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
Walk This Way – It Could Be More Lucrative
How do businesses’ pay to give workers a short stroll to amenities? Using open-source data, we find companies buy easy access to nearby banks, stores, and cafes as they pay for office space and zip codes.
In A, we see LA commercial real estate prices rise with square footage and nearby household income (P-values 3.82E-16 and 0.01%, respectively), as shown by the surface. Included in the calculation of that log-linear plane is “Walk Score,” which “measures the walkability of any address (www.walkscore.com, no affiliation with me).”
B shows the Walk Scores of 60 properties versus their prices. Walk Score is a statistically significant (P-value 0.69%) contributor to Value (as sustainable prices). The overall equation uses square footage, household income, and Walk Score. It has an adjusted R^2 of 71.8%, implying there’s more work to do.
Figures C and D reveal that in Feb 2020 LA, doubling the Walk Score more than proportionally lifted the sustainable price. Firms wishing to put up a new facility need to know this. If the added Value of a new building exceeds its added cost, it may be worthwhile to set it up in high walkability areas.
Is NYC like LA? Look at the next post.
#price #marketanalysis #marketintelligence #realestate #target
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:
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
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
The Demand For Money: Crypto vs. Fiat Currencies
Earlier, we examined Demand for fiat currencies and found they had an Upper and Outer Demand Frontier. Those types of monies have existed for millennia. A new form of exchange began to take off over a decade ago.
Cryptocurrencies began to become popular with the advent of Bitcoin. How does the Demand for cryptocurrencies behave relative to the one for fiat currencies? As it happens, when it comes to Demand, both payment forms have something important in common.
Below, using a fiat currency study from July and one on crypto 20 days later, note the slopes of their Demand Frontiers are nearly identical. At left in yellow, the crypto Demand Frontier slope is -1.47 (P-Value 1.28E-04), while that for fiat currencies is -1.42 (P-Value 7.88E-05). At that time, at the Demand Frontier, cryptocurrencies had reached about 1/1000th of the fiat currency extent. Observe with the steep cryptocurrency Demand Frontier, at its limit, there is more money at the upper end of this curve. Bitcoin’s market capitalization was nearly twice that of the rest of its market combined.
We need to see how each currency form reacts to the coronavirus. Look for my next post on that.
#currency #demandforecasting #cryptocurrencies #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
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