What to Expect in 2014 (And Beyond)

This outlook is being written a good 45 days later than when “What to Expect in 2013…” was written over a year ago. It is amazing how much can happen in that short time frame and can influence one’s view of the next year. If I let another 45 days pass I am sure there would be some things that would change. I believe the risks are to the upside on more positive news on the economy but at some point that news could affect Fed action.  Much of what could happen this coming year is influenced by what is going on in the energy sector. Middle East economies, of course, but also inflation, GDP growth, and geopolitical events will affect markets and the US economy specifically. These points will become clear as I spell out some of my expectations. Understand that these expectations follow the Byron Wien formula where I believe there is greater than a 50% chance they happen when the rest of the world may not agree. The “Expectations” are designed to stimulate thought. Some of them can relate directly to the securities markets, but some do not, and this year, a little whimsy. Hopefully, you can figure out which one that is. Let’s begin:

  1. After printing two 4% GDP quarters in 2013 and seeing a 1 percentage point drop in the unemployment rate, there is finally some recognition that, maybe, the Fed’s actions really did produce some stimulus. This could lead to self-sustaining growth in the US economy in 2014 with at least one more 4% print this year. Less noise from the crazies in Washington adds to business confidence and, ultimately, capital expenditures.
  2. Economic growth and job creation become more apparent with forecasts for a decline in the unemployment rate possibly approaching 6% before the end of the year. The Federal Reserve begins making noise about speeding up tapering and hints at reducing the time the Funds rate would remain anchored at its current level. This is in spite of limited evidence, at least early in the year, that the inflation rate is approaching the targeted 2% level. This ultimately has a dampening effect on the markets.
  3. We begin seeing some academic work and, of course, the pundits talking about an acceleration of the technological revolution making the case that low inflation or maybe even some signs of deflation are actually a good thing in this technologically driven environment. The low inflation picture is reinforced at the headline level by energy supplies expanding within the US, in the Middle East from Iraq and, ultimately, Iran. As other countries embrace fracking the potential for even more supply keeps downside pressure on energy prices.
  4. The negative elements on inflation, which are not sufficient to cause major concerns, come via erratic supply in soft commodities from continuation of drought in certain areas combined with weather abnormalities which, more and more, are blamed on climate change. As we get into the latter part of the year, the improving developed market economies combined with growth in Asia put some upward pressure on hard commodities. Investors must make the decision to invest in the extraction companies that have suffered from low prices or directly into the commodities themselves.
  5. The positive change in US trade balances from lower imports of energy combined with rising energy exports adds more than a percentage point to US GDP and reinforces the case for a strong dollar relative to almost every other currency except possibly the Chinese yuan. Asia shows growing signs of a currency war fueled by the impact of further weakening of the Japanese yen beginning to very seriously affect the export trade of its Asian competitors.  While this has a tendency to push up inflation rates in many of the Asian countries, the developed markets benefit from lower prices on many imported goods further softening their inflation rates.
  6. The impact of the currency wars raises questions about the stability of some of the emerging markets, particularly in Asia. There are also concerns about the pace of wage increases in these heretofore attractive locations for outsourcing. Manufacturing and some service corporations begin making different strategic decisions on the best places to locate manufacturing and processing centers.  The decisions are reinforced by a growing belief that technological advances will continue to allow capital to substitute for labor, or at least keep pressure on wages. More business activities find their way back into the developed countries of the world. China moves cautiously in the same direction, taking advantage of its own technological progress. It begins marketing itself as a technological leader as opposed to a low-cost labor market. This is not easy as China, at the same time, continues to push toward a more consumer-oriented society. Incomes have to rise and, politically, the population needs to be kept content. It will not be a smooth year for China.
  7. Coming elections in India point to a possible loss of leadership for the Congress party. Combined with continued economic difficulties and some strife associated with the potential leadership change, the country moves further down the path of being even less attractive for foreign direct investment. It loses another year to the relative growth of its Asian neighbors and finds itself participating in the currency wars as a possible way to salvage elements of growth.
  8. With the exception of Chile, Colombia, Mexico and Panama, the rest of Central and South America flounders. The US begins to pay more attention to its southern neighbors. Out of desperation, Argentina reaches a settlement on its outstanding debt and begins a focus on building its energy sector with some help from outside sources. A Menem-like regime change becomes a more likely political outcome.
  9. The changing energy picture outside the Middle East, combined with likely increased production out of Iraq and, ultimately, Iran, result in a change in the relative importance of Saudi Arabia and, to some extent, Israel. This could produce some positive movement in the Palestinian situation, and some changes in the relationships of Saudi Arabia with the rest of the Middle East and possibly Asia as the US becomes an even smaller market for its oil and an export competitor. On the other hand it raises the risk of some turmoil in the region as the power picture changes and attempts are made to preserve the old order in  a possibly military fashion.
  10. The fading newspaper industry surprises the street with its earnings in the early part of the year and benefits from contentious congressional races in the third and fourth quarters as well. The advertising related to Academy Award nominations and ultimately selections reaches new heights in print and social media. Studios advertise some small (but not cheap) movies to extremes to compete with some very high quality films and performances. We actually walked out of a couple of the most highly advertised ones. Aren’t two-page spreads a little extreme? Unfortunately, the correlation between the advertising and the nominations and awards becomes very direct leaving it up to the audiences to hopefully, make their own decisions after the fact. The quality and audience continue to rise for television productions and the associated delivery mechanisms for these performances leaving 3-D sequels and prequels to the movie industry. Can’t wait for “Inside Llewyn Davis Today–in IMax.”

So what does this all mean for the markets? I wish I knew. History says that the kind of equity market we had in the US in 2013 is usually followed by a decent year.  I don’t think it is that simple. We could see some re-allocation by institutions whose US equity portfolios have been pushed above their target percentages. At the same time, if we are beginning to return to a more normal relationship between earnings yields and fixed income yields, traditional debt doesn’t look that attractive. It may mean that markets outside the US are more attractive–maybe Europe and maybe some of the emerging markets if the currency is hedged out. There are some risk elements in the geopolitical situation. I think we will have to look harder for returns this year and the risks are high enough to look for some less correlated investments. I wouldn’t reduce my equity exposure, but I might change the mix.

We’ll have to see if another 45 days sets us up for totally different surprises. If nothing else I hope this has provided some food for thought.

I have some longer term expectations including a carryover from past years which, one of these days, will actually come to pass. I include these as additional repast for the brain. As has been the case since the millennium, the year will likely be more interesting than we anticipated.

  1. Contrary to normally quiet years during a transition of leadership, to some extent in reaction to some elements of an “Asian Spring” in the region, China takes further steps in response to a more activist populace upset with corruption, the environment, and some areas of economic stress. Externally, this includes significant acquisitions in other countries as well as the opening of manufacturing and service facilities where there is a receptive government. At home, R&D is accelerated, particularly in alternative energy, space and IT processing. Subsidies for hydrocarbons are reduced and an explicit carbon tax is put in place.
  2. As the US economy grows, corporations find qualified hires difficult to come by.  Enlightened corporations become educational institutions to provide skills and basic knowledge to a work force that has been idle and undereducated by the public systems. Corporations become much more vocal about creating paths to bring more immigrants into the US system, expanding visa programs and finding other mechanisms to add talented labor to the domestic pool. The tide shifts significantly on immigration issues. The skill match is aggravated by decisions on the part of some US corporations to bring business operations back into the States. Labor costs are rising elsewhere and the elements of control, rule of law, productivity, available feedstock and relative safety lead to better economics for manufacturing and service operations.
  3. Moore’s Law, driven primarily by Intel driving down the nanometer scale and introducing other innovations,  continues to march on. The use of Big Data becomes ubiquitous. This produces technological advances that enhance the opportunities in health care, manufacturing, extractive industries, media and services beyond even the imagination of some of the best speculative fiction writers. These advances, on balance, are positive but continue to raise concerns about the environment and quality of life and opportunity for those at the lower end of the economic and educational spectrum.
  4. Breakthroughs in stem cell research particularly led by work coming out of the New York Stem Cell Foundation change the nature of disease management and eradication and move general therapeutic advances away from animal models to direct testing on human cells. Targeted therapeutics driven by DNA analyses tied to narrower classes of patient recipients change the nature of drug and health delivery. It becomes apparent that the US FDA model is slowing the pace of US therapeutics development by the cost and time required to bring solutions to market. Much as financial services regulation was geared to the benefit of larger entities, it becomes clear that therapeutics development has been on the the same path. Change occurs in response to other countries moving more rapidly in bringing solutions to market.
  5. Away from continual ups and downs in financial assets as the world works its way through the hangover from the 2008-2012 financial crises, the general march of human progress is positive. I hope to be around to observe it. Maybe the breakthroughs suggested in the previous expectation will help that.

What is the Big Deal about Big Data?

I have always been fascinated by data and how it could be used to run a business, create investment opportunities and understand and affect behavior. As I was getting my engineering degree in the ’60’s, I was exposed to the value of historical data and the use of algorithms to reach conclusions under uncertainty. My first job out of the Colorado School of Mines was, strangely, with Procter & Gamble. P&G was a big user of data in its product development, marketing and manufacturing operations. After business school, I was fortunate enough to go to work for a research boutique, Mitchell Hutchins, that was prepared to take full advantage of the early big data manipulation capabilities coming from dumb terminals connected through time sharing to large computers. Mitchell Hutchins was involved in the creation of Data Resources, an economic forecasting company co-founded by the late Otto Eckstein. Data Resources created very sophisticated forecasting models that could either be accepted or manipulated by its clients over these early networks. We also used the network to create individual company models and valuation models which became a regular part of reports to clients. The reports themselves were printed and distributed through the US mail or some early private delivery services. I must say the models that were developed through all this data manipulation were seductive and created an air of certainty in the conclusions that we reached. The higher the correlation coefficients and the R-squared, the more we believed. I am not sure if our forecasts, or those of Data Resources were that much better than others created through less sophisticated means. To Otto’s credit, while publishing his models, he remained a skeptic of the end results. “Add factors” were always a part of his forecasts in discussions with clients. I think we all ultimately learned to respect the power of data, but, at the same time, recognized that the models were only as good as the inputs we were using, and what we didn’t know or measure was as important or more important than what we knew. I think that applies even more so today as the available data expand. We will get some answers that we didn’t have before, but let’s all remain skeptics and avoid the seduction of the certainty associated with the size of input, the sophistication of the models, and the speed with which we get the answers.

So let’s explore Big Data.

According to E-Bay the volume of business data is doubling every 1.2 years. The amount of data Big Science is accumulating dwarfs the business community. Several “laws” have come into play producing these enormous amounts of data and getting everyone excited about what can be done with Big Data if analyzed and manipulated properly.
The Harvard Business Review devoted much of its October, 2012, issue to Big Data. McKinsey and others have published numerous reports on the topic. This is all with the belief that applying the proper analytics to all this data can lead to better business decisions replacing or reinforcing intuition with hard facts coming from more complete and precise information. It all starts with the latest version of Moore’s Law: Processing speeds double every 18 months. This is without question the most important law related to Big Data. In my view, particularly these days, the utility of data is inversely proportional the amount of time it takes to process the data. Wirth’s law comes into play here: Software is getting slower more rapidly than hardware becomes faster. Other more assertive variations have been put forth: May’s law (sometimes facetiously called Gates’ Law): Software efficiency halves every 18 months offsetting Moore’s Law. The impact and perceived importance of processing the Big Data coming at us will very likely put even more of a premium on efficient software. For most applications, developers have had it easy. Processing speeds have allowed for the development of lazy code. One would hope that the exigencies of data growth change that. Otherwise value creation will lag and negate some of the other laws at work here. Metcalfe’s Law: The value of a network is proportional to the square of the number of connected users to the network (~n squared)–or probably more appropriate in today’s social media world, Reed’s Law: The utility of a large network can scale exponentially with the size of the network (~2 to the nth power). The real value or utility becomes, in most instances–certainly in the social media world–the near instantaneous analysis producing an economic action.

It has become a given that the proper use of data, i.e., metrics, can actually allow one to make better judgements and business decisions. Proper and selective use of the data becomes the key. Within a business what one measures can also affect how those generating the data behave. It should be apparent that it becomes important what one measures and how whatever data are collected are used. There is a variation of another law at work here, Parkinson’s Law. In its original: Work expands to fill the time available or in its computer corollary: Data expands to fill the space available for storage. With networks expanding, processing speeds increasing and the cloud and more powerful servers ultimately providing infinite storage, consultants and business school professors have discovered Big Data. In my view, this is creating another variant of Parkinson’s Law: The number of conclusions one can reach expands proportionately with the quantity of data available and inversely with the time it takes to analyze the data. All of those conclusions may be actionable. That doesn’t mean they will have a positive effect. It also doesn’t mean we shouldn’t seek these answers. It is not even a question of “should.” These answers will be sought.

There will be an advantage to those who are the early users of Big Data. This certainly proved to be the case in the investment and trading community. Every day an enormous amount of data are generated on stock price movements, trading volume, business results, economic results and, of course, the opinions of the pundits in the media and in the research departments of a wide variety of financial services entities. Models have been built and continue to be built and modified that attempt to show correlations among securities and deviations from those correlations. The low cost of trading combined with the speed at which a transaction can occur has allowed traders to take advantage of minute variations in highly correlated securities. Those who have created the better models and/or can react more quickly to a variation have done quite well. The importance of speed of reaction has been such that some traders moved their processing closer to the source of the information and the trading shortening the time it takes for electrons to activate and produce a transaction. It is a business where minute fractions of a second can make the difference. The models, though, have to keep morphing in terms of inputs and speed to stay ahead of the competition. Otherwise they all converge eliminating the disparities that produce profits. The Fallacy of Composition comes into play: When everyone stands up to see, no one can see. I think this is already happening in the trading community.

In the long run this will likely happen in other communities as well. We see aspects of this in consumer product marketing. This community has always been good at analyzing the data available to it to discern what customers want or can be made to want. This has led to a wide array of similar products from various companies with little distinction among them. The first movers always had an advantage for a brief period, but ultimately, others developed competitive products. Youngme Moon describes these phenomena well in her wonderful book “Different: Escaping the Competitive Herd.”  What she describes has broad application beyond the marketing community she uses as her examples.

There is a Big Deal about Big Data. The advances that can be made in science, business and, in particular, in the social media world are very exciting–a little scary, but most exciting things are. The early users will have an advantage–maybe a sustainable one as they learn what they still don’t know and adjust accordingly. It is important to understand that the outcomes will only be as good as the inputs and the analytics applied to them. To the extent one comes to rely on these outcomes without understanding what remains unknown, it increases the risks of larger and larger unintended consequences through error or just faulty or incomplete models. The models will always be incomplete. The more we accept that premise the more value our use of Big Data will have. It is hard to imagine the outcomes every time processing speeds and data accumulation double. We are on our way to a more superlative adjective replacing Big. Hang on!