Saturday, September 28, 2013


Tim Harford reports the experiment where students rated music with and without access to other peoples' opinion.  Unsurprisingly, other views are influential.

"Watts and his colleagues split the music fans at random into eight “worlds”. Some “worlds” were asocial: people listened to and rated songs without knowing what others were doing. In other “worlds”, people were shown what others in their world were rating and downloading. The social “worlds” produced two striking results. Inequality increased: the most popular songs were far more popular than in the asocial world, as people herded together. The unpopular songs were even less popular."

If this is the case with something that is very subjective, how much more in financial markets when there is assumed to be important information available that would influence the perceived value?  This is a good argument for momentum and a social aspect to valuation.

Thursday, September 26, 2013

Rajiv Sethi: Information, Beliefs, and Trading

Rajiv Sethi: Information, Beliefs, and Trading:  Rajiv investigates the market for the US Presidential election and finds five groups of traders:  two large players, one biased to Romney and one trading an algorithm, another large group that points in one direction or the other, some arbitragers and a small group of independent traders reacting to news.

 "This kind of reasoning, when pushed to its logical limits, leads to some paradoxical conclusions. As shown by Aumann, two individuals who are commonly known to be rational, and who share a common prior belief about the likelihood of an event, cannot agree to disagree no matter how different their private information might be. That is, they can disagree only if this disagreement is itself not common knowledge. But the willingness of two risk-averse parties to enter opposite sides of a bet requires them to agree to disagree, and hence trade between risk-averse individuals with common priors is impossible if they are commonly known to be rational."

This means that they must be acting irrationally or they do not have a common prior.  If they are not acting rationally, they may be noise traders.  Market makers can then bring informed and uninformed traders together (taking a cut of the transfer of resources from those without information who want to trade to those with information). An alternative is that traders disagree about the implication of the common information. In the jargon, there is 'heterogeneous interpretation of public information'.

Wednesday, September 25, 2013

Science can help to spot symptoms of executive hubris -

 Gillian Tett - looks at some work trying to assess hubris from the words of political or corporate leaders.

 "“From a total of 148 sentences identified as being good news, 57 per cent was attributed to the chief executive himself, while only 39 per cent was attributed to the company and a further 4 per cent to outside parties,” they write. But the chief executive “did not attribute any bad news to themselves or the company but stated it was the result of external factors”."

It looks really interest and could tie in with some analysis of the MPC documents.  However, there is clearly a risk that if some patterns are found, they will be used by corporate communications and spin doctors to change the words of politicians.

Tuesday, September 24, 2013

Bubbles Tomorrow, Yesterday, but Never Today?

John C. Williams at the Federal Reserve Bank San Francisco discussed bubbles and takes a thorough look at asset price bubbles and sets through the assessment of bubble conditions as well as the explanation for mis-pricing.

 "Many researchers are probing why people have the procyclical pattern of optimism seen in these surveys. One key element in the theories coming out of this research is that people do not possess the full set of information assumed in the standard asset price model with rational expectations. Instead, they must make do with the limited information at hand when judging likely future discounted dividend payments and the future price of the asset. Indeed, a growing body of evidence in behavioral economics and finance shows that people’s expectations of future asset returns depend on their past experiences (Vissing-Jorgensen 2003 and Malmendier and Nagel 2011). This process of forecasting with limited information has been shown to cause forecast errors that can drive a wedge between asset prices and the values implied by economic fundamentals (Cutler, Poterba, and Summers 1991 and Barsky and DeLong 1993)."

There is more here about momentum in expectations and how this explains asset price swings.  This looks particularly at 'extrapolative expectations'.  This could be a very useful way to start building a banking model.  There are more references in the item.

The 'possibility effect' - Lex

The suggests that there is something called the 'possibility effect'.  It makes intuitive sense:  the hope for a large gain; a lottery ticket; an acting career.

"The best explanation of the bizarre trading may be the same one that behavioural economists use to explain the allure of lottery tickets. Small probabilities of big gains do strange things to people’s risk appetites. If the chance of a gain moves from, say, 51 to 52 per cent, most people will pay just a bit more to invest. But if you move the probability from 0 to 1 per cent, some investors (or punters, if you prefer) will stump up big. This is the “possibility effect,” and it is ugly when it goes into reverse. Those who held on to BlackBerry until Friday were emotionally attached to the big gain they could make if BlackBerry fixed things. This sort of hope cannot be extinguished by strong evidence – only ironclad proof. The lesson is to be careful about buying a beat up stock because “the worst is priced in”. You may be co-investing with dangerous optimists."

What does this do for risk. It suggests that a large right-tail is really valuable or a portfolio with more risk assets can be valued more highly than one with modest risk (and opportunity).  Can this be back by research and modelled?

Monday, September 23, 2013

Measuring Uncertainty

Abstract from a paper by Kyle Jurado, Sydney Ludvigson and Serena Ng.  Can this be used to get another estimate of uncertainty?  What is the relationship between this uncertainty and the carry trade profit?

Measuring Uncertainty: "This paper exploits a data rich environment to provide direct econometric estimates of time-varying macro uncertainty, defined as the common variation in the unforecastable component of a large number of economic indicators. Our estimates display significant independent variations from popular uncertainty proxies, suggesting that much of their variation is not driven by uncertainty. Quantitatively important uncertainty episodes appear far more infrequently than indicated by popular uncertainty proxies, but when they do occur, they have larger and more persistent correlations with real activity. Our estimates provide a benchmark to evaluate theories for which uncertainty shocks play a role in business cycles."

'via Blog this'

Saturday, September 21, 2013

Econometrics by Simulation: Cluster Analysis

Econometrics by Simulation provides a thorough example of cluster analysis.

"Cluster analysis is class of tools in which you use to group complex data into distinct clusters based on observable variation.  Cluster analysis is closely related to the idea of latent class analysis in which data is grouped into classes based on observable characteristics."

However, why can't this be used to classify carry trade returns in different regimes.  The clusters can be things like (high level of international risk aversion; exchange rate type; month; interest rate differential; stock market performance; housing market performance; political tension.  Could be done in the same fashion?

Wednesday, September 18, 2013

Major projects

The Guardian reports on the cost of the abandoned NHS computerisation system.  Daniel Kahneman recalls a failed education project in Kahneman, D., & D. Lovallo, 1993, Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking', Management Science, 39 (1).

In 1976 one of us (Daniel Kahneman) was involved in a project designed to develop a curriculum for the study of judgment and decision making under uncertainty for high schools in Israel. The project was conducted by a small team of academics and teachers. When the team had been in operation for about a year, with some significant achievements already to its credit, the discussion at one of the team meetings turned to the question of how long the project would take. To make the debate more useful, I asked everyone to indicate on a slip of paper their best estimate of the number of months that would be needed to bring the project to a well-defined stage of completion: a complete draft ready for submission to the Ministry of Education. The estimates, including my own, ranged from 18 to 30 months. At this point I had the idea of turning to one of our members, a distinguished expert in curriculum development, asking him a question phrased about as follows: "We are surely not the only team to have tried to develop a curriculum where none existed before. Please try to recall as many such cases as you can. Think of them as they were in astage comparable to ours at present. How long did it take them, from that point, to complete their projects?" After a long silence, something much like the following answer was given, with obvious signs of discomfort: "First, I should say that not all teams that I can think of in a comparable stage ever did complete their task. About 40% of them eventually gave up. Of the remaining, I cannot think of any that was completed in
less than seven years, nor of any that took more than ten". In response to a further question, he answered: "No, I cannot think of any relevant factor that distinguishes us favorably from the teams I have been thinking about. Indeed, my impression is that we are slightly below average in terms of our resources and potential".

Monday, September 09, 2013

Speculative Investors and Tobin's Tax in the Housing Market

Speculative Investors and Tobin's Tax in the Housing Market:  Interesting support here for the notion that speculators provide liquidity and assist in the process of price discovery.

"This paper examines the impact of a policy change in Tobin’s tax on housing market speculators. The policy intervention effectively raised the transaction cost in the market segment with a high presence of speculators. Relative to the unaffected control sample, we find that the rise in transaction cost substantially reduced speculative trading activities in the treatment sample. However, it significantly raised its price volatility and reduced the price informativeness. We further show that the unintended consequences are likely due to a relatively greater withdrawal by informed speculators than by destabilizing speculators after the transaction cost increase."

There is an interesting addition with the finding that informed speculators disappeared more than those that they deem destabilising.  The authors use the fact that speculators in the housing market cannot short the market to assert that there must be more informed speculators in markets where houses were undervalued than where they were over-valued.  This is identified by future price performance and it is found that there is a greater volume decline in those areas that were under-valued than those that are over-valued.

Sunday, September 08, 2013

Spillovers, feedback and impreciseion

A beautiful overview by Chris Dillow of the difficulties of forecasting has this nugget.

 "Of course, the idea of keeping up with the Joneses is an old one. But until recently, it's been hard to find proper evidence for it: if we see a group of neighbours spending more, how can we tell whether some are copying others or simply that the neighbourhood generally has enjoyed some good fortune? Perhaps the neatest evidence here comes from a study of the effects of the Dutch postcode lottery. Every week, this selects a postcode at random and gives a BMW to everyone in it who bought a ticket. Researchers have found that the neighbours of winners who didn't win themselves are significantly more likely to buy a new car. This is a clean sign of a network effect between consumers."

The whole article has an excellent overview and great links.

'via Blog this'

Data for financial bubbles

Here is an indicator from Phillips,Wu and Yu to determine bubbles.

There is a need for data to test bubbles.  This can come from Reinhart and Rogoff or Ahamed.

Financial booms and busts were, and continue to be, a feature of the economic landscape. These bubbles
and crises seem to be deep-rooted in human nature and inherent to the capitalist system. By one count there
have been 60 different crises since the 17th century.

Ahamed (2009).

'via Blog this'

Coase's Penguin

The theory of the firm explains why firms exist and provides a framework for analysing where the boundaries of the firm will lie.  In itself this can be interesting as this is the arena to discuss the benefits of mergers or disposals or outsourcing.  See John Naughton at the Guardian for an introduction.

However, with Coase's Penguin:, Yochai Benkler suggests that this can also explain a new method of producign goods and services. 

"In this paper I explain that while free software is highly visible, it is in fact only one example of a much broader social-economic phenomenon. I suggest that we are seeing is the broad and deep emergence of a new, third mode of production in the digitally networked environment. I call this mode "commons-based peer-production," to distinguish it from the property- and contract-based models of firms and markets. Its central characteristic is that groups of individuals successfully collaborate on large-scale projects following a diverse cluster of motivational drives and social signals, rather than either market prices or managerial commands."

Paper also available from this page. '

Friday, September 06, 2013

Nokia revisited

Following up the earlier post, Surowiecki looks at what went wrong.  Where Nokia Went Wrong : The New Yorker:  Inability to take risk?

" Diverting a lot of resources into a high-end, low-volume business (which is what the touch-screen smartphone business was in 2007) would have looked risky. In that sense, Nokia’s failure resulted at least in part from an institutional reluctance to transition into a new era"

A new company can take a chance on a new area with less risk because you do not add on losing what you already have to the inherent uncertainty of any such business decision.  Breaking into the market involves taking risk.  There were similar potential benefits and much fewer costs for Apple.

Thursday, September 05, 2013

IS-LM and bank lending

An old one but a golden look at IS-LM from Brad DeLong

S(Y - T) = BL(i; π, ρ)

with rho the parameter for the risk premium in the original. However, this could also be risk aversion and therefore bank lending (BL) could fail to expand to match savings S or may expand beyond savings in a boom.

The point made here is that the government borrowing (via bond market) increases the quality of borrowing.

Wednesday, September 04, 2013

Decline and fall

Alongside some criticism of the Microsoft acquisition of Nokia and a view of the increase in Microsoft employees, Felix Salmon quotes The New York Times. 

 "Nokia’s fall has been most spectacular in Asia, a region that its phones once dominated. As recently as 2010, the company had a 64 percent share of the smartphone market in China, according to Canalys, a research firm. By the first half of this year, that had plunged to 1 percent"

The potential for large companies to disappear is much greater than is ever envisaged.  Remember that Nikia and Microsoft were one the Apple and Google of the day.  QZ has a overview of some of the things that Microsoft is said to have done as a word of warning for Google.

Monday, September 02, 2013

Bayesian vs. Frequentist in PracticeEran Raviv

Bayesian vs. Frequentist in PracticeEran Raviv: "I apply both Bootstrap and Bayesian inference in the following toy example and push forward the point that (Oy vey..) the choice between the two approached does not matter much."

'via Blog this'

Pricing of skew risk

Todd Mitton and Keith Vorkink suggest that diversified firms have to offer higher expected returns to compensate for the lack of positive skew.  This is tied to the well known phenomenon of individuals buying lotteries with a small chance of making large gains. 

In this paper, we seek to add to our understanding of why firms with diversification
discounts have higher expected returns. We consider an explanation based on the return
distributions of the stocks of diversified firms relative to single-segment firms. Specifically,
we consider whether investors pay a premium for single-segment firms because the return
distributions of single-segment firms have higher upside potential (positive skewness) than do
the return distributions of diversified firms. If investors have a preference for stocks with
positive skewness, then stocks of diversified firms may have to offer higher returns in order to
compensate investors for a lack of upside potential.
The assumption that investors would place a premium on stocks with greater skewness
exposure is grounded in theory. Arditti (1967) and Scott and Horvath (1980) demonstrate
that investors with typical preferences demonstrate a preference for positive skewness in return
distributions. Kraus and Litzenberger (1976) and Harvey and Siddique (2000) build on these
results to develop asset pricing relationships in a representative agent framework, finding that
an asset’s coskewness with the market portfolio should be priced. Other research shows that
even idiosyncratic skewness may be a priced component of stock returns. Barberis and Huang
(2005) show that when investors have preferences based on cumulative prospect theory, stocks with greater idiosyncratic skewness may command a pricing premium. Mitton and Vorkink (2006), in a model incorporating heterogeneous investor preference for skewness, also predict a pricing premium for stocks with idiosyncratic skewness. The optimal expectations model of Brunnermeier and Parker (2005) also produces qualitatively similar asset pricing implications for skewness as Barberis and Huang (2005) and Mitton and Vorkink (2006).

This seems to be about positive skew.  What does this mean for negative skew.  The obvious implication would be that investors would be more cautious about investments that have a negative skew. However, work on the carry trade suggests that they do not fully take notice of this risk.  Is this a case where risk is considered in an asymmetric fashion?  Could it be considered something akin or equivalent to Prospect Theory. 

Milton, T., & K. Vorkink, 2010, 'Why do firms with diversification discounts have higher expected returns?', Journal of Financial and Quantitative Analysis, 45 (6), pp. 1367-1390