Tag Archive for: demand

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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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Cannabis Laffer Curve Expanded:
The Netherlands Sparked North American Interest

In an earlier post, we examined the recreational pot tax structure.  Using US-only data, we discovered that at its frontier, a Laffer Curve formed that described the maximum amount of tax revenues possible given specific tax rates.

Here we entertain other authorities taxing legal recreational pot.  Added to the blue points forming a limit is another describing the tax rate and revenue per user for The Netherlands (NL) in 2008 (adjusted for inflation).  Through these blue points, the Laffer Curve explains 90% of their variation and is highly negative (power exponent -1.61).

Also considered now but not part of the Laffer Curve is the recent experience of British Columbia (BL 2019).  Observe it registered minuscule tax revenues.

At least 3 factors influence cannabis tax receipts: 1) Ease of legal access: BC, OR, and CA lag far behind their better-organized counterparts in making legal recreational marijuana sufficiently available.  2) Tax rate: From 15.3% (NV 2019) to 108% (WA 2014), revenues go up as tax percentages go down.  3) The proximity of lower-cost options: some would-be CA or CA tourist receipts or go to NV or black markets.

#laffercurve #market #marketanalysis #price #taxpolicy #demand #tax

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Missing Price Targets

Businesses frequently set price goals. What does it mean not to hit them?

In A, we predict 1, the price of a barrel of oil. When the forecast date arises, the actual price, 2, varies, resulting in a one-dimension Error Line. We know by how much we missed, but nothing else.

B shows us what it means to be off-target in archery. If we aim at 1 and land on 2, we create an Error Triangle. That’s a two-dimensional error, in elevation (up and down) and azimuth (left to right).

In C, where every blue diamond stands for a business jet, we propose a new one. We set our target quantity and price, as 1, on the Demand Frontier, the line through the market’s outermost quantity-price points, in yellow. If we can’t put in enough features to support that price, we’d make a plane fetching less money, as 2. Moving from 1 to 2, we make a Demand Error Triangle.

But, in D, we find as prices fall, we might be able to sell more models. Analysts should find the Demand Frontier Slope to ascertain the amount of revenue available at market limits. That will be the areas under the curves, the green rectangle for 1, the orange one for 2.

What does it mean to miss price targets in Value Space? Find out in my next post.

#prices #demand #demandplanning #demandforecasting

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Real Demand Curves In Action

The rock star Meatloaf tells us that “Two out of three ain’t bad.”

But, when it comes to, say, selling your new supersonic jet, it can be.

One can adequately estimate a product’s Cost and Value (as a sustainable price for the first business jet to go over 1,000 miles per hour) only run afoul of its market’s Demand Frontier.

Below, we find Aerion offers a credible development Cost target for its supersonic AS2 (see A), and offline there is evidence its $120 million Price works for the market. Their problem lies with Demand. They forecast a market of 500 models, with 300 in a decade. But in the ten years studied in B, their forecast exceeded the limit of the Upper Demand Frontier (P-Value 4.91E-04). Five years later, in C, the Demand Frontier (P-Value 5.39E-05) shifted only slightly. As of January 2020, the company still only has the 20 orders they received in 2015. Currently, its chances of selling 300 units at $120 million in a decade are less than 25%.

COVID-19 or other forces may increase business jet demand, moving the Demand Frontier where Aerion would like it to be.

Failing that, the company likely got Cost and Price right, but missed Demand.

In business, two out of three is bad.

#demand #demandforecasting #marketanalysis #prices

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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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Cryptocurrency Demand Shift

We’ve all heard about a shift in demand.  Not all of us see it in action.  With a dynamic market, we can.  The one for cryptocurrencies fits the bill.

Last August, the top 100 cryptocurrencies had quantities and prices indicated by the white circles in the figure.  Those with a red dot in the center of them formed their red Demand Frontier as of August 1, 2019 (with a P-Value, the chance this equation came about by chance, of 1.28E-04).

Then things changed.

On Friday, March 20, 2020, 94 cryptocurrencies (we lost some), with blue squares for their quantities and prices, reflecting a downward and inward shift in demand.  Each of the Demand Frontier points shifted down (the corralled ordered pairs), except for Tether (which grew slightly) and Ripple (which went down and in).  The result was a shift in the cryptocurrency Demand Frontier to the one in blue, which is steeper (the slope was -1.47, is -1.57) and more highly correlated (R2 was 92.6%, is 96.8%, with P-Value falling to 9.92E-06).  Though the log scaling tends to disguise it, the market lost over 40% of its market capitalization.

What holds up currency prices?  We’ll look at that next time.

#cryptocurrencies #bitcoin #currency #crypto #cryptocurrency #demand

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Measuring Demand

Two useful measures of Demand are the Demand Frontier and Aggregate Market Demand.

The Demand Frontier describes a market’s outer boundary.  For the S&P 500, the dark green dots show the outermost quantities (stock volumes) and prices (split-adjusted stock prices).  The Demand Frontier is the green line of best fit through them.  It shows the market’s price limits and its reaction to price changes.

Another way to portray buyers’ price sensitivity is with Aggregate Market Demand.  Here, an algorithm splits the stocks into price bins, distributed 1) equally concerning price, or unequally distributed to price following 2) a Fibonacci or 3) Geometric series for the number of observations per bin.  In this case, a 6 bin split (divided by red lines) provides 5 red points (bin 5 is empty).  Each red point is the total stock quantity in each bin and the average weighted price of those stocks.  The red line through them is Aggregate Market Demand.

Demand Frontier and Aggregate Market Demand slopes converge with many observations.  Here, the slope of the Demand Frontier is -0.244; the Aggregate Market Demand is -0.236. Good agreement between the slopes provides good evidence about market workings.

How does Value relate to Demand?  Read the next post.

#demandforecasting

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Value, Demand, and 4D States

Last time we tackled Value as sustainable Prices based on product Features, shown in Value Space.  There, 2 Valued Features, horizontal dimensions 1 & 2, drive Value, which determines Price, vertical dimension 3.

We earlier depicted Demand with a horizontal Quantity dimension 4 and the same Price dimension 3.

Last week we showed how the Antarctic claims of Argentina and Australia meet at the South Pole, their air spaces abutting the Earth’s axis.  If we call the South Pole “0,” every point away from it is positive.

As Value Spaces and Demand Planes share a common Price Axis, they abut one another as do the Argentinian and Australian claims.

It follows Value and Demand form 4D systems, such as that for electric cars below.  Every point in Value Space has a matching one on the Demand Plane.  Look at the green lines running to the isolated point in Value Space, connecting to its opposing Demand Plane point.

The diagram shows the Law of Value and Demand:

  1. Product Features determine Value
  2. Value determines Price
  3. Price determines Quantity sold
  4. Quantity sold is a feature

Value and Demand form linked, dual states.

How do we handle more valued features?  Please see the next post for the answer.

#prices#demand#4Dsystems#marketanalysis

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The Demand For Money

Well, that’s an odd title, I’ll grant you that.

Really, what we’re addressing here is the demand for fiat currency.

Recall in previous posts we found Demand Frontiers for multiple markets. Sometimes these curves have breaks. Such is the case for fiat currencies. As shown in the diagram, this market has an Upper Demand Frontier and an Outer Demand Frontier.

Upper Demand Frontiers emphasize the price-limiting boundary for a market, while Outer Demand Frontiers focus on the quantity-limiting ability of a market to absorb the product. These boundaries help countries’ central banks to figure out how many currency units to issue.

What maintains the price of any currency? Please look at the next post for the first of two answers.

#demand #prices #currency #demandforecasting

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Why Find Demand Frontiers?

In the last few posts, we’ve been examining Demand Frontiers. You might ask, “What is the point of doing that?”

Well, let’s look.

Recall in the last post, we found the Demand Frontier moved little in 20 years.  In 2016, the Demand Frontier had an equation describing the line running from the upper left to the lower right in the diagram below.  Because the programs forming this line clustered closely about it, the standard deviation of the line is relatively low: $25.5 million.

The United States Air Force proposes to build 100 B-21 bombers at a “projected average procurement unit cost of $550 million per plane in FY2010 (https://lnkd.in/g4Rkx2R or $610M per plane in 2016 dollars.  What are their chances of making that number of planes at that price, given the standard deviation of the Demand Frontier?

As shown below, the B-21 Target is nine standard deviations over the predicted limiting price ($380M) for the B-21.  Examined by another metric, the historical maximum percentage deviation over the Demand Frontier was 17.8%.  The B-21 program proposes to exceed it by 60.5%.

What was the procurement history of other programs that tried to exceed the Demand Frontier? We will find out next time.

#demand #demandforecasting