All posts by Clive Jones

Superforecasting – The Art and Science of Prediction

Philip Tetlock’s recent Superforecasting says, basically, some people do better at forecasting than others and, furthermore, networking higher performing forecasters, providing access to pooled data, can produce impressive results.

This is a change from Tetlock’s first study – Expert Political Judgment – which lasted about twenty years, concluding, famously, ‘the average expert was roughly as accurate as a dart-throwing chimpanzee.”

Tetlock’s recent research comes out of a tournament sponsored by the Intelligence Advanced Research Projects Activity (IARPA). This forecasting competition fits with the mission of IARPA, which is to improve assessments by the “intelligence community,” or IC. The IC is a generic label, according to Tetlock, for “the Central Intelligence Agency, the National Security Agency, the Defense Intelligence Agency, and thirteen other agencies.”

It is relevant that the IC is surmised (exact figures are classified) to have “a budget of more than $50 billion .. [and employ] one hundred thousand people.”

Thus, “Think how shocking it would be to the intelligence professionals who have spent their lives forecasting geopolical events – to be beaten by a few hundred ordinary people and some simple algorithms.”

Of course, Tetlock reports, this actually happened – “Thanks to IARPA, we now know a few hundred ordinary people and some simple math can not only compete with professionals supported by multibillion-dollar apparatus but also beat them.”

IARPA’s motivation, apparently, traces back to the “weapons of mass destruction (WMD)” uproar surrounding the Iraq war –

“After invading in 2003, the United States turned Iraq upside down looking for WMD’s but found nothing. It was one of the worst – arguable the worst – intelligence failure in modern history. The IC was humiliated. There were condemnations in the media, official investigations, and the familiar ritual of intelligence officials sitting in hearings ..”

So the IC needs improved methods, including utilizing “the wisdom of crowds” and practices of Tetlock’s “superforecaster” teams.

Unlike the famous M-competitions, the IARPA tournament collates subjective assessments of geopolitical risk, such as “will there be a fatal confrontation between vessels in the South China Sea” or “Will either the French or Swiss inquiries find elevated levels of polonium in the remains of Yasser Arafat’s body?”

Tetlock’s book is entertaining and thought-provoking, but many in business will page directly to the Appendix – Ten Commandments for Aspiring Superforecasters.

    1. Triage – focus on questions which are in the “Goldilocks” zone where effort pays off the most.
    2. Break seemingly intractable problems into tractable sub-problems. Tetlock really explicates this recommendation with his discussion of “Fermi-izing” questions such as “how many piano tuners there are in Chicago?.” The reference here, of course, is to Enrico Fermi, the nuclear physicist.
    3. Strike the right balance between inside and outside views. The outside view, as I understand it, is essentially “the big picture.” If you are trying to understand the likelihood of a terrorist attack, how many terrorist attacks have occurred in similar locations in the past ten years? Then, the inside view includes facts about this particular time and place that help adjust quantitative risk estimates.
    4. Strike the right balance between under- and overreacting to evidence. The problem with a precept like this is that turning it around makes it definitely false. Nobody would suggest “do not strike the right balance between under- and overreacting to evidence.” I guess keep the weight of evidence in mind.
    5. Look for clashing causal forces at work in each problem. This reminds me of one of my models of predicting real world developments – tracing out “threads” or causal pathways. When several “threads” or chains of events and developments converge, possibility can develop into likelihood. You have to be a “fox” (rather than a hedgehog) to do this effectively – being open to diverse perspectives on what drives people and how things happen.
    6. Strive to distinguish as many degrees of doubt as the problem permits but no more. Another precept that could be cast as a truism, but the reference is to an interesting discussion in the book about how the IC now brings quantitative probability estimates to the table, when developments – such as where Osama bin Laden lives – come under discussion.
    7. Strike the right balance between under- and overconfidence, between prudence and decisiveness. I really don’t see the particular value of this guideline, except to focus on whether you are being overconfident or indecisive. Give it some thought?
    8. Look for the errors behind your mistakes but beware of rearview-mirror hindsight biases. I had an intellectual mentor who served in the Marines and who was fond of saying, “we are always fighting the last war.” In this regard, I’m fond of the saying, “the only certain thing about the future is that there will be surprises.”
    9. Bring out the best in others and let others bring out the best in you. Tetlock’s following sentence is more to the point – “master the fine art of team management.”
  • Master the error-balancing cycle. Good to think about managing this, too.

Puckishly, Tetlocks adds an 11th Commandment – don’t treat commandments as commandments.

Great topic – forecasting subjective geopolitical developments in teams. Superforecasting touches on some fairly subtle points, illustrated with examples. I think it is well worth having on the bookshelf.

There are some corkers, too, like when Tetlock’s highlights the recommendations of 2nd Century physician to Roman emperors Galen, the medical authority for more than 1000 years.

Galen once wrote, apparently,

“All who drink of this treatment recover in a short time, except those whom it does not help, who all die…It is obvious, therefore, that it fails only in incurable cases.”

The Interest Rate Conundrum

It’s time to invoke the parable of the fox and the hedgehog. You know – the hedgehog knows one thing, sees the world through the lens of a single commanding idea, while the fox knows many things, entertains diverse, even conflicting points of view.

This is apropos of my reaction to David Stockman’s The Fed’s Painted Itself Into The Most Dangerous Corner In History—–Why There Will Soon Be A Riot In The Casino.

Stockman, former Director of Office of Management and Budget under President Ronald Reagan who later launched into a volatile career in high finance (See https://en.wikipedia.org/wiki/David_Stockman) currently lends his name to and writes for a spicy website called Contra Corner.

Stockman’s “Why There Will Soon Be a Riot in The Casino” pivots on an Op Ed by Lawrence Summers (Preparing for the next recession) as well as the following somewhat incredible chart, apparently developed from IMF data by Contra Corner researchers.

WEOchart

The storyline is that planetary production fell in current dollar terms in 2015. This isn’t because physical output or hours in service dropped, but because of the precipitous drop in commodity prices and the general pattern of deflation.

All this is apropos of the Fed’s coming decision to raise the federal funds rate from the zero bound (really from about 0.25 percent).

The logic is unassailable. As Summers (former US Treasury Secretary, former President of Harvard, and Professor of Economics at Harvard) writes –

U.S. and international experience suggests that once a recovery is mature, the odds that it will end within two years are about half and that it will end in less than three years are over two-thirds. Because normal growth is now below 2 percent rather than near 3 percent, as has been the case historically, the risk may even be greater now. While the risk of recession may seem remote given recent growth, it bears emphasizing that since World War II, no postwar recession has been predicted a year in advance by the Fed, the White House or the consensus forecast.

But

Historical experience suggests that when recession comes it is necessary to cut interest rates by more than 300 basis points. I agree with the market that the Fed likely will not be able to raise rates by 100 basis points a year without threatening to undermine the recovery. But even if this were possible, the chances are very high that recession will come before there is room to cut rates by enough to offset it. The knowledge that this is the case must surely reduce confidence and inhibit demand.

So let me rephrase this, to underline the points.

  1. Every business recovery has a finite length
  2. The current business recovery has gone on longer than most and probably will end within two or three years
  3. The US Federal Reserve, therefore, has a limited time in which to restore the federal funds rate to something like its historically “normal” levels
  4. But this means a rapid acceleration of interest rates over the next two to three years, something which almost inevitably will speed the onset of a business downturn and which could have alarming global implications
  5. Thus, the Fed probably will not be able to restore the federal funds rate – actually the only rate they directly control – to historically normal values
  6. Therefore, Fed tools to combat the next recession will be severely constrained.
  7. Given these facts and suppositions, secondary speculative/financial and other responses can arise which themselves can become major developments to deal with.

Header pic of fox and hedgehog from willpowered.co.

Federal Reserve Plans to Raise Interest Rates

It is widely expected the US Federal Reserve Bank will raise the federal funds rate from its seven-year low below 0.25 percent to maybe 0.50 percent. Then, further increases will bring this key short term rate back in line with its historic profile gradually, depending on the health of the US economy and international factors.

This will probably occur next week at the meeting of the Federal Open Market Committee (FOMC), December 15-16.

Here’s a chart from the excellent St. Louis Federal Reserve data site (FRED) showing how unusual recent years are in terms of this key interest rate.

FedFundsRate2

Shading in the chart indicates periods of recession.

Thus, the federal funds rate – which is the rate charged on overnight loans to banking members of the Federal Reserve system – was pushed to the zero bound as a response to the financial crisis and recession 2008-2009.

A December increase has been discussed by prominent members of the Federal Open Market Committee and, of course, in Janet Yellen’s testimony before the US Congress, December 3.

Yet discussion still considers the balance between ‘doves’ and ‘hawks’ on the FOMC. Next year, apparently, FOMC membership may shift toward more ‘hawks’ in voting positions – bankers who see inflation risks from the current recovery. See, for example, Richard Grossman’s Birdwatching at the Federal Reserve.

How far will interest rates rise? One way to address this is by considering the Fed funds futures contract. Currently, the CME futures data indicate a rise to 1.73% over the next 36 months.

All this seems long overdue, based on historical interest rate levels, but that does not stop some alarmist talk.

BIS Warns The Fed Rate Hike May Unleash The Biggest Dollar Margin Call In History

As a result, our only question for the upcoming Fed rate hike is how long it will take before the Fed, shortly after increasing rates by a modest 25 bps to “prove” to itself if not so much anyone else that the US economy is fine, will be forced to mainline trillions of dollars around the globe via swap lines for the second time in a row as the world experiences the biggest USD margin call in history.

By the end of next week or probably just after the first of 2016, interest rates may move a little from the zero bound, and from then on, one fulcrum of all business and economic forecasts will be the pace of further increases.

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

noncausal

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 –

noncausal2

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.

bitcoin

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,

“..it 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

Wavlet1

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.

shenzhen

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.

Caijin

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.

Reflections

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.

MonthSPY

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.

2008SPYMonth

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

REMIblocs

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..

RichWobbekind

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.

FlashCrash

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.

FCerror

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.

WeeklySPYMP

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.