Physical and Market Targets

Aiming at and missing is much the same for battles and marketplaces.  We account for inaccuracies in the same way.

In A, a pilot sits in a Caudron G.3, an Allied surveillance plane in WWI. Note the fuselage aft of his seat.  Scenario B recounts a different G.3 pilot on an observation sortie, climbing out during a massive artillery battle.  An unseen friendly battery aims at a bridge, 1.  Their shell comes up short (2), wide (3), and high, as it strikes the plane (4).  As we move from 1 to 4, those lines trace out a Ballistic Error Tetrahedron, a miss across three dimensions (latitude, longitude, and altitude).  Figure C reveals the mission’s surviving American Expeditionary Force officer, my grandfather.  The round shot off part of the plane behind him.

We see the same construct in D. Here, a manufacturer sets a price for a business jet, 1.  However, if the airframer loses passenger capacity, the plane’s value falls to 2.  If it drops more features, as maximum speed and range, its sustainable price shifts to 3, then 4, respectively; this, too, is a 3-dimensional error, one we call a Value Error Tetrahedron.  Combined with the last post’s analysis, it reveals a 4D miscue.

#price #target #marketanalysis #marketintelligence

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

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

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

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

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

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

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

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