Fractal Markets, Fractional Integration, and Long Memory in Financial Time Series – I

The concepts – ‘fractal market hypothesis,’ ‘fractional integration of time series,’ and ‘long memory and persistence in time series’ – are related in terms of their proponents and history.

I’m going to put up ideas, videos, observations, and analysis relating to these concepts over the next several posts, since, more and more, I think they lead to really fundamental things, which, possibly, have not yet been fully explicated.

And there are all sorts of clear connections with practical business and financial forecasting – for example, if macroeconomic or financial time series have “long memory,” why isn’t this characteristic being exploited in applied forecasting contexts?

And, since it is Friday, here are a couple of relevant videos to start the ball rolling.

Benoit Mandelbrot, maverick mathematician and discoverer of ‘fractals,’ stands at the crossroads in the 1970’s, contributing or suggesting many of the concepts still being intensively researched.

In economics, business, and finance, the self-similarity at all scales idea is trimmed in various ways, since none of the relevant time series are infinitely divisible.

A lot of energy has gone into following Mandelbrot suggestions on the estimation of Hurst exponents for stock market returns.

This YouTube by a Parallax Financial in Redmond, WA gives you a good flavor of how Hurst exponents are being used in technical analysis. Later, I will put up materials on the econometrics involved.

Blog posts are a really good way to get into this material, by the way. There is a kind of formalism – such as all the stuff in time series about backward shift operators and conventional Box-Jenkins – which is necessary to get into the discussion. And the analytics are by no means standardized yet.

An Update on Bitcoin

Fairly hum-drum days of articles on testing for unit roots in time series led to discovery of an extraordinary new forecasting approach – using the future to predict the present.

Since virtually the only empirical application of the new technique is predicting bubbles in Bitcoin values, I include some of the recent news about Bitcoins at the end of the post.

Noncausal Autoregressive Models

I think you have to describe the forecasting approach recently considered by Lanne and Saikkonen, as well as Hencic, Gouriéroux and others, as “exciting,” even “sexy” in a Saturday Night Live sort of way.

Here is a brief description from a 2015 article in the Econometrics of Risk called Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates


I’ve always been a little behind the curve on lag operators, but basically Ψ(L-1) is a function of the standard lagged operators, while Φ(L) is a second function of offsets to future time periods.

To give an example, consider,

yt = k1yt-1+s1yt+1 + et

where subscripts t indicate time period.

In other words, the current value of the variable y is related to its immediately past value, and also to its future value, with an error term e being included.

This is what I mean by the future being used to predict the present.

Ordinarily in forecasting, one would consider such models rather fruitless. After all, you are trying to forecast y for period t+1, so how can you include this variable in the drivers for the forecasting setup?

But the surprising thing is that it is possible to estimate a relationship like this on historic data, and then take the estimated parameters and develop simulations which lead to predictions at the event horizon, of, say, the next period’s value of y.

This is explained in the paragraph following the one cited above –


In other words, because et in equation (1) can have infinite variance, it is definitely not normally distributed, or distributed according to a Gaussian probability distribution.

This is fascinating, since many financial time series are associated with nonGaussian error generating processes – distributions with fat tails that often are platykurtotic.

I recommend the Hencic and Gouriéroux article as a good read, as well as interesting analytics.

The authors proposed that a stationary time series is overlaid by explosive speculative periods, and that something can be abstracted in common from the structure of these speculative excesses.

Mt. Gox, of course, mentioned in this article, was raided in 2013 by Japanese authorities, after losses of more than $465 million from Bitcoin holders.

Now, two years later, the financial industry is showing increasing interest in the underlying Bitcoin technology and Bitcoin prices are on the rise once again.


Anyway, the bottom line is that I really, really like a forecast methodology based on recognition that data come from nonGaussian processes, and am intrigued by the fact that the ability to forecast with noncausal AR models depends on the error process being nonGaussian.

Coming Attractions

Well, I have been doing a deep dive into financial modeling, but I want to get back to blogging more often. It gets in your blood, and really helps explore complex ideas.

So- one coming attraction here is going to be deeper discussion of the fractal market hypothesis.

Ladislav Kristoufek writes in a fascinating analysis (Fractal Markets Hypothesis and the Global Financial Crisis:Scaling, Investment Horizons and Liquidity) that,

“ is known that capital markets comprise of various investors with very different investment horizons { from algorithmically-based market makers with the investment horizon of fractions of a second, through noise traders with the horizon of several minutes, technical traders with the horizons of days and weeks, and fundamental analysts with the monthly horizons to pension funds with the horizons of several years. For each of these groups, the information has different value and is treated variously. Moreover, each group has its own trading rules and strategies, while for one group the information can mean severe losses, for the other, it can be taken a profitable opportunity.”

The mathematician and discoverer of fractals Mandelbrot and investor Peters started the ball rolling, but the idea maybe seemed like a fad of the 1980’s and 1990s.

But, more and more,  new work in this area (as well as my personal research) points to the fact that the fractal market hypothesis is vitally important.

Forget chaos theory, but do notice the power laws.

The latest  fractal market research is rich in mathematics – especially wavelets, which figure in forecasting, but which I have not spent much time discussing here.

There is some beautiful stuff produced in connection with wavelet analysis.

For example, here is a construction from a wavelet analysis of the NASDAQ from another paper by Kristoufek


The idea is that around 2008, for example, investing horizons collapsed, with long term traders exiting and trading becoming more and more short term. This is associated with problems of liquidity – a concept in the fractal market hypothesis, but almost completely absent from many versions of the so-called “efficient market hypothesis.”

Now, maybe like some physicists, I am open to the discovery of deep keys to phenomena which open doors of interpretation across broad areas of life.

Another coming attraction will be further discussion of forward information on turning points in markets and the business cycle generally.

The current economic expansion is growing long in tooth, pushing towards the upper historically observed lengths of business expansions in the United States.

The basic facts are there for anyone to notice, and almost sound like a litany of complaints about how the last crisis in 2008-2009 was mishandled. But China is decelerating, and the emerging economies do not seem positioned to make up the global growth gap, as in 2008-2009. Interest rates still bounce along the zero bound. With signs of deteriorating markets and employment conditions, the Fed may never find the right time to raise short term rates – or if they plunge ahead will garner virulent outcry. Financial institutions are even larger and more concentrated now than before 2008, so “too big to fail” can be a future theme again.

What is the best panel of financial and macroeconomic data to watch the developments in the business cycle now?

So those are a couple of topics to be discussed in posts here in the future.

And, of course, politics, including geopolitics will probably intervene at various points.

Initially, I started this blog to explore issues I encountered in real-time business forecasting.

But I have wide-ranging interests – being more of a fox than a hedgehog in terms of Nate Silver’s intellectual classification.

I’m a hybrid in terms of my skill set. I’m seriously interested in mathematics and things mathematical. I maybe have a knack for picking through long mathematical arguments to grab the key points. I had a moment of apparent prodigy late in my undergrad college career, when I took graduate math courses and got straight A’s and even A+ scores on final exams and the like.

Mathematics is time consuming, and I’ve broadened my interests into economics and global developments, working around 2002-2005 partly in China.

As a trivia note,  my parents were immigrants to the US from Great Britain , where their families were in some respects connected to the British Empire that more or less vanished after World War II and, in my father’s case, to the Bank of England. But I grew up in what is known as “the West” (Colorado, not California, interestingly), where I became a sort of British cowboy and subsequently, hopefully, have continued to mature in terms of attitudes and understanding.

China Update – The Beatings Will Continue Until Morale Improves

It’s essentially illegal to short a stock in China today.

This developed in response to the more than 40  percent drop in market value  of most stocks– as in the chart for Shenzhen Index B shown below.


B shares (Chinese: B股, officially Domestically Listed Foreign Investment Shares) on the Shanghai and Shenzhen stock exchanges refers to those that are traded in foreign currencies. Shares that are traded on the two mainland Chinese stock exchanges in Renminbi, the currency in mainland China, are called A shares.

China Seen Delaying Shenzhen Stock Link Until 2016 Amid Turmoil

Chinese authorities have gone to extreme lengths to prop up the stock market as both the Shanghai Composite Index and the Shenzhen Composite Index lost more than 40 percent from their June highs. They’ve banned major shareholders from selling stakes, armed a state-run margin trader with billions of dollars to buy equities, allowed hundreds of companies to halt their shares and placed curbs on bearish bets in the futures market.

One casualty has been the World’s Biggest Stock-Index Futures Market

Volumes in the country’s CSI 300 Index and CSI 500 Index futures sank to record lows on Wednesday after falling 99 percent from their June highs. Ranked by the World Federation of Exchanges as the most active market for index futures as recently as July, liquidity in China has dried up as authorities raised margin requirements, tightened position limits and started a police probe into bearish wagers.

China’s Response to Stock Plunge Rattles Traders

A business journalist has been detained and shown apologizing on national television for writing an article that could have hurt the market.


Apparently, the beatings will continue until morale improves.

The spectacle of Wang Xiaolu, reporter at a Chinese business publication, enacting Monday what authorities called a confession of spreading “panic and disorder” in the stock market through his reporting should scare investors.

The announcement Sunday by China’s Ministry of Public Security that 197 people had been punished for spreading rumors about stocks and other issues should scare investors.

That the head of hedge fund manager Man Group Plc’s China unit has been taken into custody, as reported by Bloomberg on Monday, should scare global investors.

This is not to say that China isn’t capable of browbeating and arm-twisting and public money spending its way to a higher stock market. China asked brokerages to increase their support of a market rescue fund by 100 billion yuan last week, according to reports, a move perhaps tied to a desire for a rally, or at least stability, ahead of Thursday’s parade commemorating victory over Japan…….

They do know that a huge country is scared so badly of something that they will not, cannot, allow things to be said that cause stocks to fall.


These strong-arm tactics go beyond anything seen in the West, in terms of damage control. Transparency,  an key feature in financial analysis, has been thrown out the window. With Goons going around bullying people to hold stocks that have lost 40 percent of their value, pressing journalists to keep quiet about problems in the market, it is a strange world in Chinese finance and stocks. People “disappear” – no record of arrest being announced.

There is no question but that Chinese macroeconomic statistics must also be viewed in a more skeptical light.

This makes assessment of the Chinese slowdown more difficult.

One Month Forecast of SPY ETF Price Level – Extreme Value Prediction Algorithm (EVPA)

Here is a chart showing backtests and a prediction for the midpoints of the monthly trading ranges of the SPY exchange traded fund.


The orange line traces out the sequence of actual monthly midpoints of the price range – the average of the high and low price for the month.

The blue line charts the backtests going back to 2013 and a forecast for September 2015 – which is the right-most endpoint of the blue line. The predicted September midpoint is $190.43.

The predictions come from the EVPA, an algorithm backtested to 1994 for the SPY. Since 1994, the forecast accuracy, measured by the MAPE or mean absolute percent error, is 2.4 percent. This significantly improves on a No Change model, one of the alternative forecast benchmarks for this series, and the OOS R2 of the forecast of the midpoint of the monthly trading range is a solid 0.33.

Just from eyeballing the graph above, it seems like there are systematic errors to the downside in the forecasts.

However, the reverse appears to happen, when the SPY prices are falling over a longer period of time.


I would suggest, therefore, that the prediction of about $190 for September is probably going to be higher than the actual number.

Now – disclaimer. This discussion is provided for scientific and entertainment purposes only. We assume no responsibility for any trading actions taken, based on this information. The stock market, particularly now, is volatile. There are lots of balls in the air – China, potential Fed actions on interest rates, more companies missing profit expectations, and so forth – so literally anything may happen.

The EVPA is purely probabilistic. It is right more often than wrong. Its metrics are better than those of alternative forecast models in the cases I have studied. But, withal, it still is a gamble to act on its predictions.

But the performance of the EVPA is remarkable, and I believe further improvements are within reach.

Economic Impact Modeling

I had a chance, recently, to watch computer simulations and interact with a regional economic impact model called REMI. This is a multi-equation model of some vintage (dating back the 1980’s) that has continued to evolve. It’s probably currently the leader in the field and has seen recent application to assessing proposals for increasing the minimum wage – in California, Vermont, San Francisco – and to evaluating  a carbon tax for the Citizen’s Climate Initiative  (see the video presentation at the end of this post).

One way to interact with REMI is to click on blocks in a computer screen based on the following schematic


I watched Brian Lewandowski do this at Colorado University’s Leeds School of Business.

Brian set parameters for increases in labor productivity for professional services and changes in investment in primary and secondary educational by clicking on boxes or blocks. Brian, Richard Wobbekind (pictured below), and I discussed results, and how REMI is helpful in exploring “what-if’s” and might have applications to  optimizing tax policies at the state level..


Wobbekind is himself a leader in preparing and presenting State-level forecasts for Colorado, and is active in the International Institute of Forecasters (IIF) which sponsors the International Journal of Forecasting and Foresight – as well as being an Associate Dean of CU’s Leeds School of Business.

Key Point About Multi-Equation, Multivariate Economic Models

From the standpoint of forecasting, the best way I can understand where REMI should be placed in the tool-kit is to remember the distinction between conditional and unconditional forecasts.

REMI model documentation indicates that,

The REMI model consists of thousands of simultaneous equations with a structure that is relatively straightforward. The exact number of equations used varies depending on the extent of industry, demographic, demand, and other detail in the specific model being used. The overall structure of the model can be summarized in five major blocks: (1) Output, (2) Labor and Capital Demand, (3) Population and Labor Supply, (4) Wages, Prices, and Costs, and (5) Market Shares

So you might have equations such as,

X1t = a0 + a1Z1t +..+ akZkt

X2t = b0 + b1Z1t +..+ brZrt

In order to predict unconditionally what (X1t,X2t) will be at some specific future time T*, it is necessary to correctly derive the parameters (a0,a1,..,ak,b0,b1,,…,br).

And it also is necessary, for an unconditional forecast, to predict the future values of all the exogenous variables on the right-hand side of the equation – that is all the Z variables that are not in fact X variables.

This usually means that unconditional forecasts from multivariate forecast models have wide and rapidly diverging confidence intervals.

Thus, if you try to forecast future employment in, say, California with such models, they may underperform simpler, single equation models – such as those based on exponential smoothing, for example.

This does not invalidate general systems models such as REMI.

Assuming the flows and linkages of sectors and blocks are realistic and correctly modeled, such models can help think through the consequences of policy decisions, new legislation, and infrastructure investments.

This is essentially to say that these models may present good conditional forecasts – basically “what-if’s” without being the best forecasting tool available.

Here is a video presentation based on the Citizen’s Climate Initiative application of REMI to assessing a carbon tax – an interesting proposal.

Today’s Stock Market

Shakespeare’s Hamlet says at one point, “There are more things Horatio, than are dreamt of in your philosophy.” This is also a worthy thought when it comes to stock market forecasts.

So here is a chart showing recent daily predictions of the midpoint (MP) of the daily price ranges for the SPY exchange traded fund (ETF), compared with the actual dollar value of the midpoint of the trading range for each day.


The predictions are generated by the EVPA – extreme value prediction algorithm. This is a grown-up version of the idea that ratios, like the ratio between the opening price and the previous high or low, can give us a leg up in predicting the high or low prices (or midpoint) for a trading day.

Note the EVPA is quite accurate up to recent days, when forecast errors amplify at the end of the week of August 21. In fact, in backtests to 1994, EVPA forecasts the daily MP with a mean absolute percent error (MAPE) less that 0.4%.

Here is a chart of the forecast errors corresponding to the predicted and actuals in the first chart above.


Note the apparently missing bars for some dates are just those forecasts which show such tiny errors they don’t even make it to the display, which is dominated by the forecast error August 24 at about 4 percent.

Note also, the EVPA adjusts quickly, so that by Monday August 28th – when the most dramatic drop in the midpoint (closing price, etc.) occurs – the forecasts (or backcasts) show much lower forecast error (-0.1%).

Where This Is All Going

The EVPA keys off the opening price, which this morning is listed as 198.11 – lower than Friday’s closing price of 199.24.

This should tell you that the predictions for the day are not particularly rosy, and, indeed, the EVPA forecast for the week’s MP of the trading range is almost flat, compared to the midpoint of the trading range for last week. My forecast of the MP for this week is up 0.13%.

What that means, I am not quite sure.

Here is a chart showing the performance of the weekly EVPA forecasts of the SPY MP for recent weeks.


This chart does not include the forecast for the current week.

What I find really interesting is that, according to this model, the slide in the SPY might have been spotted as early as the second week in August.

The EVPA is an amazing piece of data analytics, but it exists in an environment of enormous complexity. Studies showing that various international stock markets are cointegrated, and the sense of this clearly applies to Chinese and US stock markets. Also, there is talk of pulling the punchbowl of “free money” or zero interest rates, and that seems to have a dampening effect on the trading outlook.

Quite frankly, in recent weeks I have been so absorbed in R programming, testing various approaches, and, as noted in my previous post, weddings, that I have neglected these simple series. No longer. I plan to make these two prediction series automatic with my systems.

We will see.

Recent Events in the US Stock Market

The recent drop in US stocks is dramatic, as the steep falloff of the SPY exchange traded fund (ETF) Monday, August 24th– almost the most recent action in the chart – shows.


At the same time, this is by no means the steepest drop in closing prices, as the following chart of daily returns highlights.


TV commentators and others point to China and the prospective liftoff of US short term interest rates, with the Federal Reserve finally raising rates off the zero bound in – it was thought – September.

I have been impressed at the accuracy of Michael Pettis’ predictions in his China Financial Markets. Pettis has warned about a debt bubble in China for two years and consistently makes other correct calls. I have some first-hand experience doing business in China, and plan a longer post of the collapse of Chinese stock markets and the economic slowdown there.

You can imagine, if you will, a sort of global input-output table with a corresponding table of import/export flows. China has gotten a lot bigger since 2008-2009, absorbing significant amounts of the global output of iron and steel, oil, and other commodities.

Also, in 2008-2009 and in the earlier recession of 2001, China led the way to greater spending, buoying the global economy which, otherwise, was in sad shape. That’s not going to happen this time, if a real recession takes hold.

All very scary, but while the latest stuff took place, this is what I was doing.


In other words, I was the father of the groom at a splendid wedding for my younger son at the Pearl Buck estate just outside Philadelphia.

Well, that wonderful thing being done, I plan to return to more frequent posting on BusinessForecastblog.

I also apologize for having the tools to predict the current downturn, at least after developments later last week, and not signaling readers.

But frankly, I’m not sure the extreme value prediction algorithms (EVPA) reliably predict major turning points. In fact, there seem to be outside influences at key junctures. However, once a correction is underway, predictability returns. Thus, the algorithms do more than simply forecast the growth in stock prices. The EVPA also works to predict the extent of downturns.

Here’s a tip. Start watching ratios such as those between  differences between the  opening price in a trading day and the previous day’s high or low price, divided by the previous day’s high or low price, respectively. Very significant predictors of the change in daily highs and lows, and with significance for changes in closing prices, if you bring some data analytics to bear.

Links – early August, 2015

Well, I’m back, after deep dives into R programming and statistical modeling. I’d like to offer these links which I’ve bookmarked in recent days. The first four cover a scatter of topics, from impacts of the so-called sharing economy and climate developments to the currency impacts of the more and more certain moves by the US Federal Reserve to increase interest rates in September.

But then I’ve collected a number of useful links on robotics and artificial intelligence.

How the ‘sharing economy’ is upending the travel industry

DS: New York Attorney General Eric Schneiderman last October issued a report finding 72 percent of the reservations on Airbnb going back to 2010 were in violation of city law. What’s the industry doing to address these concerns?

MB: Listen, I think there are a lot of outdated regulations and a lot of outdated laws that were written in a time where you couldn’t possibly imagine the innovation that has come up from the sharing economy, and a lot of those need to be updated to meet the world that we live in today, and I think that’s important.  Sometimes you have regulations that are put in place by incumbent industries that didn’t want competition and you have some regulations that were put in place back in the ’60s and ’70s, where you couldn’t imagine any of these things, and so I think sometimes you need to see updates.

So there you go – laws on the books are outdated.

Brain-controlled prosthesis nearly as good as one-finger typing

The goal of all this research is to get thought-controlled prosthetics to people with ALS. Today these people may use an eye-tracking system to direct cursors or a “head mouse” that tracks the movement of the head. Both are fatiguing to use. Neither provides the natural and intuitive control of readings taken directly from the brain.

The U.S. Food and Drug Administration recently gave Shenoy’s team the green light to conduct a pilot clinical trial of their thought-controlled cursor on people with spinal cord injuries.

Jimmy Carter: The U.S. Is an “Oligarchy With Unlimited Political Bribery”

Unfortunately, very apt characterization from a formal standpoint of political science.


What to Expect from El Niño: North America

The only El Niño events in NOAA’s 1950-2015 database comparable in strength to the one now developing occurred in 1982-83 and 1997-98… Like other strong El Niño events, this one will almost certainly last just one winter. But at least for the coming wet season, it holds encouraging odds of well-above average precipitation for California. During a strong El Niño, the subtropical jet stream is energized across the southern U.S., while the polar jet stream tends to stay north of its usual winter position or else consolidate with the subtropical jet. This gives warm, wet Pacific systems a better chance to push northeast into California… Milder and drier a good bet for Pacific Northwest, Northern Plains, western Canada.. Rockies snowfall: The south usually wins out…Thanks to the jet-shifting effects noted above, snowfall tends to be below average in the Northern Rockies and above average in the Southern Rockies during strong El Niños. The north-south split extends to Colorado, where northern resorts such as Steamboat Springs typically lose out to areas like the San Juan and Sangre de Cristo ranges across the southern part of the state. Along the populous Front Range from Denver to Fort Collins, El Niño hikes the odds of a big snowstorm, especially in the spring and autumn. About half of Boulder’s 12” – 14” storms occur during El Niño, and the odds of a 20” or greater storm are quadrupled during El Niño as opposed to La Niña.

According to NOAA, the single most reliable El Niño outcome in the United States, occurring in more than 80% of El Niño events over the last century, is the tendency for wet wintertime conditions along and near the Gulf Coast, thanks to the juiced-up subtropical jet stream.

Emerging market currencies crash on Fed fears and China slump

The currencies of Brazil, Mexico, South Africa and Turkey have all crashed to multi-year lows as investors flee emerging markets and commodity prices crumble.

Robotics and Artificial Intelligence

Some of the most valuable research I’ve found so far on the job and societal impacts of robotics comes from a survey of experts conducted by the Pew Research Internet Project AI, Robotics, and the Future of Jobs,

Some 1,896 experts responded to the following question:

The economic impact of robotic advances and AI—Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?

Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.

The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution.

Read this – the comments on both sides of this important question are trenchant, important.

The next most useful research comes from a 2011 publication of Brian Arthur in the McKinsey Quarterly The second economy – which is the part of the economy where machines transact just with other machines.

Something deep is going on with information technology, something that goes well beyond the use of computers, social media, and commerce on the Internet. Business processes that once took place among human beings are now being executed electronically. They are taking place in an unseen domain that is strictly digital. On the surface, this shift doesn’t seem particularly consequential—it’s almost something we take for granted. But I believe it is causing a revolution no less important and dramatic than that of the railroads. It is quietly creating a second economy, a digital one.

Twenty years ago, if you went into an airport you would walk up to a counter and present paper tickets to a human being. That person would register you on a computer, notify the flight you’d arrived, and check your luggage in. All this was done by humans. Today, you walk into an airport and look for a machine. You put in a frequent-flier card or credit card, and it takes just three or four seconds to get back a boarding pass, receipt, and luggage tag. What interests me is what happens in those three or four seconds. The moment the card goes in, you are starting a huge conversation conducted entirely among machines. Once your name is recognized, computers are checking your flight status with the airlines, your past travel history, your name with the TSA (and possibly also with the National Security Agency). They are checking your seat choice, your frequent-flier status, and your access to lounges. This unseen, underground conversation is happening among multiple servers talking to other servers, talking to satellites that are talking to computers (possibly in London, where you’re going), and checking with passport control, with foreign immigration, with ongoing connecting flights. And to make sure the aircraft’s weight distribution is fine, the machines are also starting to adjust the passenger count and seating according to whether the fuselage is loaded more heavily at the front or back.

These large and fairly complicated conversations that you’ve triggered occur entirely among things remotely talking to other things: servers, switches, routers, and other Internet and telecommunications devices, updating and shuttling information back and forth. All of this occurs in the few seconds it takes to get your boarding pass back. And even after that happens, if you could see these conversations as flashing lights, they’d still be flashing all over the country for some time, perhaps talking to the flight controllers—starting to say that the flight’s getting ready for departure and to prepare for that…

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent—a great computer expression—which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing…

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent—a great computer expression—which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing

I’m interested in how to measure the value of services produced in this “second economy.”

Finally, China’s adoption of robotics seems to signal something – as in this piece about a totally automatic factor for cell phone parts –

China sets up first unmanned factory; all processes are operated by robots

At the workshop of Changying Precision Technology Company in Dongguan, known as the “world factory”, which manufactures cell phone modules, 60 robot arms at 10 production lines polish the modules day .. The technical staff just sits at the computer and monitors through a central control system… In the plant, all the processes are operated by computer- controlled robots, computer numerical control machining equipment, unmanned transport trucks and automated warehouse equipment.

Our Next President is a Wrestling Giant – Trump

Greetings, and I thought you would all enjoy this bit of rough-and-tumble involving the leading Republican candidate so far for US President – Donald Trump.

Make sure you watch past the 42 second mark to see Trump lambast his billionaire buddy. 

So this is really happening. Trump apparently has hired people to work on his campaign for President, and he has taken an early lead over Scott Walker and Jeb Bush, and the other more minor candidates.

Sales and new product forecasting in data-limited (real world) contexts