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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

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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Many Features Can Determine Value

Last time we looked at how 3 features revealed value in stocks.  But, stockholders do not limit themselves to some fixed number of features in their collection buying decisions.

Here, the market considers at least 4 features simultaneously.  It entertains 1) book value per share on one horizontal axis, 2) market capitalization on the other, and then, if it sets 3) EPS to 20 and 4) stock volume to 10 million, it produces the surface at left.  If it resets the stock volume to 1 million, it shifts that surface upward.  [This analysis excludes Amazon.]
Stocks support higher prices with higher earnings and book values per share.  But prices climb as market capitalization goes up and volume falls.  Accounting for opposing forces is crucial to market analysis.

This S&P 500 study only examines stock parameters.  As the world reacts to COVID-19, we see the impact of global market factors.  Stocks fall with uncertainty.

In markets such as the S&P 500, with a sufficient number of entrants, several measures of demand come into view.  The slopes of 2 important demand curves start to converge with enough market participants.  See the next post for a discussion of their differences and similarities.

#stockmarket #equities #markets #trading #stocks

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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