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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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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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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

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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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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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Plotting in 4 Dimensions

You likely have seen readouts from GPS applications, where a device reveals its latitude and longitude.  That is a 2D plot.  Some GPS outputs also include an altitude reading.  That’s a 3D plot.

In the green Value Space at left, below, the Range (analogous to longitude), MV (momentum, akin to latitude), and 2016 Price (similar to altitude) reveal 3D positions of 1 bomb (the BLU-111) and 2 missiles (the AGMs -158-1 and -84). The BLU-111 has a Range, MV, and 2016 Price of (28, 364,389, $32,000), while the ordered triples for AGMs -158-1 and -84 are (1000, 1,225,2000, $1.912M) and (270, 577,125, $528K), in that order.

The red Demand Plane, at right, describes positions as ordered pairs, with Quantities as the horizontal component, and 2016 Prices on the vertical axis, as (33,330, $32,000), (275, $1.912M), and (4,152, $528K) for BLU-11, AGM-158-1, AGM-84, respectively.  The Demand Plane is a 2D plot.

As the 3D Value Space and 2D Demand Plane share the Price axis, the collectively form a 4D system, as displayed in the diagram and described in the table.

How do we display 5D systems?  Read the next post for an explanation.

#marketanalysis #3Dsystems #4Dsystems

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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