331,4) at $60K, or one with (647,6) at $100K find themselves in open market space.

#productionpossibilitycurve #trading #markets #prices

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Economic & Social Distancing

We’re in the middle of a global COVID-19 pandemic.  We’ve heard about social distancing.  It sounds bad. What if we could use such measures to our advantage?

In the mid-1800s, miasma theory dominated disease transmission thinking.  It said, “bad air” caused most disorders.  Dr. John Snow didn’t buy it. As cholera hit home, he decided to see for himself.  He made dot plot A, with one dot on a Soho, London map for every cholera death.  They centered near the Broad Street Pump.  The opposite of distancing, clustering, proved cholera a water-borne disease.

Distancing and clustering both figure into market success.  In B, the 2018 electric car market had many players offering 5 passenger capacity with up to 250 horsepower.  New entrants may want to provide unique combinations to create separation.  We observe open market spaces.  In 2016, Tesla placed multiple Model 3 versions in then-existing like regions.  It became the best-selling US plug-in car.  Economic distancing can help sales.

In 2018, buyers agreed within about features for which they’ll pay. As shown in C, they cluster to the added value they assign to seats and horsepower (P-Values of 0.59% & 9.80E-11).  How else can we use Figure C? See the next post.

#socialdistancing #economicdistancing #markets

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Five-Dimensional Markets

Markets move.

We may show the 2012 car market Value (the upper surface of the red space at left, the points deriving that surface omitted), and the Costs for those cars (an estimate shown by the lower red surface of that space).  The region between those surfaces is the Financial Opportunity Space (FOS), where suppliers make Profits.  That market’s matching Demand Frontier (in red) is at right. Electric car Value comes from Horsepower & Range (Dimensions 1 & 2), which determines Price (Dim 3), which drives Demand (Dim 4).

As this market moved into 2013, more entrants joined.  Existing models sales climbed. Over Time (Dim 5), the 2013 Demand Frontier shifted to the blue line.  Simultaneously, the viable profitability region moved too, from the red 2012 to the blue 2013 FOS.  Values changed (Value Space points left out for clarity), and learning on existing models drove their costs lower (the lower blue space surface).  We know costs fall over time for models due to the learning curves that apply to repetitive activities and producers drop prices at the same time to gain market size – see the post from a month ago on the Model T for a real-world example.

The origin of 5D systems is (0,0,0,0,Tn). Tn is a timestamp.

#markets #prices #profits #profitability