Business Analytics: Questions for Enterprise

Firms today are increasingly seeking competitive advantage through advances in business analytics and decision support systems. According to a white paper published by nGenera (2008) in collaboration with Prof Thomas Davenport:

The next wave of business reengineering is being powered by business analytics, and the potential performance breakthroughs are just as large as they were 15 or so years ago. Many of these breakthroughs will come through the ability to integrate the demand side of the house with the supply side of the house as never before. Even information-rich industries have tended to concentrate on one side or the other. With the power of business analytics, corporations can make and manage the demand-supply connections – a big step closer to the goal of optimizing the performance of the corporation as a whole.

Here are six topical questions (with supporting questions) posed by nGenera for companies seeking to compete analytically:

1. Where should we leverage business analytics?

• What is our distinctive capability? On what basis do we choose to compete? And how clear and definitive are we about that choice?
• What performance levels or innovations in this area would blow away the competition?
• What information, knowledge, and insight would it take to perform that way? What are the biggest unanswered questions and biggest opportunities?
• How would we act upon that information, knowledge, and insights.

2. Why now?

• What are our direct competitors doing or attempting with business analytics? Is anyone in our industry jumping ahead in terms of analytical capability?
• How are analytics changing our competitive landscape? Are we at risk from non-traditional competitors who may use analytics to encroach on our markets?
• What emerging technologies of information integration and analysis should we be exploring more aggressively?
• How fast can we launch a serious business analytics initiative? What’s holding us back?

3. What's the payoff?

• What are our specific performance goals in the area where we choose to compete?
• How well do we measure them? How might better measurement and analysis of today’s performance reveal tomorrow’s opportunities?
• How well aligned are the organization, its management, and its stakeholders with these performance goals?
• What’s our highest ambition? What would it mean in terms of revenue, profit, and market share if we were really to change the basis of competition?

4. What information and technology do we need?

• Is the information we need at hand? Is the data that support our distinctive capability in one repository, with common definitions of key data elements?
• Is this data integrated enough not only to be accessible, but also to be manipulated with analytical tools?
• How completely and accurately does the information measure and represent our distinctive business capability and basis of competition? Is it up-to-date? What are the most glaring gaps and shortfalls?
• Do we have the technologies in place to support business analytics in this area? Or is technology fragmentation holding us back?

5. What kinds of people do we need?

• Do we have a critical mass of analytical professionals on staff? Are we prepared to hire them? Do we need to “rent” this talent in the short term to fill gaps?
• Who can manage analytical professionals? Who has the necessary experience, credibility, and “bridging” skills?
• Will we be ready to train employees to apply the analytical results and operate differently?
• Is the organization at large oriented toward analytical decision-making, or is it wedded to yesterday’s procedures and rules of thumb? How quickly can the organization come up to speed analytically?

6. What roles must senior executives play?

• Are we committed to competing on analytics, starting at the top of the organization? What are the CEO and executive team doing to demonstrate that commitment?
• Is the leader of the analytical function prepared to act upon the results of the analyses? Are the roles and decision rights of other stakeholders, including the CFO and CIO, clear – especially when their roles are novel or overlap?
• Do we have a project leader who can span the worlds of strategy, process performance, and analytics?

Reference: Business Analytics: Six Questions To Ask About Information And Competition (2008), Austin, TX: nGenera Corp.

Higher Education, Experience, and Skills

One of the questions I regularly pose to my economics students is whether a college education is a contributor to productivity. Intriguingly, what I get in response often parallels the views of Greg Ip of the Wall Street Journal (2008, “The Declining Value Of Your College Degree”), who argues “college-educated workers are more plentiful, more commoditized and more subject to the downsizings that used to be the purview of blue-collar workers only. What employers want from workers nowadays is more narrow, more abstract and less easily learned in college.” In essence, the value of a college degree is said to be “not what it was.”

Nonetheless, a college education remains an important predictor of productivity (and earnings) in the marketplace. Even Ip concedes that “the average American with a college diploma still earns about 75% more than a worker with a high-school diploma and is less likely to be unemployed.” Given these facts, how can it be that students are so vexed by the value proposition of their education?

I have previously written about our new economy, the need for good people in our society, as well as the importance of polyvalence in the workplace. I argue here that there exists today a crying need for people with versatile skills sets in what is a transforming global economy and that the university offers the best opportunity to acquire the knowledge required to succeed in this environment.

This leads to the next contention I often hear from students, that “experience counts more than education?” My response to this view is, “not really,” because what is paramount is neither experience nor education, but skills, and if one is complacent in acquiring new skills experientially, embedded knowledge eventually becomes obsolete. The good news is that experience often translates into new more refined skills. The bad news is that breadth and depth of knowledge are not assured through experiential learning. Hence, attention is required to ensure one’s career track includes a multiplicity of experiences in organization and management across a diversity of functions, and eventually industries. In my view, the university offers the most efficient place to acquire breadth and depth of knowledge, as opposed to the workplace, where resources and opportunities for the same are typically limited. Experience that does not result in additional skills has little value in the workplace.

Finally, there is the matter of college graduates who are anomalously incompetent. This happens, and when it does, these people are released for cause. Those who finish a college degree program while somehow avoiding skill acquisition, do so at their peril. Acquiring new skills is the responsibility of all workers who wish to enhance or expand their careers, but especially college graduates. To attend a college or university and not acquire new knowledge along the way is a bewildering and perplexing outcome that defies good judgment.

Concluding, a college education is the most reliable track to improving skills, productivity, and earnings. And while experience can certainly result in new skills, the typical worker will find acquiring the breadth and depth of knowledge necessary to enhance or expand a career through experience alone, challenging. A college degree is still the most profound symbol of embedded knowledge available in our society today.

What's in Your Wallet...?

Some say that the discipline of philosophy has failed society with regards to two incisive questions of importance in our times. The first is, What is power? The second is, What is money? The second question has been occupying my time most recently.

So, what is money, really...? To find out, the first thing I did was take a closer look at the money in my wallet, after which I concluded that money looks about like money as always looked throughout my life (though I have noticed that money now includes various security measures intended to thwart counterfeiting).

I then went to the Internet to learn more about what money is. Since I had mostly $10 "bills" in my wallet, I focused my searches on the history of the $10 bill in America. Eventually, I found several images of different $10 bills that have been issued in the US and realized that none of the images were "bills" at all. Click on the images below and see for yourself...

The first image is a so-called "Federal Reserve Note," which Americans today use on a more or less daily basis. The second image is an older "Silver Certificate," which is no longer in circulation. The third is a still older "Gold Certificate," also no longer in circulation. Now that you have carefully looked over each, ask yourself:

Which currency would you prefer to carry in your wallet...?

My preference would be the Gold Certificate, thank you very much. As for the question of what money is...

Related Posts:

In Fiat Currency We Trust

All the Gold at Fort Knox

The Power of Story - The Story Paradigm

by Tom Atlee © Co-Intelligence.org

In the field of co-intelligence, stories are more than dramas people tell or read. Story, as a pattern, is a powerful way of organizing and sharing individual experience and exploring and co-creating shared realties. It forms one of the underlying structures of reality, comprehensible and responsive to those who possess what we call narrative intelligence. Our psyches and cultures are filled with narrative fields of influence, or story fields, which shape the awareness and behavior of the individuals and collectives associated with them.

Story-reality is the reality that we see when we recognize that every person, every being, every thing has a story and contains stories -- and, in fact, is a story -- and that all of these stories interconnect, that we are, in fact, surrounded by stories, embedded in stories and made of stories. When poet Murial Rukeyser tells us "the universe is made of stories, not atoms," she's describing story-reality. Ultimately, story-reality includes any and all actual events and realities, but experienced as stories, not as the more usual patterns -- objects-and-actions; matter, energy, space, time; patterns of probability; etc. Story-reality is made up of lived stories.

Lived stories are those real-life, actual stories that are happening in the real world all around us all the time. The actual unfolding events relating to any one actual entity or subject comprise that entity's or subject's lived story. Everything that exists has, embodies and participates in many lived stories. The way to co-intelligently engage in story-reality is to become sensitive to lived stories... to learn about the lived stories of people, places, things... to share our own lived stories... to discover how all these stories intersect, who or what is in the foreground and background of each other's lived stories. Ultimately, this provides the guidance we need to find our own most meaningful place in the universal story.

While analysis is good for control and prediction, story-sensibility is good for understanding meaning and role. [italics added]

Narrative intelligence is the ability (or tendency) to perceive, know, think, feel, explain one's experience and influence reality through the use of stories and narrative forms.

It includes:

• the ability and tendency to organize experience and ideas using stories and narrative patterns (an excellent example of this is the use of myth, which defines and discusses concepts -- such as archetypes -- in narrative form)
• the tendency to understand things better when they are presented in the form of a story (and sometimes to have trouble understanding things when they aren't presented as stories)
• the capacity to sense the importance of context, character, history, etc., in any explanation -- and dissatisfaction when these are omitted
• dissatisfaction with isolated events and abstract ideas, out of context
• an ability to sense or imagine the stories of people, objects, places; the ability to accurately guess where something (or someone) comes from, what has happened to it, where it is going, what it means
• curiosity about the stories behind things, and an ability to investigate such stories
• a tendency to make up stories, plausible or fantastic, to illustrate a point
• the ability to maintain a repertoire of stories (real and imaginary) to convey meanings; the ability to access that repertoire
• the ability to sort out and describe what has happened to oneself or others, often with a richness of context and detail, and often with great relish
• the ability to place and remember events in sequence
• the ability to envision chains and webs of causation
• the tendency to build scenarios (stories of possibilities); an ability to plan and think strategically
• a love of stories
• the ability and tendency to see people, places and things in terms of their function in a story (very helpful for novelists picking up tidbits from the lives around them for use in their creative work)
• resonance with the stories of others; the ability to see another's viewpoint when presented with the stories which underlie or embody that viewpoint
• the ability to discover themes in the events of a life or story
• the ability to recognize (or select) certain elements as significant, as embodying certain meanings that "make sense of things"
• the ability to build a story out of randomly-selected items
• the ability to use stories as memory-enhancing devices (such as remembering a phone number by making the digits into characters and weaving them into a story).

Story fields are fields of influence or patterns of dynamic potential that permeate psycho-social space and influence the lives of those connected to them. They are made up of many mutually-reinforcing stories (myths, news, soap operas, lives, memories) and story-like phenomena (roles, metaphors, archetypes, images). A story field paints a particular picture of how life is or should be, and shapes the life within its range into its image.

The American Way of Life is a powerful story field, which includes everything from principles like freedom and the pursuit of happiness, to stories of cowboys and rags-to-riches heroes, to metaphors like the melting pot and the safety net, to images like the Statue of Liberty and the flag. It is communicated by movies, men in business suits, advertisements, college catalogues, and mall displays -- among many, many other things. It takes immense effort to resist or change it. Anyone or anything which doesn't live within this story-sea and move with its currents doesn't seem quite American.

Psychological, organizational or social transformation is usually preceded or accompanied by a change in the story field governing that system. It is therefore usually non-productive to try to change forms and habits without changing the story fields that hold them in place. Once the story field is changed, subsidiary patterns tend to realign rapidly. (This process is part of what has been called a paradigm shift.)

Co-intelligent cultural transformation necessarily includes the co-generation of co-intelligent story fields. This would include examples of co-intelligence in action, visions of how things could be more co-intelligent, biographies of co-intelligent people, fiction illustrating the dynamics of co-intelligence, co-intelligent myths and poems, academic reframing of numerous other subjects in terms of co-intelligence, people actually living co-intelligently, the clarification and use of special roles (like elder and partner) associated with co-intelligence, etc.

Reproduced with permission of Co-Intelligence.org

IT Implementation Failures

An alarming number of information technology (IT) projects fail. The oft-cited statistic is that greater than half of all IT implementations are out of variance with one or more critical requirements or specifications.

Of particular concern is that value-added resellers dressed up as "independent" consultants are frequently behind these failures. The troubling pattern is that the problem or opportunity to be solved is never validated by hard evidence. Hence, the client buys the "wrong" solution, while the "real" problems and opportunities remain out of scope. To be fair, not all value-added resellers focus on aligning specific IT solutions with any and every problem. Other problem-solving approaches such as evidence-based management and consulting are gaining traction in response. In the mean time, more and more firms are seeking some "fix" for a failed or failing IT initiative.

How do you know when your IT project is "going bad?" Here are some warning signs offered by CIO Magazine (2007):

• Project team lacks substantial buy-in and interest in the project’s success
• Poor communication between stakeholders and project team members
• Few interim deliverables, so tangible progress is not demonstrated
• Bad news isn’t allowed to be shared, meaning denial is pervasive
• Project team works lots of overtime, suggesting the schedule is slipping
• Project resources are frequently diverted to other activities
• Interim milestones are often missedReducing project scope is viewed as an acceptable means to meet budget and schedule requirements

ESL International (2006) offers this list of flags:

• No one has a firm idea of when the project will be finished and most people have given up trying to guess
• The product is laden with defects
• Team members are working excessive hours—20 or more hours per week of involuntary overtime
• Management has lost its ability to control progress or even to ascertain the project’s status with any accuracy
• The customer has lost confidence that the project team will ever deliver the promised goods
• The team is defensive about its progress
• Relations between project team members are strained
• The project is on the verge of cancellation
• The morale of the project team has hit rock bottom
• The customer is threatening legal action

I recently had a career IT professional say to me that most of the IT projects he had been involved with over the years were, in his words, "experimental." The remark stunned me, but perhaps explains why so many IT implementations fail. Many IT professionals started their careers as programmers, network administrators, and installation specialists. In each of these roles, "trial and error" problem-solving techniques prevail, so it is not surprising to see the same cohort of IT experts continuing to rely on these same methods. What is changing however is that IT projects now undergo continuous scrutiny for return on investment, and many IT professionals are unprepared for this reality by either training or temperament.

If your IT project appears to be in one or more of the states described above, it may be time to bring in another set of eyes to see just what is going on, and perhaps consider an entirely different approach to your firm’s needs.

How to Save the Euro? Lessons from the US

by Jacques Melitz © VoxEU.org

Earlier this year, the fiscal situation in Greece caused turmoil across Europe. This column examines why the financial difficulties of several state governments in the US are not having similar impacts on its economy.

The problems of the Eurozone this year brought to light some failures of the system. Nevertheless, the resulting drop in confidence in the system has gone further than we might have expected. Questions have even arisen about the survival of the system (see Baldwin 2010 and Blejer and Levy-Yeyati 2010 for discussions). Yet monetary systems do not tend to dissolve simply because of faulty performance. On the contrary, as a rule they endure even when they function very badly. It takes a political force majeure to bring about the break-up of a single currency area, typically without connection to monetary performance. Why, then, has the possible default of a country engaged in irresponsible fiscal policy and accounting for only 3% of the Eurozone’s GDP raised questions about ‘saving the euro’ and the survival of the Eurozone?

The issue has not received the attention it deserves. It is often simply taken for granted that the departures from the Stability and Growth Pact provide a sufficient reason for the earthquake that has shaken the whole currency area. Yet if we look around the world past and present, the mismanagement of finances by regional governments has no particular tendency to bring down entire monetary systems, far from it. In line with the usual – I think superficial – diagnosis of the ailment, proposed remedies for the Eurozone centre on strengthening the Pact, increasing joint political control over fiscal policy, and providing joint insurance against government default, or some mixture of the three. But what if a vital element of the problem is really the official doctrine that sovereign default is incompatible with the euro? What if the scale of the crisis that took place this year has resulted from financial markets’ conviction, based on this doctrine, that the future of the euro was at stake? What if assuring the long-run sustainability of the euro means convincing those markets, quite differently, that nothing as manageable as a Greek default can upset the Eurozone?

Lessons from the US

That is precisely what the US example would suggest and what I will defend. With this idea governing beliefs, the right road ahead looks quite different. It means shifting the emphasis away from avoiding government defaults toward assuring the stability and the solvency of the banking system at all times, regardless of the financial difficulties of some member governments.

In the US, default on state and municipal contractual obligations is very much a possibility whenever lower-level governments are in financial trouble; bailout cannot be taken for granted. New York City defaulted in 1975, the biggest default of all by a lower-level government unit since World War II took place in 1983 when the Washington Public Power Supply System went into bankruptcy, and Orange County defaulted in 1995. Various municipal governments have been on the verge of default at times in the last few decades, including Philadelphia and Cleveland. There is also no Stability and Growth Pact in the US. Yet financial discipline is considerably higher in the US at the state government level than in the Eurozone at the national level. All states except Vermont have balanced-budget rules; but these rules are self-imposed. It is easy to argue that this difference in fiscal discipline on the two sides of the Atlantic is related to the fact that when push comes to shove in the US and a lower-level government unit cannot or will not meet its debt obligations, the lenders can expect to take a big part of the hit.

Some rudimentary analysis is relevant. Consider any government unit unable to print money and without any prospect of a bailout. Theory tells us that credit rationing is very much a possibility. As the interest rate that such a government offers on its debt goes up, extra lending dries up completely at some point as the expected rate of return on the government debt falls. This must happen because higher nominal interest rates impair the government’s solvability and bring default nearer. Risk aversion simply lowers the interest rate at which credit rationing begins.

Suppose we compare the situation in the US and the Eurozone since the 2007-2009 financial crisis in this light. The crisis brought about dire financing problems for many lower-level government units in the US and some national governments in the Eurozone. According to the spreads on credit default swaps, California and Illinois now have a higher probability of non-performance on public debt than Portugal and Spain. This has been true for months. Consider next the difference in response in the States and Europe. Recently Illinois simply stopped paying $5 billion of bills. In June of last year California issued vouchers for wage payments. In addition, savage cuts in public services have begun and are now threatened in various states in difficulty, not only these two. Nevada has made startling reductions in spending on higher education and welfare. In the case of Portugal and Spain, nothing so drastic has happened thus far. There have been occasional spikes in interest rate spreads over German bunds of 100 to 200 percentage-points above usual levels. Both Spanish and Portuguese governments have also been forced to plan greater austerity and reduced government deficit spending. Meanwhile, they have been able and willing to keep borrowing.

Why the difference between the Eurozone and the US?

Part of the explanation may be that Portugal and Spain are more able to raise tax revenues than US states. But another part is the higher probability of a bailout in Europe. The example of Greece is to the point. Greece has been able to continue borrowing this year at interest rates typically around 200 percentage-points above Portugal and Spain on 10-year government bonds (and since May more than 500 percentage-points higher than German bunds). If you do the math, it is clear that this could never have happened without a high probability of a bailout. In fact, you do not need to do the math: there have been occasions in February/March and particularly May when some Greek issues would clearly have failed without the assurance of public lending and ECB support. If Greece can borrow on the probability of a bailout, so could Portugal and Spain.

Based on this evidence, the current Eurozone strategy of treating government default as anathema permits member governments to sink into deeper waters, weakens the forces that would otherwise exist toward self-imposed budget restraints, and thereby raises the probability of a bailout. But an actual bailout is perhaps the most likely setting for the breakdown of the Eurozone. If taxes ever need to rise all over the Eurozone in order to bail out a member government, one can easily imagine a pullout by Germany, followed by the Netherlands and Austria (if no others), in order to form a separate monetary union.[1]

What are the dangers of the opposite strategy of mimicking the US instead and moving toward heavier reliance on markets to discipline member governments and to price sovereign risk? The answer lies in the external effects of government default on the payment system and the banks, and this problem would be aggravated by contagion. But those dangers exist in the US as well. If the US federal government were to allow Illinois or California to default on state government debt in today’s circumstances of widespread financial difficulties across the states, there is a serious threat that interest premia would go up on the debt of most state governments and a wave of state defaults would follow. For this reason, the federal government might well step in. But if we look at the institutional manner in which the US deals with the problem, we find the answer to lie in country-wide prudential rules for banks and central bank powers of lender of last resort. There is no general announcement that state government default is incompatible with the dollar. Instead there is a strict separation of the issue of joint support of the financial system and joint support of financing by the sub-government units in the country. Would Europe not be wise to adopt the same strategy and to cease to conflate the two issues?

Tweaking the Pact

What this would mean, of course, is adopting Eurozone-wide prudential rules on banks, providing the ECB full powers of lender of last resort, and, very significantly, dismissing the idea that the Stability and Growth Pact is the pillar on which the whole Eurozone project stands. This idea is highly perilous.[2] Markets believe it, and at times of financial precariousness, what markets believe is extremely important. According to my proposal, the Pact could still be upheld as a code of good behaviour which improves public finances in Europe and facilitates the task of the ECB. But the basic philosophy would be that if any individual member government in the Eurozone engages in irresponsible fiscal conduct, contrary to the Pact, the creditors and its taxpayers would bear the brunt of the consequences. Everything would be done to assure the stability of the financial sector in the Eurozone and the lack of repercussions on the risk premiums that the more financially responsible member governments need to pay. Banks might be bailed out but not governments. Any aid to member governments, if it came, would not concern the euro system but the IMF or if any aid did come from the EU it would be part of a programme that could as well have existed had the euro never appeared and would be clearly sealed off.

VoxEU Editors' note: This article will appear as a roundtable discussion in Miroslav Beblavy, David Cobham and Ludovit Odor, eds., The euro area and the financial crisis, Cambridge University Press, forthcoming.

References:

Baldwin, R (2010), A re-cap of Vox columns on the Eurozone crisis” VoxEU.org, 13 May.

Blejer, M and Levy-Yeyati, E (2010), Leaving the euro: What’s in the box, VoxEU.org, 21 July.

Economist (2010), Can pay, won’t pay, June 19.

Poterba, J (1996), Do budget rules work? NBER Working Paper 5550, April.

Public Bonds (2010), Municipal bonds and defaults, downloaded 23 August.

Reinhart, C M and Rogoff, K (2009), This time is different: eight centuries of financial folly, Princeton: Princeton University Press.

Sinn, H-W (2010), Rescuing Europe, CESifo Forum, special issue, August.

Notes:

[1] Many would say that Greece has already been bailed out. But so far no holder of Greek debt has yet suffered a credit event. Further, no one outside of Greece has yet paid any taxes to fulfil a claim on Greek debt. Thus, according to my usage, no bailout has happened. However, none of the argument hinges on this choice of words.

[2] If we really think that a government default would bring the euro under, we must conclude that the euro has no long-run future ahead – that it is doomed. A reading of Reinhart and Rogoff (2009) should convince anyone.

Republished with permission of VoxEU.org

The Nordics in the Global Crisis

by Thorvaldur Gylfason, Bengt Holmstrom, Sixten Korkman, Hans Tson Söderström, and Vesa Vihriälä © VoxEU.org

Is the Nordic model an asset or a liability? The global crisis has seen GDP in the region decline by between 4.5% and 7%. This column argues that the Nordic model, with its welfare state and high rate of investment in human capital, can, properly implemented, be part of the solution.

The Nordic countries – Denmark, Finland, Iceland, Norway and Sweden – are champions of free trade and open markets. And for a good reason; they see international specialisation within a global framework as a means of raising productivity and income. Much of the Nordic socio-economic model can be interpreted as aiming at collective risk sharing with a view to fostering acceptance of open markets, new technologies and the need for change.

In a broad sense the model includes a set of labour market organisations, with an important role for negotiations, a comprehensive safety net and a high rate of publicly supported investment in human capital. Embracing globalisation and sharing risk are mutually reinforcing planks of the Nordic Model, as discussed in Andersen et al. (2007).

Is the Nordic model an asset or a liability?

The current crisis is not only a financial and economic crisis but also a crisis of the much heralded globalisation process itself. It therefore raises many key questions for small open economies, not least the Nordics. What are the lessons of the crisis for economic policies? Is the future of the global economy more unstable than the past, and does that perspective call for a fundamental review of the economic policy strategy? Is the Nordic model an asset or a liability in the light of the crisis?

As we discuss in our new report (Gylfason et al. 2010), the crisis is best seen as the outcome of a lopsided globalisation process that overwhelmed the global financial system. The global savings glut was largely absorbed by the US shadow banking system, because of its capacity to create innovative products. It seemed to offer safe investment outlets at attractive rates of return, while at the same time encouraging excessive leverage. Once the bubble burst and the world economy went down, the Nordics, with their high dependence on exports of investment goods and consumer durables, were particularly hard hit (with the exception of oil-rich Norway). Their GDP in 2009 declined at rates between 4.5% and 7%.

While the sharpness of the global downturn was a surprise, so was the early stabilisation, which started around the middle of 2009. At this point the recession has been declared over in many countries and a recovery – though weak and hesitant – seems to be underway. There is little doubt of the explanation; policies matter. Authorities have demonstrated an unprecedented activism in monetary and fiscal policy as well as inventiveness in financial crisis management. While the world escaped a repetition of the Great Depression, the crisis has nevertheless left a legacy of difficult issues and challenges.

The Nordics are vulnerable but also resilient. While Iceland is a case of its own, we believe that the Nordics have the capacity to recover and to continue combining economic efficiency with high social ambitions. Sweden and Finland experienced a severe banking crisis in the early 1990s, thereby learning a lot about the need for better financial regulation and supervision.

Lessons were learnt about the need for a solid crisis management framework, the pros and cons of a blanket government guarantee for financial institutions, the need for precautionary and other capital injections, the problems of transferring assets into “bad banks”, and the case for not shying away from the government taking over institutions in certain circumstances. Many of these lessons were useful in avoiding mistakes in this crisis and they have attracted interest in other countries. This is also why companies and banks in the region have had balance sheets strong enough to weather the crisis pretty well (we obviously leave out Iceland again).

The effect of the euro

The Nordics have all had different monetary regimes since the euro. Given their similarity in other respects, a comparison of Finland and Sweden is especially interesting. It is almost a laboratory experiment. Sweden has a floating exchange rate and an independent central bank geared to price stability, while Finland is part of the Eurozone. Who has made the better choice?

The krona was mostly stable and developments in Finland and Sweden were strikingly similar during the first decade of the euro. Once the crisis erupted, however, the krona fell significantly relative to the euro, thereby strengthening the price competitiveness of Sweden relative to Finland and the euro area. One might expect this to help Sweden come through the crisis at less cost than Finland, arguably benefitting at the expense of its neighbour by capturing market shares.

The decline in exports and output in 2009 was indeed smaller in Sweden than in Finland, and GDP growth is forecast to be somewhat faster. However, the differences do not seem large. Also, manufacturing output shows little response to the change in competitiveness, and unemployment is rising in parallel with developments in Finland.

Either the effects of the improved competitiveness are relatively modest or the lags are long, or a depreciation of a floating currency has less effect on export and output volumes than a devaluation of a pegged currency used to have. What is clear is that the floating exchange rate does not insulate an economy from external shocks, and the economic differences between the two exchange rate regimes seem smaller than often claimed in the heated debate about the Eurozone.

An effective fiscal stimulus?

For small and open economies, in particular, one may question the power of fiscal expansion as an instrument of demand management. Nevertheless, we still find that an expansionary fiscal policy is helpful in a crisis. It is a useful complement to monetary policy when the interest rate hits its lower bound or when the credit system becomes dysfunctional. Also, fiscal action may alleviate particular problems such as long-term or youth unemployment. Furthermore, automatic fiscal stabilisers allow the government to avoid hasty and unduly harmful measures. The social contract is very valuable in a crisis as it tempers the panic and gives the government more time to plan and undertake measures to reignite growth in an orderly manner.

Of course, sustainable public finances need to be restored, and it is useful to consider the merits of alternative means of fiscal consolidation. Public expenditure may be cut or its composition twisted in a growth-friendly direction, and efficiency in the provision of public services improved. The tax base may be broadened by measures to raise the employment rate, particularly by prolonging the length of working careers. There is some scope for changing the structure of taxation with a view to encouraging economic growth, notably by reducing the share of taxes that fall directly on corporate profits and wage income.

The Nordics were in a position to pursue fiscal expansion in the crisis because these countries were – in contrast to the US and almost all other EU countries –running sizeable budget surpluses in the preceding decade. The government debt level of the Nordics is only half of what it is in the OECD on average and they continue to borrow at very favourable terms. Given their track record, there is reason to believe that the Nordics will continue to be countries with relatively sound public finances, retaining the scope for fiscal policy to be used when needed.

Safety in numbers

Most importantly, the Nordic model itself contributes to resilience. The comprehensive safety net, one of the attributes of the Nordic model, has proved to be robust also in times of crisis. The entitlements are not tied to the fate of individual companies or particular markets, and risks are widely shared in the society. While forest plants are shutting down in Finland and car manufacturing is sharply contracting in Sweden, the governments are firmly rejecting requests for support of ailing industries. Still, there are no crowds protesting in the streets, largely because flexible work arrangements, based both on general and company-specific agreements between businesses and labour, alleviate a rise in unemployment. Structural change is enhanced by the employment protection legislation, which is more liberal than in most other EU countries. A well-educated labour force, another of the attributes of the Nordic model, facilitates adjustment by making it easier to upgrade skills through additional training.

Provided that governments continue to be able to take the decisions needed to safeguard competitiveness and the sustainability of public finances, the Nordic model can be both robust and resilient. The Nordic model with its welfare state, labour market institutions and high rate of investment in human capital, is not the source of the current problems. On the contrary, the Nordic model, properly implemented, can be part of the solution.

References

Andersen, T, Holmström, B, Honkapohja, S, Korkman, S, Söderström, H T, and Vartiainen, J (2007), ”The Nordic Model – Embracing Globalisation and Sharing Risks,” The Economic Research Institute of the Finnish Economy, Helsinki: ETLA.

Gylfason, T, Holmström, B, Korkman, S, Söderström, H T, and Vihriälä, V (2010), "Nordics in Global Crisis – Vulnerability and Resilience,” Helsinki: ETLA.

Republished with permission of VoxEU.org

The Evolution of Decision Analysis

by Ronald A Howard © The Stanford Decisions and Ethics Center

Although decision analysis has developed significantly over the last two decades, the basic principles of the field have served well. They are unlikely to change because they are based on simple logic. In the first part of this paper, we summarize the original, fundamental disciplines of decision analysis; in the second part, we show how the discipline has evolved.

PART I: A BRIEF DESCRIPTION OF DECISION ANALYSIS

Making important decisions often requires treating major uncertainty, long time horizons, and complex value issues. To deal with such problems, the discipline of decision analysis was developed. The discipline comprises the philosophy, theory, methodology, and professional practice necessary to formalize the analysis of important decisions.

Overview of Decision Analysis

Decision analysis is the latest step in a sequence of quantitative advances in the operations research/management science field. Specifically, decision analysis results from combining the fields of systems analysis and statistical decision theory. Systems analysis, which grew as a branch of engineering, was good at capturing the interactions and dynamic behavior of complex situations. Statistical decision theory was concerned with logical decisions in simple, uncertain situations. The merger of these concepts creates a methodology for making logical decisions in complex, dynamic, and uncertain situations.

Decision analysis specifies the alternatives, information, and preferences of the decision-maker and then finds the logically implied decision.

Decision-making requires choosing between alternatives, mutually exclusive resource allocations that will produce outcomes of different desirabilities with different likelihoods. While the range of alternatives to be considered is set by the decision-maker, the decision analyst may be able to suggest new alternatives as the analysis progresses.

Since uncertainty is at the heart of most significant decision problems, decision-making requires specifying the amount of uncertainty that exists given available information. Many decision problems become relatively trivial if uncertainty is removed. For example, consider how easily a decision-maker could make a critical decision in launching a new commercial product if he could predict with certainty production and sales costs, price-demand relationships, and governmental decisions. Decision analysis treats uncertainty effectively by encoding informed judgment in the form of probability assignments to events and variables.

Decision-making also requires assigning values on the outcomes of interest to the decision-maker. These outcomes may be as customary as profit or as troubling as pain. Decision analysis determines the decision-maker's trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that are risky or distributed over time.

One of the most basic concepts in decision analysis is the distinction between a good decision and a good outcome. A good decision is a logical decision -- one based on the information, values, and preferences of the decision-maker. A good outcome is one that is profitable, or otherwise highly valued. In short, a good outcome is one that we wish would happen. By making good decisions in all situations that face us, we hope to ensure as high a percentage of good outcomes as possible. We may be disappointed to find that a good decision has produced a bad outcome, or dismayed to learn that someone who has made what we consider to be a bad decision has achieved a good outcome. Short of having a clairvoyant, however, making good decisions is the best way to pursue good outcomes.

An important benefit of decision analysis is that it provides a formal, unequivocal language for communication among the people included in the decision-making process. During the analysis, the basis for a decision becomes evident, not just the decision itself. A disagreement about whether to adopt an alternative may occur because individuals possess different relevant information or because they place different values on the consequences. The formal logic of decision analysis subjects these component elements of the decision process to scrutiny. Information gaps can be uncovered and filled, and differences in values can be openly examined. Revealing the sources of disagreement usually opens the door to cooperative resolution.

The formalism of decision analysis is also valuable for vertical communication in a management hierarchy. The organizational value structure determined by policymakers must be wedded to the detailed information that the line manager, staff analyst, or research worker possesses. By providing a structure for delegating decision-making to lower levels of authority and for synthesizing information from diverse areas for decision-making at high levels, decision analysis accomplishes this union.

Methodology

The application of decision analysis often takes the form of an iterative procedure called the Decision Analysis Cycle (see Figure 1). Although this procedure is not an inviolable method of attacking the problem, it is a means of ensuring that essential steps have been considered.

The procedure is divided into three phases. In the first (deterministic) phase, the variables affecting the decision are defined and related, values are assigned, and the importance of the variables is measured without any consideration of uncertainty.

The second (probabilistic) phase starts with the encoding of probability on the important variables; then, the associated probability assignments on values are derived. This phase also introduces the assessment of risk preference, which defines the best solution in the face of uncertainty.

In the third (informational) phase, the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty in each of the important variables in the problem. In some ways, this is the most important phase because it shows just what it would be worth in dollars and cents to have perfect information. Comparing the value of information with its cost determines whether additional information should be collected.

If there are further profitable sources of information, then the decision should be to gather the information rather than to make the primary decision at this time. The design and execution of the information-gathering program follows.

Since new information generally requires revisions in the original analysis, the original three phases must be performed once more. However, the additional work required to incorporate the modifications is usually slight, and the evaluation, rapid. At the decision point, it may again be profitable to gather new information and repeat the cycle, or it may be more advisable to act. Eventually, the decision to act will be made because the value of new analysis and information-gathering will be less than its cost.

Applying the above procedure ensures that the total effort is responsive to changes in information -- the approach is adaptive. Identifying the crucial areas of uncertainty can also aid in generating new alternatives for future analysis.

Model Sequence

Typically, a decision analysis is performed not with one, but with a sequence of progressively more realistic models. These models generally will be in the form of computer programs. The first model in the sequence is the pilot model, an extremely simplified representation of the problem useful only for determining the most important relationships. Although the pilot model looks very little like the desired final product, it is indispensable in achieving that goal.

The next model in the sequence is the prototype model, a quite detailed representation of the problem that may, however, still be lacking a few important attributes. Although it will generally have objectionable features that must be eliminated, it does demonstrate how the final version will appear and perform.

The final model in the sequence is the production model; it is the most accurate representation of reality that decision analysis can produce. It should function well even though it may retain features that are treated in a less than ideal way.

Starting with the pilot model, sensitivity analyses are used throughout each phase to guide its further evolution. If decisions are insensitive to changes in some aspect of the model, there is no need to model that particular aspect in more detail. The goal of a good modeler is to model in detail only those aspects of the problem that have an impact on the decisions, while keeping the costs of this modeling commensurate with the level of the overall analysis.

Important aids in determining whether further modeling is economically justifiable are the calculations of the value of information. Some variables may be uncertain partially because detailed models have not bee" constructed. If the analyst can calculate the value of perfect information about these variables, he will have a standard to use in comparing the co of any additional modeling. If the cost of modeling is greater than the value of perfect information, the modeling is clearly not economically justifiable.

Using a combination of sensitivity analysis and calculations of the value of information, the analyst continually directs the development of model in an economically efficient way. An analysis conducted in this wa provides not only answers, but also often insights for creating new alternatives. When completed, the model should be able to withstand the test of any good engineering design: additional modeling resources could utilized with equal effectiveness in any part of the model. There is no such thing as a final or complete analysis; there is only an economic analysis given the resources available.

PART II: REFINEMENTS AND NEW DEVELOPMENTS IN DECISION ANALYSIS

Having seen the basic concepts of decision analysis and the main poi of its professional practice, let us now examine some of the evolution changes in the field over the last two decades.

The Decision Basis

It has become useful to have a name for the formal description of a decision problem; we call it the decision basis. The decision basis consists of a quantitative specification of the three elements of the basis: the alternatives, the information, and the preferences of the decision-maker. We can then think of two essential steps in any decision analysis: the development and the evaluation of the decision basis.

Basis Development

To develop the decision basis, the decision analyst must elicit each of the three elements from the decision-maker or from his delegates. For example, in a medical problem, the ultimate decision-maker should be the patient. The patient would provide the element of preference in the basis, probably in a series of interviews with the decision analyst. In most cases, however, the patient will delegate the alternative and information elements to doctors who, in turn, would be interviewed by the decision analyst. The analyst should be able to certify that the decision basis accurately represents the alternatives, information, and preferences provided directly or indirectly by the decision-maker. We should note here that the alternatives must include alternatives of information-gathering, such as tests, experimental programs, surveys, or pilot plants.

One key issue is the extent to which the decision analyst can provide substantive portions of the decision basis by acting as an expert. In many circumstances, the analyst cannot be an expert because he has only a lay knowledge of the decision field. Even when the analyst does have substantial knowledge of the subject area, he should make clear to the decision-maker when he has changed from the role of decision analyst to that substantive expert. Playing the role of expert can also force the analyst to defend his views against those of others; to this extent, he would be less of a "fair witness" in the subsequent analysis. Nevertheless, this possible loss of impartiality and fresh viewpoint must be balanced against the communication advantages of dealing with an analyst familiar with the decision field.

Basis Evaluation

Once the basis is developed, the next step is to evaluate it using the sensitivity analysis and value of information calculations described earlier. However, casting the problem as a decision basis shows that value-of-information calculations, important as they are, focus on only one element of the basis -- information.

Using the concept of the basis, we can also compute the value of a new alternative, which we might call the value of control. Such a calculation might well motivate the search for an alternative with certain characteristics and perhaps even the development of such an alternative.

One can perform a similar sensitivity analysis to preference with the intention not of changing preference, but of ensuring that preferences have been accurately assessed. A large change in value resulting from a small change in preference would indicate the need for more interviews about preference.

A Revised Cycle

Using the concept of the basis, we may wish to restructure the decision analysis cycle in the four-phase form shown in Figure 2. Here, the information gathering that must precede analysis or augment subsequent analyses has been included in a basis development phase. The deterministic and probabilistic phases are essentially unchanged, but the informational phase -- renamed "basis appraisal" -- is expanded to include the examination of all three basis elements.

A Refined Analysis Sequence

As a problem is analyzed, the analysis may progress through the decision analysis cycle several times in increasing levels of detail. The basic distinction is between the pilot and full-scale analysis. The pilot analysis is a simplified, approximate, but comprehensive, analysis of a decision problem. The dictionary defines pilot as "serving as a tentative model for future experiment or development." The full-scale analysis is an increasingly realistic, accurate, and justifiable analysis of a decisicision problem, where full-scale is defined as "employing all resources, not limited or partial." To understand these distinctions, we must explain in more detail what constitutes a pilot or full-scale analysis.

The purpose of a pilot analysis is to provide understanding and establish effective communication about the nature of the decision and the major issues surrounding it. The content of the pilot analysis is a simplified decision model, a tentative preference structure, and a rough characterization of uncertainty. From a pilot analysis, the decision-maker should expect preliminary recommendations for the decision and the analyst should expect guidance in conducting the full-scale analysis.

The purpose of the full-scale analysis is to find the most desirable action, given the fully developed decision basis. The full-scale analysis consists of a balanced and realistic decision model, preferences that have been certified by the decision-maker, and a careful representation of important uncertainties. From the full-scale analysis, the decision-maker should expect a recommended course of action.

While most analyses progress from pilot to full-scale, some are so complex that valuable distinctions may be made between different stages of full-scale analysis.

The first stage of full-scale analysis is the prototypical stage, which is intended to reveal weaknesses and excesses in the full-scale analysis that are worthy of correction. A prototype is defined as "an original type, form, or instance that serves as a model on which later stages are based or judged."

After the indicated corrections have been made, the analyst has an integrated stage of full-scale analysis that provides the decision-maker with confidence in having a unified, balanced, and economic analysis as a basis for decision. To integrate is "to make into a whole by bringing all parts together: unify." If a decision-maker is making a personal decision that will not require the support or approval of others, then the integrated stage of full-scale analysis is all that is required. However, if the decision-maker must convince others of the wisdom of the chosen course of action or even defend that course against hostile elements, then an additional stage of full-scale analysis will be necessary — the defensible stage.

The defensible stage of full-scale analysis is intended to demonstrate to supportive, doubtful, and possibly hostile audiences that the analysis provides an appropriate basis for decision. Defensible means "capable of being defended, protected, or justified." Typically, defensible analyses are necessary for important decisions in the public arena; however, even private enterprises may wish to conduct defensible analyses to win the support of workers, financial institutions, or venture partners. Defensible analyses are very demanding because they must show not only that the basis used is reasonable, but also that other possible bases that would lead to different decisions are not reasonable.

Contributions from Psychological Research

One of the most significant factors influencing the practice of decision analysis in recent years has been new knowledge about cognitive processes from the field of psychology. This research, centering on the contribution of Kahneman and Tversky, has had two major effects. First, the research on cognitive biases [10] has shown the need for subtlety and careful procedure in eliciting the probabilistic judgments on which decision analysis depends. Second, and perhaps even more important the descriptive research on how people actually make decisions [6,11] shows that man is considerably less skilled in decision-making than expected. The main thrust of this research shows that people violate the rules of probabilistic logic in even quite simple settings. When we say that people violate certain rules, we mean that when they are made aware of the implications of their choices, they often wish they had made another choice: that is, they realize they have made a mistake. While these mistakes can be produced in analyzing simple decision settings, they become almost unavoidable when the problem is complex.

These findings may change our interpretation of the logical axioms that are the foundations of decision analysis. We have always considered these axioms as normative: they must be satisfied if our decisions are to have many properties that we would regard as desirable. If a particular individual did not satisfy the axioms, then he would be simply making mistakes in the view of those who followed the axioms. While this interpretation is still possible, a more appropriate way to look at the axioms is that they describe what any person would do if faced with a situation as simple as the one described by the axioms. In other words, the axioms are descriptive of human behavior for simple situations. If, however, the situation becomes more complex, more "opaque" as opposed to "transparent," the axioms are no longer descriptive because the person may unintentionally violate the axiom systems.

We may now think of the job of the decision analyst as that of making "opaque" situations "transparent," so that the person clearly sees what t do. This interpretation of the work may not make it any easier, but it is far more humane than the view that the analyst is trying to impose logic willfully illogical world.

Influence Diagrams

The influence diagram is one of the most useful concepts developed in decision analysis [3]. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The analyst has always faced the problem of how to reduce the multifaceted knowledge in people's heads to a form that could meet the rigid tests of explicitness and consistency required by a computer. The influence diagram is a major aid in this transformation because it cross the border between the graphic view of relationships that is very convenient for human beings and the explicit equations and numbers that are the province of present computers. To find a device that can readily be sketched by a layman and yet be so carefully defined that useful theorems concerning it can be proved by formal methods is rare. Although there is a danger that people who do not thoroughly understand influence diagrams may abuse them and be misled, there is an even greater promise that the influence diagram will be an important bridge between analyst and decision-maker.

Valuing Extreme Outcomes

One of the problems perplexing early users of decision analysis was how to treat outcomes so extreme that they seemed to be beyond analysis. For example, the question of how a person's death as the result of medical treatment can be balanced with other medical outcomes, like paralysis or even purely economic outcomes, was especially demanding. These problems appear to raise both ethical dilemmas and technical difficulties. One ethical dilemma centered on who had the right to value lives. A technical difficulty was revealed when an economist testifying in court on the value of a life was asked whether he would be willing to allow himself to be killed if he were given that amount of money. Nevertheless, once the ethical issue is clarified by acknowledging that a person may properly place a value on his own life, then the technical question of how to do it can be addressed quite satisfactorily, especially in the case of exposure to the many small risks present in modern life [4,5]. The results have major implications for many decisions affecting health and safety.

The development of ways to think about the unthinkable has shown that no decision problem lies beyond the realm of decision analysis. That is very satisfying, for were you faced with medical decisions about a loved one, would you want to use second-rate logic any more than a second-rate doctor?

Conclusion

When decision analysis was first developed, a common comment was, "If this is such a great idea, why doesn't [insert name of large, famous company] use it?" Today, it is difficult to find a major corporation that has not employed decision analysis in some form. There are some factors that should lead to even greater use. For example, decision analysis procedures are now more efficiently executable because the increased power of modern computers has reduced the costs of even very complex analyses to an affordable level. The problems that can be successfully attacked now run the gamut of all important decision problems. Increasing uncertainties and rapid change require fresh solutions rather than tested "rules of thumb." Some day, decision analysis of important decisions will perhaps become recognized as so necessary for conducting a provident life that it will be taught in grade school rather than in graduate school.

References:

1. Ronald A Howard, "Decision Analysis: Applied Decision Theory," Proceedings of the Fourth International Conference on Operational Research, Wiley-Interscience, New York, 1966, pp. 55-71.

2. Ronald A Howard, "The Foundations of Decision Analysis," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No. 3, (September 1968): 211-19.

3. Ronald A Howard and James E Matheson, "Influence Diagrams," Department of Engineering-Economic Systems, Stanford University, July 1979.

4. Ronald A Howard, "On Making Life and Death Decisions," Societal Risk Assessment, How Safe Is Safe Enough?, Edited by Richard C. Schwing and Walter A Albers, Jr, General Motors Research Laboratories, Plenum Press, New York, 1980.

5. Ronald A Howard, James E Matheson, and Daniel L Owen, "The Value of Life and Nuclear Design," Proceedings of the American Nuclear Society Topical Meeting on Probabilistic Analysis of Nuclear Reactor Safety, American Nuclear Society, May 8-10, 1978.

6. Daniel Kahneman and Amos Tversky, "Prospect Theory: An Analysis of Decision under Risk," Econometrica, 47, No. 2 (March 1979): 263-291.

7. D Warner North, "A Tutorial Introduction to Decision Theory," IEEE Transactions on Systems Science and Cybernetics, SSC-4, No 3, (September 1968): 200-10.

8. Howard Raiffa, Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley, 1968.

9. Myron Tribus, Rational Descriptions, Decisions, and Designs, Pergamon Press, 1969.

10. Amos Tversky and Daniel Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science, 185 (Sept 27, 1974): 1124-1131.

11. Amos Tversky and Daniel Kahneman, "The Framing of Decisions and the Psychology of Choice," Science, 211 (Jan 30, 1981): 453-458.

Republished with permission of The Stanford Decisions and Ethics Center

The Need for Good People

I believe there is always a crying need for good people. My father passed these words to me following one of my adolescent tirades about the apparent lack of opportunity in my life – that was almost forty years ago. Today, while attractive opportunities seem scarce to many in society, the need for good people is as great as ever.

My own career has taken me through both highs and lows in advancement and success. My work history spans three decades and includes public and private sector roles that have taken me around the world. I have worked in tents, factories, offices, camps, hangers, barracks, warehouses, restaurants, vans, kiosks, aircraft, and classrooms, as well as on stage, behind podiums, on telephones and radios, and of course, online. Each of these workplaces was a vantage point from where I could watch and learn how opportunity presents itself to people. The result of these experiences is my deep conviction that opportunity abounds in our world, and that what is in short supply are good people.

Choosing to believe that opportunity is abundant is not an easy path for anyone, including me. It means taking risks and accepting responsibility. It means lifetime learning. It means practicing integrity in my affairs, keeping promises, and exercising compassion for those less fortunate. Ultimately, it means living life centered on the ideals and virtues that lift humanity, while becoming the best person, I can be.

My father understood this very well. He grew up a depression child, joined the Marines during World War II, earned an accounting degree after the war, and raised a family on a salary thereafter. He has since passed away, but his legacy to me are his words that find their way into my beliefs and values, and while I am now on my own course through life, what is clear for me is that opportunity is abundant, and that the search for more good people goes on, just like it always has. Choosing to live life abundantly is a choice I hope I can realize in who I am and will become. Life’s opportunities are as great as ever, and the choice of becoming the person who can fulfill society’s need is mine to make everyday. My father was right; there is always a crying need for good people, now and in all times. This I believe.

More at This I Believe

Why Policymakers Need to Take Note of High-Frequency Finance

by Richard Olsen © VoxEU.org

Why should high-frequency finance be of any interest to policymakers interested in long-term economic issues? This column argues that the discipline can revolutionise economics and finance by turning accepted assumptions on their head and offering novel solutions to today’s issues.

I believe high-frequency finance is turning aspects of economics and finance into a hard science. The discipline was officially inaugurated at a conference in Zurich in 1995 that was attended by over 200 of the world’s top researchers. Since then, there have been a large number of publications including a book with the title Introduction to High-frequency Finance. “High-frequency data” is a term used for tick-by-tick price information that is collected from financial markets. The tick data is valuable, because they represent transaction prices at which assets are bought and sold. The price changes are a footprint of the changing balance of buyers and sellers.

The term “high-frequency finance” has a deeper meaning and is a statement of intent indicating that research is data-driven and agnostic. There are no ex ante theories or hypotheses. We let the data speak for itself. In natural sciences this is how research is often conducted. The first step towards discovery is pure observation and coming up with a description of what has been observed – this may sound easy but is not at all the case. Only in a second step, when the facts are clearly established, do natural scientists start formulating hypotheses that are then verified with experiments.

In high-frequency finance:

• The first step involves the collecting and scrubbing of data.
• The second step is to analyse the data and identify its statistical properties.

Here one looks for stylised facts which are significant and not just spurious. Due to the masses of data points available for analysis (for many financial instruments one can collect more than 100,000 data points per day), identification of structures is straightforward, either there is a regularity or there is none.

• The third step is to formalise observations of specific patterns and seek tentative explanations, theories to explain them.

The abundance of data in high-frequency finance has profound implications for the statistical relevance of its results. Unlike in other fields of economics and finance, where there is not sufficient data to back up the inferences, this is not an issue in high-frequency finance. The results are unambiguous and turn economics and finance into a hard science, just as is the case for natural sciences. This is not a bad thing.

High-frequency data as an answer to singularity of macro events

Today we are all grappling with the global financial crisis and have to make hard decisions. In living memory, we have not seen a crisis of a similar scale, so policymakers are in a vacuum and do not have any comparable historical precedents to validate their policy decisions.

If the global economy had been in existence for 100,000 years, this would be a different matter. We would have had many crises of a similar scale, and we could use these previous events as a benchmark to evaluate the current crisis. The modern economy with financial markets linked together through high speed communication networks trading trillions of dollars on a daily basis is a new phenomenon that did not exist even 20 years ago. People refer to the events of 1929 and subsequent years, but while these events can be used as one possible point of reference, they are not meaningful in the statistical sense. On a macro level, we can make observations but no inferences because we do not have the historical data. There is a void that researchers and policymakers need to acknowledge.

Fractals: Understanding macro structure from micro data

High-frequency finance can fill the void with its huge amounts of data – if we embrace fractal theory that explains how phenomena are the same even if they occur at different scales. Fractal theory suggests that we can search for explanations of the big crisis by moving to another time scale, the short term.

At a second-by-second level, there are an abundance of crises and systemic shocks; just imagine the occurrence of the many price jumps due to unexpected news releases and political events or large market orders. Albeit on a short-term time scale, we study how regime shifts occur and how human beings react. The large number of occurrences allows for meaningful analysis. We study all facets of a crisis, how traders behave prior to the crisis, how they react to the first onslaught, how they panic, when the going gets hard and finally, how their frame of reference which previously was a kind of anchor and gave them a degree of security breaks down and how later, when the shock has passed, the excitement dies down, there is the aftershock depression and then eventually how gradual recovery to a new state of normality begins.

The everyday events sum up and shape the tomorrow

High-frequency finance has another big selling point, one which policymakers should take note of: the study of market events on a tick-by-tick basis brings to the surface the detailed flows of buying and selling that occur in the market. From this information, it is possible to build maps of how market participants build up positions and how asset bubbles develop over time. By tracking price action on a tick-by-tick basis, it is possible to infer the composition of those bubbles similar to the work of geologists studying rock formations. Researchers can identify, who has been buying and selling, on what time horizons they trade, how resilient they are to price shocks, what makes them turn their position and become net sellers as buyers. Based on this information we can make inferences of the likely collapse of those bubbles.

High-frequency finance opens the way to develop "economic weather maps". Just as in meteorology, where the large scale models rely on the most detailed information of precipitation, air pressure and wind, the same is true for the economic weather map. We have to start collecting data on a tick-by-tick level and then iteratively build large scale models. Today, the development of such a global economic weather map has barely started. The "scale of market quake" (a free Internet service) is a first instalment, but the start of an exciting development.

High-frequency finance holds out the hope of turning aspects economics and finance into a hard science by the sheer volume of data and its ability to set events into their appropriate context by mapping rare events into a short-term time scale with a near infinity of events, albeit at a shorter-term time scale. Second, the tracking of events on a tick-by-tick basis opens the door to identify underlying flows and develop economic weather maps. Surely that’s not a bad thing?

References

Bisig T, Dupuis, A, Impagliazzo, V, and Olsen, R (2009), “The scale of market quakes”, working paper, September.

Gençay, R, Dacorogna, M, Müller, U, Olsen, R, and Pictet, O (2001), An Introduction to High Frequency Finance, Academic Press.

Mandelbrot, B (1997), Fractals and Scaling in Finance, Springer.

Mandelbrot, B, Hudson, R (2004), The (Mis)behavior of Markets, Basic Books.

Republished with permission of VoxEU.org

The Empire Strikes Back

by Avinash Persaud © VoxEU.org

The role of financial institutions in the global crisis has led to a consensus that financial regulation must change. This column argues that the banking lobby, far from depleted, has struck back with a vengeance. It has managed to postpone the much needed regulation for a time when the need for it will be forgotten.

There are two remarkable aspects of the consensus around international financial regulation emerging in the run up to the November G20 meeting in Seoul. The first is that there is a consensus. International regulators are agreed that banks must set aside much more capital for risky assets; be less dependent on the whims of money markets; constrain the maturity mismatches between their assets and liabilities and set aside capital for holding complex derivatives where there may be settlement and clearing risks. They also agree that capital adequacy should move counter to the economic cycle and that banks should not be “too big to fail”. Getting an international consensus around action that is sensible – save for the emphasis on “too big to fail”- is no mean achievement.

The second is that despite appearing to be down and out, the banking lobby has struck back, successfully making the case that all of these initiatives should be postponed or phased-in between 2015 and 2019. By then the pressure for regulatory reform could be a distant memory. Financial regulation veterans will be experiencing déjà vu. In each of the last seven international financial crises, plans for a radical shake up of international regulatory or monetary arrangements made surprising progress, only to be tidied away and stuffed in the bottom drawer once the economy recovered. Many of the new initiatives being proposed today have been pulled out of that same drawer, dusted down and updated.

The argument that the banking system is too broken and the world economy too fragile, to support more onerous regulations, is seductive for politicians desperately trying to boost consumer demand. But it is suspect. It highlights that attempts to make banking regulation more counter-cyclical have not gone far enough. The point of counter-cyclicality is to loosen the constraints to lending in times of recession like today and to tighten them when growth and optimism have returned and the worse credit mistakes are being made. Counter-cyclicality needs to be at the heart of the new regulatory regime and not an optional extra. As Professor Charles Goodhart of the LSE and I have said before, crashes will not be avoided if we continue to feed the booms. The methodology of counter-cyclicality is complex and given that economic cycles are more national or regional than global, it makes for greater host country regulation and national ring-fencing of bankers’ operations. International banks do not like that. To counter they appeal to the “right”-sounding notion of level playing fields.

The other problem of kicking regulatory initiatives into the long grass is that as long as the prospect of new profit-squeezing regulation is out there, uncertainty will limit the one thing everyone is agreed the banking system needs more of – capital from investors. It is one of those delicious fallacies of composition that what banks want individually is often not in their collective interests. I recall writing in October 2002, what the FT headline writers presciently captured as “Banks put themselves at risk in Basel.”

Competitive finance is critical to the development of a robust and dynamic economy – locally and globally. But the lesson currently being repeated is that regulatory capture – subtle, sophisticated, and seductive – has the power to stop us from developing a financial industry that serves the economy rather than the other way around.

Tackling regulatory capture head on is the better argument for limiting bank size. The notion that smaller institutions will make the financial system safer ignores history. The UK Secondary Banking Crisis of 1973-75, for example, had a bigger impact on property prices and the stock market than the current one. The principal avenue of financial contagion is the panic-stricken search for institutions that look similar to the one that has just failed. Moreover, a large number of small institutions doing the same dangerous thing is just as toxic, if not more so, than a small number of large institutions engaged in the same activity. But smaller institutions invest less in political lobbying. A politically less powerful financial system has a better chance of being reassuringly boring.

The way to make the financial system safer is to break up institutions not by the porous boundaries of “narrow” and “wholesale” banking, but by the more fundamental boundaries of risk capacity. To create systemic resilience we need a systemic approach to capital adequacy requirements across the entire financial system, one that pushes different financial risks to wherever across the entire financial there is greater capacity for those different risks.

This is simpler than it sounds. There are three major types of risk: credit risk, market risk, and liquidity risk. Their differences can be found by the different ways in which these risks can be hedged or absorbed. The capacity to absorb liquidity risk comes from having time to sell an asset because liabilities, like promises to pay a pension in twenty years, are long-term. The capacity to absorb credit risk comes from having access to a wide range of uncorrelated credit risks to pool together, like a loan to an international oil company and another to a local wind farm. A financial system in which liquidity risks were held by young pension funds because of the capital required to set aside maturity mismatches, and credit risks by large consumer banks, because of the capital required to set aside for concentrated credit risks, would be far safer than one with twice the amount of capital but where the banks fund illiquid private equity investments and pension funds hold credit derivatives because regulators and accountants treated risk as if all that mattered was price volatility not risk capacity. Limiting risk taking to risk capacity would limit the size of banking institutions. It would create opportunities for new players with different risk capacities.

But the odds of a systemic approach to systemic risk appear slim. It’s politics, stupid!

Republished with permission of VoxEU.org

Demographics and Stock Market Fluctuations

by Carlo Favero, Arie Gozluklu, and Andrea Tomoni © VoxEU.org

Are long-run stock market returns predictable? This column shows that a forecasting model that uses a demographic variable – the ratio of middle-aged to young adults – as well as the dividend price ratio, performs “very well” in forecasting long-horizon stock market returns.

Figure 1 shows 1-year and 20-year annualised US stock market returns (S&P 500 index) over the course of nearly the last century. Returns are determined by a “slow-moving” information component and by a “noise” component. The noise component dominates the data at high frequencies, while the information component emerges when high-frequency observations are aggregated over time to construct long-horizon returns.

Figure 1

As the information component is naturally related to “fundamentals”, Figure 1 helps understand the empirical evidence that fundamentals perform better in predicting returns as the predictive horizon gets longer. In particular, the dynamic dividend growth model (Campbell-Shiller 1988) suggests that the relevant fundamental to capture the information component in stock market returns is the dividend-price ratio. This variable regularly plays an important role in recent empirical literature that has replaced the long tradition of the efficient market hypothesis with a view of predictability of returns (see for example, Cochrane 2007). But there is an ongoing debate on the robustness of return predictability and its potential use from a portfolio allocation perspective (Boudoukh et al. 2008). The essence of this debate is captured by Figure 2 that reports the US dividend/price ratio along with the 20-Year annualised stock market returns.

Figure 2

The Figure shows the presence of some co-movement between the two variables. This is somewhat limited by the fact that the dividend-price shows a very high degree of persistence that does match the mean reversion of the returns. This high degree of persistence contradicts one of the crucial hypotheses of the dynamic dividend growth model that is based on the assumption that the dividend-price is a stationary variable. This degree of persistence is at the heart of the debate on the robustness of the statistical evidence on the predictability of stock market returns.

Is there a role for demographics?

Intuitive reasoning hints at demography as an important variable to determine the long-run behaviour of the stock market, while it is difficult to imagine a relationship between high-frequency fluctuations in stock market prices and a slow-moving trend determined by demographic factors.

In fact, a theoretical model by Geanakopoulos et al. (2004) predicts that a specific demographic variable – the ratio of middle-aged to young population – explains fluctuations in the dividend yield.

Geanakopoulos and his co-authors consider an overlapping generation model in which the demographic structure mimics the pattern of live births in the US, that have featured alternating twenty-year periods of boom and busts. They conjecture that the life-cycle portfolio behaviour – which suggests that agents should borrow when young, invest for retirement when middle-aged, and live off their investment once they are retired – plays an important role in determining equilibrium asset prices. Consumption smoothing by the agents, given the assumed demographic structure, requires that when the middle-aged to young population ratio is small, there will be excess demand for consumption by a large cohort of retirees and for the market to clear, equilibrium prices of financial assets should adjust, i.e. decrease. The result is that saving is encouraged for the middle-aged. As the dividend/price ratio is negatively related to fluctuations in prices, he model predicts a negative relation between this variable and the middle-aged-to-young ratio.

In a recent CEPR Discussion Paper (Favero et al. 2010), we take the Geanakopoulos et al. model to the data via the conjecture that fluctuations in the middle-aged-to-young ratio could capture a slowly evolving mean in the dividend price ratio within the dynamic dividend growth model. We find strong evidence in favour of using this variable together with the dividend/price ratio in long-run forecasting regressions for stock market returns, as Figure 3 and Figure 4 illustrate.

Figure 3

Figure 3 reports the dividend price ratio and the middle-aged-to-young ratio to show how, in line with the predictions of Geanakopoulos et al., a negative relation between the middle-aged-to-young ratio and the dividend price is present in the data. The demographic variable captures the slowly evolving information component in the fundamental. Does this help to predict long-horizon stock market returns? Yes. In fact, the econometric based yes, contained in our CEPR paper, is visually illustrated by Figure 4, which reports the middle-aged-to-young ratio, 10-year stock market returns, and the deviation of the dividend-price ratio from its slowly time varying mean captured by the middle-aged-to-young ratio.

Figure 4

Figure 4 also illustrates an additional interesting feature of the middle-aged-to-young ratio. Long-run forecasts for this (exogenous) variable are readily available. In fact, the Bureau of Census provides projections up to 2050 for the middle-aged-to-young ratio. In our paper we exploit the exogeneity and the predictability of the demographic ratio to project the equity risk premia up to 2050. Our simulations point to an average equity risk premium of about 5% for the next forty years.

The research agenda

The empirical evidence of a stable relation between a demographic variable and long-horizon US stock market returns naturally generates a number of interesting research questions.

• First, the fact that a slow moving variable determined by demographics has very little impact on predictability of stock market returns at high frequency but a sizeable and strongly significant impact at low frequency has some obvious consequences on the slope of stock market risk, defined as the conditional variance and covariance per period of asset returns. Demographics should then become a natural input into the optimal asset allocation decision of a long-horizon investor.
• Second, what about the bond market? If the middle-aged-to-young ratio plays an important role in capturing an information component that helps to predict long-horizon stock market returns it should also have a role in capturing a persistence components also in bond-yields.
• Third, what is the international evidence? Our empirical results are so far limited to the US case only, but it is important to assess their robustness when the model is extended to other countries.

References:

Boudoukh, J, Richardson, M, and Whitelaw, R F (2008), “The Myth of Long-Horizon Predictability,” The Review of Financial Studies, 21(4):1577-1605.

Campbell, J Y Robert Shiller, R (1988), “Stock Prices, Earnings, and Expected Dividends,” Journal of Finance, 43:661-676.

Cochrane, J H (2007),”The Dog that Did Not Bark: A Defence of Return Predictability,” Review of Financial Studies, 20, 5.

Favero, C A, Gozluklu, A E, and Tamoni, A (2009) “Demographic Trends, the Dividend-Price Ratio and the Predictability of Long-Run Stock Market Returns,” CEPR working paper 7734, forthcoming in the Journal of Financial and Quantitative Analysis.

Geanakoplos, John, Michael Magill and Martine Quinzii (2004), “Demography and the Long Run Behaviour of the Stock Market,” Brookings Papers on Economic Activities, 1: 241-325.

Republished with permission of VoxEU.org