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All-Time Highs, But

On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves
Nate Silver, founder and editor-in-chief of FiveThirtyEight</small

They stood there laughing/They’re not laughing anymore—The Call, The Walls Came Down

Recently, it seems that everybody has been shouting about the financial markets. It seemed the markets set new records daily this week, which is excellent for those invested in these pools. The best firms and exchanges in the world are doing the best they’ve ever done. A skeptic might ask, is that all there is?

Well, I’m a skeptic.

About four years ago, using the principles I had discovered while writing my book, I took my money and used our software to create the Hypernomics Fund. There may be thousands of viable fund variations, but ours, based only on S&P 500 stocks and always going long, works better than many of the leading names in the business.

A few years from now, everyone will use Hypernomics principles or be behind those that do since it lets you see more of the markets’ inner workings than the alternatives.

You wouldn’t study the stars without a telescope. Don’t research your markets without Hypernomics.

#hypernomics #stockmarkets #innovation

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The Little Fund That Could – And Did And Does

Yeah. Beethoven was deaf.  Helen Keller was blind.  I think Rocky’s got a good chance.
Adrian, ‘Rocky’

Funny thing about entering a game late.  People think you can’t play just because you haven’t been on the field.

Make no mistake.  We’re a late entrant.  Many might think of us as a world-weary veteran reliever, coming in the bottom of the ninth to get out the last batter.

We see ourselves more as an untested first-round draft pick sitting on the bench.  And we’ve been studying the game – and we think we’ve figured a few things out.  Our fund reflects that.

We founded Hypernomics, Inc. (yes, it’s official, we were formerly MEE Inc.) to offer training, software, and consulting for the field we discovered, which, of course, is Hypernomics.  We still do that, and that’s what’s kept the lights on.

More and more, though, our advisors and we are seeing the potential of this fund.  We’re not open to the public, but we think one or more firms could benefit from licensing our analytics.  Just as we didn’t know about Hypernomics until we discovered it, we don’t know what to do or where to go exactly.

If you have some thoughts, please share them.

#hypernomics, #stockmarkets, #innovation, #stockanalysis

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Hypernomics Fund

“Price is what you pay, value is what you get” – Warren Buffett

In the last post, as we studied the Aerion AS2, we found out how
Hypernomics could help avoid losses.

Someone asked me how Hypernomics could be used to make money. A stock fund is one good example.

Value analysis is a critical component of this new discipline. As Mr. Buffett is quick to point out, we need to distinguish a product’s price versus its value. In today’s red-hot real estate market, an underpriced house would be sniffed out by anxious buyers quicker than a drunken gazelle tripping into a bar full of lions. Comparable sales of several homes sold down the street take care of that.

Stocks are more complex. With hundreds of financial metrics for each company and thousands of competitors, it is easy to get lost in all the data. Hypernomics sorts through that information to find and buy undervalued stocks.

Below are metrics comparing how we’ve done since we began trading with our algorithms nearly a year and a half ago, all growth-indexed from the same starting point beginning 2/20/2020.

This fund is not open to the public.

#hypernomics #stock #stockmarket #innovation #value #valueanalysis

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Measuring Demand

Two useful measures of Demand are the Demand Frontier and Aggregate Market Demand.

The Demand Frontier describes a market’s outer boundary.  For the S&P 500, the dark green dots show the outermost quantities (stock volumes) and prices (split-adjusted stock prices).  The Demand Frontier is the green line of best fit through them.  It shows the market’s price limits and its reaction to price changes.

Another way to portray buyers’ price sensitivity is with Aggregate Market Demand.  Here, an algorithm splits the stocks into price bins, distributed 1) equally concerning price, or unequally distributed to price following 2) a Fibonacci or 3) Geometric series for the number of observations per bin.  In this case, a 6 bin split (divided by red lines) provides 5 red points (bin 5 is empty).  Each red point is the total stock quantity in each bin and the average weighted price of those stocks.  The red line through them is Aggregate Market Demand.

Demand Frontier and Aggregate Market Demand slopes converge with many observations.  Here, the slope of the Demand Frontier is -0.244; the Aggregate Market Demand is -0.236. Good agreement between the slopes provides good evidence about market workings.

How does Value relate to Demand?  Read the next post.

#demandforecasting

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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