What causes asset price bubbles? The paper below (just released) by Sheen Levine and David Stark looks into the issue in an original way. Findings: ethnic homogeneity promotes conformity and leads to misplacing; diversity disrupts conformity and leads to better information processing.
Here’s the longer summary:
In this paper from the Proceedings of the National Academy of Sciences, Sheen Levine and I (together with other co-authors) examine a prominent market failure: price bubbles. We propose that bubbles are affected by ethnic homogeneity in the market and can be thwarted by diversity. Using experimental markets in Southeast Asia and North America, we find that market prices fit true values 58% better in ethnically diverse markets. In homogenous markets, overpricing is higher and traders’ errors are more correlated than in diverse markets. The findings suggest that homogeneity promotes conformity. Price bubbles arise not only from individual errors or financial conditions, but also from the social context of decision making. Informing public discussion, our findings suggest that ethnic diversity disrupts conformity and leads to better information processing.
Congratulations, Sheen and David!
See here for more information:
October 2, 2014
There are three things wrong. The first is that risk management has become too bureaucratic. It emphasises a controls-based approach, characterised by excessive box-ticking. The second is in the financial arena, where banks and other financial institutions became reliant on value-at-risk models. Those models were dependent on a range of assumptions, not least an implicit assumption about liquidity and the real availability of interbank funding.
In hindsight, we can see there was insensitivity to the limitations of those models. And the third thing to go horribly wrong was focusing on entities in isolation, rather than the relationship between entities. Banks and insurers all used the same risk management model. But they didn’t consider what would happen if they all acted in the same way and how that would create a systemic problem.
See the full interview with Professor Mike Power here.
September 19, 2014
In this issue of the London Review of Books, Donald Mackenzie describes the communication technologies that help high frequency traders do their thing (Be Grateful for Drizzle, v. 36:17). After an extensive discussion of how fibre-optic cables, microwaves, millimetre waves, and laser transmission through the atmosphere move information between the exchanges, Mackenzie asks a relatively straightforward question:
The right question to ask about high-frequency trading is not just whether high-frequency traders are good or bad, or whether they add liquidity to the markets or increase volatility in them, but whether the entire financial system of which they are part is doing what we want it to do.
Compare and contrast to Vasant Dhar, former HFT trader and Head of Information Systems at Stern School who wrote in a commentary at CNBC:
Let’s not risk our markets to a populist-based reaction that asks whether HFTs do any “social good,” but rely instead on an objective analysis of the “big data” that emanates from the markets.
On the one hand science studies says that technologies can be designed to accommodate a variety social visions. On the other, once in place, material infrastructures do constrain the kinds of choices we can make.
I definitely think we have to treat big data analytics as a space where political battles can happen in data-saturated systems, but it can’t be the only one. And yet, the dominance of digital data must neutralize some of the tools that social scientists usually deploy to contribute to the conversation. Not all conversations about the social good are possible, but which ones are…?
Since exchanges are not my topic of research, I’m sort of off the hook on this one. But still, I’m left wondering how to go about taking a position.
September 11, 2014
A post written by Dane Pflueger and Tommaso Palermo
As a recent NY Times article has pointed out, the public rankings regime in higher
education is changing. From the elusive quest of determining quality or ‘who’s
the best’, public and private authorities in America have moved to the even more
daunting quest of determining best value, or, as the article explains it, ‘where you
can get the most bang for your buck’.
Although, as the article makes clear, there are innumerable different ways in which
this new notion of best value is being measured and expressed, it is made possible,
in principle, by placing a denominator, cost, below those sort of public measures that
might be summarised as quality.
Best Value = Quality/Cost
We might consider this movement to be simply another instantiation of the public
ranking phenomena, producing yet another set of dysfunctional effects through
mechanisms such as reactivity and commensuration to use Espeland and Sauder’s
However, one ranking is not necessarily the same as any other. As accountants are
well aware, fractions are very different from integers. Indeed, as we aim to quickly
show here, drawing from examples in management accounting, public rankings
that are conceptually-conceived fractions, might produce quite distinct sorts of
phenomena and effects.
The movement from an integer to a fraction reorients attention and action in two
directions. Faced with the fraction quality/cost, administrators confront two options
for increasing performance: increase quality or reduce cost.
This sounds initially like a more enlightened form of ranking than quality alone.
Indeed, it transforms inputs from a constraint on performance, into an asset, thus
injecting the notion of performance with a more relative and even democratic appeal.
No longer, it might be argued, is organizational performance constrained by one’s market
or business model. Instead, quality is reconstituted as something closer to ‘fit for purpose’.
However, much management accounting literature has drawn attention to the fact
that such options are heavily constrained by the organization’s existing place in the
league table, leading to ‘under-optimization’ from the standpoint of the system as a
whole is a common result.
In the case of the Return on Investment ration (Profit/Assets), which is conceptually
similar to Best Value, organizations that initially achieve high performance are
encouraged to under-invest in assets that will be productive from the system
perspective, but that will lower the ROI of the individual unit. At the same time, poorly
performing units are encouraged to invest to generate new returns, but at much less
efficient rate than the system as a whole. The net result is that overall performance
of the system declines at the inside of the edges: either through under-investment in
profitable assets or over-investment in unprofitable ones.
In the educational setting, using quality/value as a primary public measure of
performance might manifest itself as a constraint on overall educational market,
discouraging high performing universities from expanding its offerings into less
value products, while encouraging low performing universities to expand poor value
The movement from an integer to a fraction also highlights the distinctive relationship
between the numerator and denominator, which presumably interact in a quite
specific manner. In the ROI regime, there is seen to be an imperfect relation
between return and investment. Managers, it is sometimes thought, can increase
returns by increasing investment, but very good managers can increase return/
investment at a higher rate. This may be true in the long term, but in the short
term, ‘very good managers’ are made by exploiting the imperfect relationship
between the two terms, often in dysfunctional ways. Managers, for example, often
manipulate the timing of investment and booking of revenues so as to drive a
temporarily artificial wedge between the two and increase returns.
In the educational context, the relationship between cost and quality is under-
explored and undoubtedly complex. We might imagine that, like ROI, the best value
school, like the good manager subject to ROI calculations, will try to interact the
two terms opportunistically (shifting more costs from tuition to fees, for example, or
borrowing heavily to boost quality at one point, only to pay for it in increased fees
It is also unclear how quality and cost relate and interact. If cost and quality relate
in a perfectly elastic way, then the ratio merely presents different possibilities for
organizations to move along the ratio line, repositioning itself in a different ‘market’
but not affecting the overall value of the product delivered. This scenario might be
quite likely given the existing arguments about costs simply providing market proxies
If cost and quality are perfectly inelastic, then the ratio merely presents two variables
to optimise without considering them to have a tradeoff or mutual effect. Hence we
would have merely an extension of the ranking system on merely a number of new
One final point relates to those who produce or sponsor a ranking system for higher
education institutions. As a recent article shows, ranking systems themselves
‘compete’ with each other. A successful ranking is one that combines familiarity
(some universities have to be at the top!) and surprise (something unexpected that
attracts the attention of the media). On this basis, it is clear that the production of a
specific ranking system is far from being a neutral game.
Considering all the unintended consequences and behavioral problems that rankings
may trigger, the question is whether some sort of auditing or external certification
of the quality of rankings might help. The answer is probably not. As shown in
Free at al. (2009) on the FT ranking, the auditing of the data underlying
universities’ rankings is subject to several constraints leading to the auditing of the
relatively small portion of data that indeed is auditable.
In summary, rankings need to be handled with extreme caution by university
administrators and those responsible for allocating funds to the higher education
sector. The cursory discussion of changes in the public measure system suggests
some interesting questions to be pursued regarding the changing public measures
regime in education. Often a number is seen to be just the same as any other
number, but a closer attention to the type and form of the number might help
us better understand the many complex ways in which public measures and
organizational performance intertwine.
Tommaso Palermo is a Lecturer in Accounting at the London School of Economics.
Dane Pflueger is an Assistant Professor in Performance Management at at Copenhagen Business School.
September 8, 2014
Here’s some great news. Today the Wall Street Journal is running an article on the research that Yuval and I have been doing for ah… a number of of years, on the New York Stock Exchange.
You can find a link to the newspaper article here here.
September 2, 2014
Blog readers may be interested in “Capitalizing on Performativity: Performing on Capitalization”, a symposium to be held at the Ecole des Mines de Paris on 16-17 October 2014. Details on purpose, content and form here.