Tag Archive for: prices

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Announcing The Hypernomics YouTube Channel

It is the obvious which is so difficult to see most of the time.
Isaac Asimov, I, Robot

Here’s a question with a seemingly obvious answer:  How many stocks are part of the S&P 500?  If you guessed 500, you’d be close, as there are 504 companies listed there today.

You likely know that not all S&P companies have issued the same number of shares, nor do all share price match.  Too obvious?  Not really.

Consider what you were undoubtedly told if you ever took an economics class.  According to Paul Samuelson (Economics, 9th Ed., p. 63), “the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.”  Samuelson would have you believe markets have but one equilibrium point.

But we know that is nonsense:  504 stocks in the S&P 500 form 504 quantity and price pairs.  While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.

What’s really going on?  It turns out the value of products goes up as producers add features customers like.  At the same time, as prices go up, quantities sold fall.  To see this phenomenon, one must employ Hypernomics.

To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:

https://www.youtube.com/channel/UCYsso5Yf0OFY3k78u5c30LQ

#hypernomics #marketanalysis #prices #demand

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Hypernomics, Missing Dimensions, & Price Determination: 2nd in a Series

“Everything must be made as simple as possible. But not simpler.” ― Albert Einstein

In the last post, Paul Samuelson said equilibrium prices exist where supply meets demand.

While prices for simple products work that way, Value analyst Sheila (A) suspects markets for more complicated products behave differently.  She knows she can account for mountaintops using latitude, longitude, and altitude referencing the equator, prime meridian, and sea level, respectively (B).

With each of the 44 dots representing a unique flat screen tv’s features and price, she finds she can plot the model’s size (C) or cycles per second (D) against prices and get significant but mediocre R2s.  She works to improve her prediction.

In E, she discovers she can plot Price (Dim 3) against the tv’s refresh rate (Hz, Dim 1) and its size (Diag. “, Dim 2) as ordered triples, using an origin of (0,0,0) as a starting point.  With Hz and Diag. “ as Valued Features 1 and 2, respectively, she predicts flat-screen Value (as sustainable prices) with an R2 of 97.0% and a P-Value of 4.85E-32.  She accounts for other features as needed.

All multi-attribute markets have similar “lost dimensions.”

#markets #innovation #hypernomics #prices #dimensions #wsu

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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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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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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

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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

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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