All posts by Clive Jones

global business forecasts, macroeconomic forecasting, Uncategorized

Investment and Other Bank Macro Forecasts and Outlooks – 2

November 13, 2014 Clive Jones

Today, I take a brief look at economic forecasts available from Morgan Stanley, Wells Fargo, and the French concern Credit Agricole. As readers will note, Morgan Stanley has a lively discussion of the implications of the US midterms, while Wells Fargo has a very comprehensive and easy-to-access series of economic projections, ranging from weekly, to monthly and annual. Credit Agricole (apologies for omitting the accent mark) is the first European bank profiled in these brief looks, and has quarterly updates of fairly comprehensive economic projections across a range of variables.

And I might mention that these publications, which date back into September in many cases, are interesting to review both because of their projections and because of what they miss – notably the drop in oil prices and aggressive new round of quantitative easing by the Bank of Japan.

The fact these developments are missed in these September and even later releases qualifies them as genuine surprises. Thus, their impacts are not discounted in past market developments, and, going forward, oil prices and Japan QE could exert significant, discrete effects on markets.

Morgan Stanley

According to the Federal Reserve’s National Information Center, Morgan Stanley is the nation’s 6^th largest bank.

The Global Investment Committee (GOC) Weekly for November 10 is notable for some straight talk on the Implications of the US midterms, which Morgan Stanley see as slightly pro-growth, positive for equities, with constructive compromises, characteristic of lame duck presidencies. I quote fairly extensively, because the frankness of the insights and suggestions is refreshing.

The maxim that gridlock in Washington is good for markets has certainly held true during the “do nothing” Congress of the past two years. Now, with the Republicans winning control of the Senate and adding 15 seats to their House majority, the outlook appears to be for more of the same. Happily for investors, an analysis going back to 1900 shows that equity markets have averaged annualized 15% returns when the Congress is controlled by Republicans and the White House by a Democrat.

Although many pundits have suggested that the GOP sweep creates a mandate, the Global Investment Committee (GIC) sees the results as a mandate for change in the functioning and compromise in Washington rather than the embrace of a specific agenda. On that score, unlike the deeply partisan divide between the House and the Senate of the last four years that prevented any compromise bills from getting off the Hill, legislation may actually get to the president’s desk. While President Obama will be free to veto, he is now playing for his legacy and may be apt to compromise on some issues.

The Republicans’ challenge is to demonstrate leadership and competence in governing, a task that will require corralling the Tea Party caucus and, as Morgan Stanley & Co. Chief US Economist Vincent Reinhart wrote last week, “sequencing priorities” in a constructive way. Lacking a coherent issue-driven platform, most Republicans simply ran against Obama. Party infighting or an immediate battle about the debt ceiling and budget authorizations would likely be disastrous for the GOP—and the markets. From the GIC’s perspective, a better result would be for Congress to focus on job-creating initiatives and not on eviscerating the Affordable Care Act (ACA).

Agreement should be easiest around initiatives involving the energy sector, where this year’s 25% decline in oil prices has been front and center. American energy independence is no longer a dream but a real prospect with profound geopolitical as well as economic consequences (see Chart of the Week, page 3). Heretofore, the Keystone XL pipeline, a six-year-old proposal to connect Canadian oil with US Gulf Coast refineries, has been stalled amid wrangling with environmentalists. We believe the pipeline is now likely to win approval, creating a large national infrastructure project. Similarly, the growth of US energy supply is likely to reignite a debate on oil exports, which have been banned since the Arab oil embargoes of the 1970s. With US dollar strength likely to crimp other exports, expanding energy exports is a way to maintain economic growth. There is likely to be similar debate about exports of liquefied natural gas as the US is the world’s largest and lowest-cost producer. We believe that energy exports would be a major beneficiary focus if the new Congress approves the Trans-Pacific Partnership, a free trade agreement that would give the president authority to negotiate deals with 11 Asian nations.

Beyond energy, we expect repeal of the medical-device tax; expansion of defense spending, which has been curtailed under sequestration; and a debate on corporate tax reform, especially given the noise around tax-driven international mergers. Revisions to the ACA, to the extent they are pursued, will likely focus on measures that impact the number of insured and thus, hospitals and managed-care companies. The employer mandate, which requires employers with more than 100 workers to make available health insurance for any employee working more than 30 hours per week, is most likely to be revised, in our view.

As a final note, a review of state and local ballot initiatives suggest that voters are far from embracing an ideological position on fiscal austerity. Minimum-wage increases were passed in each state where they were on the ballot as did several large new-money infrastructure projects in New York and California—a development that MS & Co. Municipals Strategist, Michael Zezas, notes will likely increase bond supplies in 2015.

It looks like the august Global Economic Forum is being being published more infrequently than in the past, the last edition being March 5 of this year.

Wells Fargo

Wells Fargo, accounting to Wikipedia is –

an American multinational banking and financial services holding company which is headquartered in San Francisco, California, with “hubquarters” throughout the country… It is the fourth largest bank in the U.S. by assets and the largest bank by market capitalization…Wells Fargo is the second largest bank in deposits, home mortgage servicing, and debit cards. In 2011, Wells Fargo was the 23rd largest company in the United States.

The Wells Fargo website has a suite of forecasting reports, ranging from weekly, to monthly, to the big annual report, all downloadable in PDF format.

In October, the bank also released this video interview about their economic outlook.

In case you did not get time to watch that, one of the key graphics is the PCE deflator, which has been trending down recently, raising the spectre of deflation in the minds of some.

Credit Agricole

Credit Agricole is an international full services banking company, headquartered in France, with historical ties to French farming,

Their website offers at least two quarterly macroeconomic forecasting publications.

The publication Economic and Financial Forecasts presents a series of tabular forecasts for interest rates, exchange rates and commodity prices, together with the Crédit Agricole Group’s central economic projections. This is a kind of “just the numbers ma’am report.”

Macro Prospects is more discursive and with short highlights on key countries, such as, in the September issue, Brazil and China.

I signed up for emails from Credit Agricole, announcing updates of these documents.

financial forecasts, global business forecasts, macroeconomic forecasting

Investment and Other Bank Macro Forecasts and Outlooks – 1

November 12, 2014 Clive Jones

In yesterday’s post, I detailed the IMF World Economic Outlook revision for October 2014, recent OECD macroeconomic projections, and latest from the Survey of Professional Forecasters.

All these are publically available, quite comprehensive forecasts, sort of standards in the field.

But there also are a range of private forecasts, and I want to focus on investment and other bank forecasts for the next few posts – touching on Goldman Sachs and JP Morgan today.

Goldman Sachs

Goldman Sachs – video presentations on global economic outlook with additional videos for the US, Europe, and major global regions. December 2013

Goldman Sachs, Economic Outlook for the United States, June 2014, Jan Hatzius

Goldman Sachs Asset Management, FISG Quarterly Outlook Q4 2014, (click on the right of the page for Full Document). This is the most up-to-date forecast/commentary I am able to find, and has a couple of relevant points.

One concerns the policy divergence at the central bank level. This is even more true now than when the report was released (probably in October), since the Bank of Japan is plunging into new, aggressive quantitative easing (QE), while the US Fed has ended its QE program, for the time being at least.

The other point concerns the European economy.

Among our economic forecasts, our negative outlook on the Eurozone represents the biggest departure from consensus. We believe policymakers will struggle to correct the trend of poor growth and disinflation. Optimism about the peripheries has faded, and the Eurozone’s powerhouse economy, Germany, has slowed amid weak global demand. Once again the Eurozone’s political divisions and fiscal constraints leave the ECB as the only authority able to respond unilaterally to the threat of a sharper downturn, though hopes of fiscal action are mounting.

Some signs of a sustainable Eurozone recovery have not held up to closer inspection. The peripheries have made substantial progress on austerity and structural reforms, but efforts appear to have stalled, and Spain has probably reaped the most it can from its adjustment for now. Italy’s policy paralysis and relapse into recession is disappointing given this year’s changing of the political guard, which saw Silvio Berlusconi’s exit and Prime Minister Matteo Renzi’s election on a heavily reformist platform. Renzi has shifted gears from political reform to labor reform, which could get under way in early 2015. But Italy’s high debt stock makes it particularly vulnerable to a market backlash, and we are watching for signs of investor pullback that could drive sovereign yields higher.

JP Morgan

JP Morgan has a 2014 Economic Outlook in a special issue of their Thought magazine. This is definitely dated, but there is a weekly Economic Update in a kind of scorecard format (up/down/nochange) from their Asset Management Group.

I’ve got to say, however, that one of the most exciting publications along these lines is their quarterly Guide to the Markets from JP Morgan Asset Management. Here are highlights from an interactive version of the 4Q Guide.

First, the scope of coverage is impressive, although, note this is more of an update of conditions, than a forecast. The reader supplies the forecasts, however, from these engaging slides.

But this slide does not need to produce a forecast to make its point – which is maybe we are not in a stock market bubble but at the start of a long upward climb in the market. Optimism forever!

There are plenty of slides that have moral to the story, such as this one on education and employment.

Then, this graphic on China is extremely revealing, and suggests a forward perspective.

I’m finding this excursion into bank forecasts productive and plan coming posts along these lines. I’d rather use the blog as a scratch-pad to share insights as I go along, than produce one humungous summary. So stay tuned.

Top photo courtesy of the University of Richmond

Chinese economy, emerging market forecasts, global business forecasts, macroeconomic forecasting

Global and US Economic Outlook – November 2014

November 11, 2014 Clive Jones

There are a number of free, publically available macroeconomic forecast resources which have standing and a long track record.

Also, investment and other banks make partial releases of their macro projections.

IMF World Economic Outlook

The International Monetary Fund (IMF) revises its World Economic Outlook (WEO) toward the end of each year, this year in October with Legacies, Clouds, Uncertainties.

One advantage is comprehensive coverage. So there are WEO projections over 1, 2 and 3 year horizons for more than 100 countries, even obscure island principalities, and for dozens of variables, including GDP variously measured, inflation, imports and exports, unemployment rate, and population.

Here are highlights of the October revision (click to enlarge).

Largely due to weaker-than-expected global activity in the first half of 2014, the growth forecast for the world economy has been revised downward to 3.3 percent for this year, 0.4 percentage point lower than in the April 2014 World Economic Outlook (WEO). The global growth projection for 2015 was lowered to 3.8 percent.

The global recovery continues to be uneven, with some countries and areas struggling, while others move forward into growth.

Downside risks are increasing and include –

SHORT TERM: worsening geopolitical tensions (Ukraine, Syria) and reversal of recent risk spread and volatility compression in financial markets

MEDIUM TERM: stagnation and low potential growth in advanced economies (Eurozone flirting with deflation) and a decline in potential growth in emerging markets

Organization of Economic Cooperation and Development (OECD) Projections

The OECD Economic Outlook Advance Release for the G-20 from October 2014 projects the following growth rates for 2014 and 2015 (click to enlarge).

For total global GDP growth, the OECD projects 3.3 percent for 2014 and 3.7 percent for 2015 or 0.1 percent less for 2015 than the IMF.

Chinese economic growth is ratcheting down from double-digit levels several years ago, to around 7 percent, while Indian GDP growth is projected to stay in the 6 percent range.

There are significant differences in the IMF and OECD forecasts for the United States.

Survey of Professional Forecasters

The Survey of Professional Forecasters (SPF) is another publically available set of macroeconomic forecasts, but focusing on the US economy. The SPF is maintained by the Philadelphia Federal Reserve Bank, which polls participating analysts quarterly, compiling consensus results, spreads, and distributions.

The latest SPF Survey was released August 2014, and is somewhat more optimistic about US economic growth than the IMF and OECD projections.

Investment Bank Data and Projections

Wells Fargo Securities Economics Group produces a monthly report with detailed quarterly forecasts for the US economy. Here is a sample from August 2014 (click to enlarge).

I’m compiling a list of these products and their availability.

The bottom line is there are plenty of forecasts to average together to gin up high likelihood numbers to plug into sales and other business forecast models.

At the same time, there is a problem with calling turning points in almost all these products.

This is not a problem on YouTube now, though. If you search “economic forecasts 2015” on YouTube today, you will see a lengthly list of predictions of economic collapse and market catastrophe by the likes of Jim Rogers, Gerald Calente, and others who dabble in this genre.

We need something like the canary in the coal mine.

celebrity forecasters, election forecasts, predictive analytics

Predicting the Midterm Elections

November 10, 2014 Clive Jones

Predicting the outcome of elections is a fascinating game with more and more sophisticated predictive analytics.

The Republicans won bigtime, of course.

They won comfortable control of the US Senate and further consolidated their majority in the House of Representatives.

Counting before the Louisiana runoff election, which a Republican is expected to win, the balance is 52 to 44 in the Senate, highlighted in the following map from Politico.

In the US House of Representatives, Republicans gained 12 seats for a 57 percent majority, 244 to 184, as illustrated in a New York Times graphic.

Did Anyone See This Coming?

Nate Silver, who was prescient in the 2012 General Election, issued an update on his website FiveThirtyEight on November 4 stating that Republicans Have A 3 In 4 Chance Of Winning The Senate.

And so they did win.

Salon’s review of Silver’s predictions notes that,

Overall, the candidate with better-than-even odds in FiveThirtyEight’s model won or is likely to in 34 of the 36 Senate contests this year, for a success rate of 94 percent.

The track record for the governorships was less shining, with upsets in Maryland and Kansas and several wins by candidates with unfavorable odds in the FiveThirtyEight lineup.

Bias in Polls

Silver’s forecasting model weighs both polling data and fundamentals- like demographics.

After the election, Silver blamed some of his mistakes on bias in polls, claiming that, this time, the Polls Were Skewed Toward Democrats.

Based on results as reported through early Wednesday morning …. the average Senate poll conducted in the final three weeks of this year’s campaign overestimated the Democrat’s performance by 4 percentage points. The average gubernatorial poll was nearly as bad, overestimating the Democrat’s performance by 3.4 points.

He backs this up with details of bias in polls by race, and, interestingly, throws up the following exhibit, suggesting that there is nothing systematic about bias in the polls.

Here is another discussion of mid-term election polling error – arguing it is significantly greater during midterms than in Presidential election years.

While not my area of expertise (although I have designed and analyzed survey data), I’m think the changing demographics of “cell-only” voters, no-call lists, and people’s readiness to hang up on unsolicited calls impacts the reliability of polling data, as usually gathered. What Silver seems to show with his graphic above, is that adjusting for these changes causes another form of unreliability.

global business forecasts, impact of QE, Quantitative Easing (QE)

The End of Quantitative Easing, the Expansion of QE

November 9, 2014 Clive Jones

The US Federal Reserve Bank declared an end to its quantitative easing (QE) program at the end of October.

QE involves direct Fed intervention into buying longer term bonds with an eye to exercising leverage on long term interest rates and, thus, encouraging investment. Readers wanting more detail on how QE is implemented – check Ed Dolan’s slide show Quantitative Easing and the Fed 2008-2014: A Tutorial

The New York Times article on the Fed actions – Quantitative Easing Is Ending. Here’s What It Did, in Charts – had at least two charts that are must-see’s.

First, the ballooning of the Federal Reserve Balance sheet from less than $1 trillion to $4.5 trillion today –

Secondly, according to Times estimates, about 40 percent of Fed assets are comprised of mortgage-backed securities now – making the Fed a potential major player in the US housing markets.

Several recent articles offer interpretation – what does the end of this five-year long program mean for the US economy and for investors. What were the impacts of QE?

I thought Jeff Miller’s “Old Prof” compendium was especially good – Weighing the Week Ahead: What the End of QE Means for the Individual Investor. If you click this link and find a post more recent than November 1, scroll down for the QE discussion. Basically, Miller thinks the impact on investors will be minimal.

This is also true in the Business Week article The Hawaiian Tropic Effect: Why the Fed’s Quantitative Easing Isn’t Over

But quantitative easing is the gift that keeps on giving. Even after the purchases end, its effects will persist. How could that be? The Fed will still own all those bonds it bought, and according to the agency itself, it’s the level of its holdings that affects the bond market, not the rate of addition to those holdings. Having reduced the supply of bonds available on the market, the Fed has raised their price. Yields (i.e. market interest rates) go down when prices go up. So the effect of quantitative easing is to lower interest rates for things Americans actually care about, such as 30-year fixed-rate mortgages.

Some other articles which attempt to tease out exactly what impacts QE did have on the economy –

Evaluation of quantitative easing QE had “some effects” but it’s one of several influences on the bond market and long term interest rates.

Quantitative easing: giving cash to the public would have been more effective

QE has also had unforeseen side-effects. The policy involved allowing banks and other financial institutions to exchange bonds for cash, and the hope was that this would lead to improved flows of credit to firms looking to expand. In reality, it encouraged financial speculation in property, shares and commodities. The bankers and the hedge fund owners did well out of QE, but the side-effect of footloose money searching the globe for high yields was higher food and fuel prices. High inflation and minimal wage growth led to falling real incomes and a slower recovery.

What Quantitative Easing Did Not Do: Three Revealing Charts – good discussion organized around the following three points –

QE did not work according to the textbook model
QE did not cause inflation
QE was not powerful enough to overcome fiscal restraint

Expansion of QE

But quantitative easing as a central bank policy is by no means a dead letter.

In fact, at the very moment the US Federal Reserve announced the end of its five-year long program of bond-buying, the Bank of Japan (BOPJ) announced a significant expansion of its QE, as noted in this article from Forbes.

Last week, as the Federal Reserve officially announced the end of its long-term asset purchase program (commonly known as QE3), the Bank of Japan significantly ratcheted up its own quantitative easing program, in a surprising 5-4 split decision. Starting next year, the Bank of Japan will increase its balance sheet by 15 percent of GDP per annum and will extend the average duration of its bond purchases from 7 years to 10 years. The big move by Japan’s central bank comes amid the country’s GDP declining by 7.1% in the second quarter of 2014 (on an annualized basis) from the previous quarter following the increase of the VAT sales tax from 5% to 8% in Japan earlier this year and worries that Japan could fall into another deflationary spiral..

The scale of the Japanese effort is truly staggering, as this chart from the Forbes article illustrates.

The Economist article on this development Every man for himself tries to work out the implications of the Japanese action on the value of the yen, Japanese inflation/deflation, the Japanese international trade position, impact on competitors (China), and impacts on the US dollar.

What about Europe? Well, Bloomberg offers this primer – Europe’s QE Quandary. Short take – there are 18 nations which have to agree and move together, Germany’s support being decisive. But deflation appears to be spreading in Europe, so many expect something to be done along QE lines.

If you are forecasting for businesses, government agencies, or investors, these developments by central banks around the world are critically important. Their effects may be subtle and largely in unintended consequences, but the scale of operations means you simply have to keep track.

asset bubbles, cointegration in time series, forecasting research

Forecasting the Downswing in Markets – II

November 7, 2014 Clive Jones

Because the Great Recession of 2008-2009 was closely tied with asset bubbles in the US and other housing markets, I have a category for asset bubbles in this blog.

In researching the housing and other asset bubbles, I have been surprised to discover that there are economists who deny their existence.

By one definition, an asset bubble is a movement of prices in a market away from fundamental values in a sustained manner. While there are precedents for suggesting that bubbles can form in the context of rational expectations (for example, Blanchard’s widely quoted 1982 paper), it seems more reasonable to consider that “noise” investors who are less than perfectly informed are part of the picture. Thus, there is an interesting study of the presence and importance of “out-of-town” investors in the recent run-up of US residential real estate prices which peaked in 2008.

The “deviations from fundamentals” approach in econometrics often translates to attempts to develop or show breaks in cointegrating relationships, between, for example, rental rates and housing prices. Let me just say right off that the problem with this is that the whole subject of cointegration of nonstationary time series is fraught with statistical pitfalls – such as weak tests to reject unit roots. To hang everything on whether or not Granger causation can or cannot be shown is to really be subject to the whims of random influences in the data, as well as violations of distributional assumptions on the relevant error terms.

I am sorry if all that sounds kind of wonkish, but it really needs to be said.

Institutionalist approaches seem more promising – such as a recent white paper arguing that the housing bubble and bust was the result of a ..

supply-side phenomenon, attributable to an excess of mispriced mortgage finance: mortgage-finance spreads declined and volume increased, even as risk increased—a confluence attributable only to an oversupply of mortgage finance.

But what about forecasting the trajectory of prices, both up and then down, in an asset bubble?

What can we make out of charts such as this, in a recent paper by Sornette and Cauwels?

Sornett and the many researchers collaborating with him over the years are working with a paradigm of an asset bubble as a faster than exponential increase in prices. In an as yet futile effort to extend the olive branch to traditional economists (Sornette is a geophysicist by training), Sornette evokes the “bubbles following from rational expectations meme.” The idea is that it could be rational for an investor to participate in a market that is in the throes of an asset bubble, providing that the investor believes that his gains in the near future adequately compensate for the increased risk of a collapse of prices. This is the “greater fool” theory to a large extent, and I always take delight in pointing out that one of the most intelligent of all human beings – Isaac Newton – was burned by exactly such a situation hundreds of years ago.

In any case, the mathematics of the Sornette et al approach are organized around the log-periodic power law, expressed in the following equation with the Sornette and Cauwels commentary (click to enlarge).

From a big picture standpoint, the first thing to observe is that there is a parameter t_c in the equation which is the “critical time.”

The whole point of this mathematical apparatus, which derives in part from differential equations and some basic modeling approaches common in physics, is that faster than exponential growth is destined to reach a point at which it basically goes ballistic. That is the critical point. The purpose of forecasting in this context then is to predict when this will be, when will the asset bubble reach its maximum price and then collapse?

And the Sornette framework allows for negative as well as positive price movements according to the dynamics in this equation. So, it is possible, if we can implement this, to predict how far the market will fall after the bubble pops, so to speak, and when it will turn around.

Pretty heady stuff.

The second big picture feature is to note the number of parameters to be estimated in fitting this model to real price data – minimally constants A, B, and C, an exponent m, the angular frequency ω and phase φ, plus the critical time.

For the mathematically inclined, there is a thread of criticism and response, more or less culminating in Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model which used to be available as a PDF download from ETC Zurich.

In brief, the issue is whether the numerical analysis methods fitting the data to the LPPL model arrive at local, instead of global maxima. Obviously, different values for the parameters can lead to wholly different forecasts for the critical time t_c.

To some extent, this issue can be dealt with by running a great number of estimations of the parameters, or by developing collateral metrics for adequacy of the estimates.

But the bottom line is – regardless of the extensive applications of this approach to all manner of asset bubbles internationally and in different markets – the estimation of the parameters seems more in the realm of art, than science, at the present time.

However, it may be that mathematical or computational breakthroughs are possible.

I feel these researchers are “very close.”

In any case, it would be great if there were a package in R or the like to gin up these estimates of the critical time, applying the log-periodic power law.

Then we could figure out “how low it can go.’

And, a final note to this post – it is ironic that as I write and post this, the stock markets have recovered from their recent swoon and are setting new records. So I guess I just want to be prepared, and am not willing to believe the runup can go on forever.

I’m also interested in methodologies that can keep forecasters usefully at work, during the downswing.

macroeconomic forecasting, mixed data frequencies, nowcasting, predicting recessions, predicting turning points

Forecasting the Downswing in Markets

November 4, 2014 Clive Jones

I got a chance to work with the problem of forecasting during a business downturn at Microsoft 2007-2010.

Usually, a recession is not good for a forecasting team. There is a tendency to shoot the messenger bearing the bad news. Cost cutting often falls on marketing first, which often is where forecasting is housed.

But Microsoft in 2007 was a company which, based on past experience, looked on recessions with a certain aplomb. Company revenues continued to climb during the recession of 2001 and also during the previous recession in the early 1990’s, when company revenues were smaller.

But the plunge in markets in late 2008 was scary. Microsoft’s executive team wanted answers. Since there were few forthcoming from the usual market research vendors – vendors seemed sort of “paralyzed” in bringing out updates – management looked within the organization.

I was part of a team that got this assignment.

We developed a model to forecast global software sales across more than 80 national and regional markets. Forecasts, at one point, were utilized in deliberations of the finance directors, developing budgets for FY2010. Our Model, by several performance comparisons, did as well or better than what was available in the belated efforts of the market research vendors.

This was a formative experience for me, because a lot of what I did, as the primary statistical or econometric modeler, was seat-of-the-pants. But I tried a lot of things.

That’s one reason why this blog explores method and technique – an area of forecasting that, currently, is exploding.

Importance of the Problem

Forecasting the downswing in markets can be vitally important for an organization, or an investor, but the first requirement is to keep your wits. All too often there are across-the-board cuts.

A targeted approach can be better. All market corrections, inflections, and business downturns come to an end. Growth resumes somewhere, and then picks up generally. Companies that cut to the bone are poorly prepared for the future and can pay heavily in terms of loss of market share. Also, re-assembling the talent pool currently serving the organization can be very expensive.

But how do you set reasonable targets, in essence – make intelligent decisions about cutbacks?

I think there are many more answers than are easily available in the management literature at present.

But one thing you need to do is get a handle on the overall swing of markets. How long will the downturn continue, for example?

For someone concerned with stocks, how long and how far will the correction go? Obviously, perspective on this can inform shorting the market, which, my research suggests, is an important source of profits for successful investors.

A New Approach – Deploying high frequency data

Based on recent explorations, I’m optimistic it will be possible to get several weeks lead-time on releases of key US quarterly macroeconomic metrics in the next downturn.

My last post, for example, has this graph.

Note how the orange line hugs the blue line during the descent 2008-2009.

This orange line is the out-of-sample forecast of quarterly nominal GDP growth based on the quarter previous GDP and suitable lagged values of the monthly Chicago Fed National Activity Index. The blue line, of course, is actual GDP growth.

The official name for this is Nowcasting and MIDAS or Mixed Data Sampling techniques are widely-discussed approaches to this problem.

But because I was only mapping monthly and not, say, daily values onto quarterly values, I was able to simply specify the last period quarterly value and fifteen lagged values of the CFNAI in a straight-forward regression.

And in reviewing literature on MIDAS and mixing data frequencies, it is clear to me that, often, it is not necessary to calibrate polynomial lag expressions to encapsulate all the higher frequency data, as in the classic MIDAS approach.

Instead, one can deploy all the “many predictors” techniques developed over the past decade or so, starting with the work of Stock and Watson and factor analysis. These methods also can bring “ragged edge” data into play, or data with different release dates, if not different fundamental frequencies.

So, for example, you could specify daily data against quarterly data, involving perhaps several financial variables with deep lags – maybe totaling more explanatory variables than observations on the quarterly or lower frequency target variable – and wrap the whole estimation up in a bundle with ridge regression or the LASSO. You are really only interested in the result, the prediction of the next value for the quarterly metric, rather than unbiased estimates of the coefficients of explanatory variables.

Or you could run a principal component analysis of the data on explanatory variables, including a rag-tag collection of daily, weekly, and monthly metrics, as well as one or more lagged values of the higher frequency variable (quarterly GDP growth in the graph above).

Dynamic principal components also are a possibility, if anyone can figure out the estimation algorithms to move into a predictive mode.

Being able to put together predictor variables of all different frequencies and reporting periods is really exciting. Maybe in some way this is really what Big Data means in predictive analytics. But, of course, progress in this area is wholly empirical, it not being clear what higher frequency series can successfully map onto the big news indices, until the analysis is performed. And I think it is important to stress the importance of out-of-sample testing of the models, perhaps using cross-validation to estimate parameters if there is simply not enough data.

One thing I believe is for sure, however, and that is we will not be in the dark for so long during the next major downturn. It will be possible to deploy all sorts of higher frequency data to chart the trajectory of the downturn, probably allowing a call on the turning point sooner than if we waited for the “big number” to come out officially.

Top picture courtesy of the Bridgespan Group

autoregressive model, blending data of different frequencies, macroeconomic forecasting

Mapping High Frequency Data Onto Aggregated Variables – Monthly and Quarterly Data

November 1, 2014 Clive Jones

A lot of important economic data only are available in quarterly installments. The US Gross Domestic Product (GDP) is one example.

Other financial series and indexes, such as the Chicago Fed National Activity Index, are available in monthly, or even higher frequencies.

Aggregation is a common tactic in this situation. So monthly data is aggregated to quarterly data, and then mapped against quarterly GDP.

But there are alternatives.

One is what Elena Andreou, Eric Ghysels and Andros Kourtellos call a naïve specification –

With daily (D) and quarterly (Q) data, there typically are a proliferation of parameters to estimate – 66 if you allow 22 trading days per month. Here N^D in the above equation is the number of days in the quarterly period.

The usual workaround is a weighting scheme. Thus, two parameter exponential Almon lag polynomials are identified with MIDAS, or Mixed Data Sampling.

However, other researchers note that with the monthly and quarterly data, direct estimation of expressions such as the one above (with X^M instead of X^D) is more feasible.

The example presented here shows that such models can achieve dramatic gains in accuracy.

Quarterly and Monthly Data Example

Let’s consider forecasting releases of the US nominal Gross Domestic Product by the Bureau of Economic Analysis.

From the BEA’s 2014 News Release Schedule for the National Economic Accounts, one can see that advance estimates of GDP occur a minimum of one month after the end of the quarter being reported. So, for example, the advance estimate for the Third Quarter was released October 30 of this year.

This means the earliest quarter updates on US GDP become available fully a month after the end of the quarter in question.

The Chicago Fed National Activity Index (CFNAI), a monthly guage of overall economic activity, is released three weeks after the month being measured.

So, by the time the preliminary GDP for the latest quarter (analyzed or measured) is released, as many as four CFNAI recent monthly indexes are available, three of which pertain to the months constituting this latest measured quarter.

Accordingly, I set up an equation with a lagged term for GDP growth and fifteen terms or values for CFNAImonthly indexes. For each case, I regress a value for GDP growth for quarter t onto GDP growth for quarter t-1 and values for all the monthly CFNAI indices for quarter t, except for the most recent or last month, and twelve other values for the CFNAI index for the three quarters preceding the final quarter to be estimated – quarter t-1, quarter t-2, and quarter t-3.

One of the keys to this data structure is that the monthly CFNAI values do not “stack,” as it were. Instead the most recent lagged CFNAI value for a case always jumps by three months. So, for the 3rd quarter GDP in, say, 2006, the CFNAI value starts with the value for August 2006 and tracks back 14 values to July 2005. Then for the 4th quarter of 2006, the CFNAI values start with November 2006, and so forth.

This somewhat intricate description supports the idea that we are estimating current quarter GDP just at the end of the current quarter before the preliminary measurements are released.

Data and Estimation

I compile BEA quarterly data for nominal US GDP dating from the first Quarter of 1981 or 1981:1 to the 4^th Quarter of 2011. I also download monthly data from the Chicago Fed National Activity Index from October 1979 to December 2011.

For my dependent or target variable, I calculate year-over-year GDP growth rates by quarter, from the BEA data.

I estimate an equation, as illustrated initially in this post, by ordinary least squares (OLS). For quarters, I use the sample period 1981:2 to 2006:4. The monthly data start earlier to assure enough lagged terms for the CFNAI index, and run from 1979:10 to 2006:12.

Results

The results are fairly impressive. The regression equation estimated over quarterly and monthly data to the end of 2006 performs much better than a simple first order autocorrelation during the tremendous dip in growth characterizing the Great Recession. In general, even after stabilization of GDP growth in 2010 and 2011, the high frequency data regression produces better out-of-sample forecasts.

Here is a graph comparing the out-of-sample forecast accuracy of the high frequency regression and a simple first order autocorrelation relationship.

What’s especially interesting is that the high frequency data regression does a good job of capturing the drop in GDP and the movement at the turning point in 2009 – the depth of the Great Recession.

I throw this chart up as a proof-of-concept. More detailed methods, using a specially-constructed Chicago Fed index, are described in a paper in the Journal of Economic Perspectives.

energy forecasting, forecasting turning points, futures markets

Do Oil and Gas Futures Forecast Oil and Gas Spot Prices?

October 29, 2014 Clive Jones

I’m looking at evidence that oil and gas futures are useful in forecasting future prices. This is an important for reasons ranging from investment guidance to policy analysis (assessing the role of speculators in influencing current market prices).

So – what are futures contracts, where are they traded, and where do you find out about them?

A futures contract (long position) is an agreement to buy an amount of a commodity (oil or gas) at a specified price at the expiration of the contract. The seller (the party with a short position) agrees to sell the underlying commodity to the buyer at expiration at the fixed sales price. Futures contracts can be traded many times prior to the expiration date.

At the expiration of the contract, if the price of the contract is below the market or spot price at that time, the buyer makes money. Futures contracts also can be used to lock in prices, and hedge risk.

The New York Mercantile Exchange (NYMEX) maintains futures markets for oil and gas. Natural gas futures are based on delivery at the Henry Hub, Louisiana, a major crossroads for natural gas pipelines.

So there are futures contracts for 1 month, 2 month, and so forth, delivery dates.

Evidence Futures Predict Spot Prices

As noted by Menzie Chinn, a popular idea is that the futures price is the optimal forecast of the spot price is an implication of the efficient market hypothesis.

Nevertheless, the evidence for futures prices being unbiased estimators of future spot prices is mixed, despite widespread acceptance of the idea in central banks and the International Monetary Fund (IMF).

A recent benchmark study, Forecasting the Price of Oil, finds –

some evidence that the price of oil futures has additional predictive content compared with the current spot price at the 12-month horizon; the magnitude of the reduction in mean-squared prediction error (MSPE) is modest even at the 12-month horizon, however, and there are indications that this result is sensitive to fairly small changes in the sample period and in the forecast horizon. There is no evidence of significant forecast accuracy gains at shorter horizons, and at the long horizons of interest to policymakers, oil futures prices are clearly inferior to the no-change forecast.

Here, the “no-change forecast” can be understood and is sometimes also referred to as a “random walk forecast.”

Both Chinn and the Forecasting the Price of Oil chapter in the Handbook of Forecasting are good places for readers to check the extensive literature on this topic.

Hands-On Calculation

Forecasting is about computation and calculation, working with real data.

So I downloaded the Contract1 daily futures prices from the US EIA, a source which also provides the Henry Hub spot prices.

Natural gas contracts, for example, expire three business days prior to the first calendar day of the delivery month. Thus, the delivery month for Contract 1 in the US EIA tables is the calendar month following the trade date.

Here is a chart from the spreadsheet I developed.

I compared the daily spot prices and 1 month futures contract prices by date to see how often the futures prices correctly indicate the direction of change of the spot price at the settlement or delivery date, three days prior to the first calendar day of the delivery month. So, the April 14, 2014 spot price was $4.64 and the Contract1 futures closing price for that day was $4.56, indicating that the spot price in late May would be lower than the current spot price. In fact, the May 27th spot price was $4.56. So, in this case, not only was the predicted direction of change correct, but also the point estimate of the future spot price.

The chart above averages the performance of these daily forecasts of the future direction of spot prices over rolling 20 trading day windows.

From January through the end of September 2014, these averages score better than 50:50 about 71 percent of the time.

I have not calculated how accurate these one month natural gas futures are per se, but my guess is that the accuracies would be close.

However, clearly, a “no-change forecast” is incapable of indicating the future direction of changes in the gas spot price.

So the above chart and the associated information structure are potentially useful regardless of the point forecast accuracy. My explorations suggest additional information about direction and, possibly, even turning points in price, can be extracted from longer range gas futures contracts.

asset bubbles, energy forecasting, predictive analytics

Speculators and Oil Prices

October 26, 2014 Clive Jones

One of the more important questions in the petroleum business is the degree to which speculators influence oil prices.

If speculators can significantly move oil spot prices, there might be “overshooting” on the downside, in the current oil price environment. That is, the spot price of oil might drop more than fundamentals warrant, given that spot prices have dropped significantly in recent weeks and the Saudi’s may not reduce production, as they have in the past.

This issue can be rephrased more colorfully in terms of whether the 2008 oil price spike, shown below, was a “bubble,” driven in part by speculators, or whether, as some economists argue, things can be explained in terms of surging Chinese demand and supply constraints.

James Hamilton’s Causes and Consequences of the Oil Shock of 2007–08, Spring 2009, documents a failure of oil production to increase between 2005-2007, and the exponential growth in Chinese petroleum demand through 2007.

Hamilton, nevertheless, admits “the speed and magnitude of the price collapse leads one to give serious consideration to the alternative hypothesis that this episode represents a speculative price bubble that popped.”

Enter hedge fund manager Michael Masters stage left.

In testimony before the US Senate, Masters blames the 2007-08 oil price spike on speculators, and specifically on commodity index trading funds which held a quarter trillion dollars worth of futures contracts in 2008.

Hamilton characterizes Masters’ position as follows,

A typical strategy is to take a long position in a near-term futures contract, sell it a few weeks before expiry, and use the proceeds to take a long position in a subsequent near-term futures contract. When commodity prices are rising, the sell price should be higher than the buy, and the investor can profit without ever physically taking delivery. As more investment funds sought to take positions in commodity futures contracts for this purpose, so that the number of buys of next contracts always exceeded the number of sells of expiring ones, the effect, Masters argues, was to drive up the futures price, and with it the spot price. This “financialization” of commodities, according to Masters, introduced a speculative bubble in the price of oil.

Where’s the Beef?

If speculators were instrumental in driving up oil prices in 2008, however, where is the inventory build one would expect to accompany such activity? As noted above, oil production 2005-2007 was relatively static.

There are several possible answers.

One is simply that activity in the futures markets involve “paper barrels of oil” and that pricing of real supplies follows signals being generated by the futures markets. This is essentially Masters’ position.

A second, more sophisticated response is that the term structure of the oil futures markets changed, running up to 2008. The sweet spot changed from short term to long term futures, encouraging “ground storage,” rather than immediate extraction and stockpiling of inventories in storage tanks. Short term pricing followed the lead being indicated by longer term oil futures. The MIT researcher Parsons makes this case in a fascinating paper Black Gold & Fool’s Gold: Speculation in the Oil Futures Market.

..successful innovations in the financial industry made it possible for paper oil to be a financial asset in a very complete way. Once that was accomplished, a speculative bubble became possible. Oil is no different from equities or housing in this regard.

A third, more conventional answer is that, in fact, it is possible to show a direct causal link from activity in the oil futures markets to oil inventories, despite the appearances of flat production leading up to 2008.

Where This Leads

The uproar on this issue is related to efforts to increase regulation on the nasty speculators, who are distorting oil and other commodity prices away from values determined by fundamental forces.

While that might be a fine objective, I am more interested in the predictive standpoint.

Well, there is enough here to justify collecting a wide scope of data on production, prices, storage, reserves, and futures markets, and developing predictive models. It’s not clear the result would be most successful short term, or for the longer term. But I suspect forward-looking perspective is possible through predictive analytics in this area.

Top graphic from Evil Speculator.

Business Forecasting