Academic Paper of the Day

On the dangers of equating uncertainties...something that I have discussed on this blog  previously.

Full paper here.

"The quantitative aspirations of economists and financial analysts have for many years been
based on the belief that it should be possible to build models of economic systems—and
financial markets in particular—that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, “physics envy” has
also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based
on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two
concrete examples: the classical harmonic oscillator with some new twists that make physics
look more like economics, and a quantitative equity market-neutral strategy. We conclude
by offering a new interpretation of tail events, proposing an “uncertainty checklist” with
which our taxonomy can be implemented, and considering the role that quants played in the
current financial crisis."

Excellent video...

...overviews the "quant bubble" currently expanding once again.

Quants: The Alchemists of Wall Street

Open systems, financial data, and algorithmic trading

Economic data suffers from a peculiar conundrum. Most of this data is collected in the form of asset prices at different points in time. Unfortunately, since time is embedded, the points of data cannot be described as neat fungible packets such as what we find in physics or chemistry...these fields have data that operates independently of historical human events and meddling.

This is a somewhat convoluted way of saying that it is difficult to distill historical social data without reference to the unique period of time from which that data is derived. This is why finance and investing is more art than science.

Unless, of course, you are an algorithmic trader. These quants scalp spreads and collect exchange rebates with efficiency that would alarm most casual observers. The art of parsing history and data is irrelevant to these traders, which makes them extremely attractive as investments given the expectation their returns would be uncorrelated with market fluctuations.

However, the profits that were there for easy taking in my opinion are eroding fast in this field. A flood of competition, trade secret expropriation (read: some guy steals your code and uses it against you) is producing the usual effects, and the "asset class" that is algorithmic trading is experiencing its own bubble which will end (through the abruptness of the guillotine via government regulation or by more mundane natural causes) soon.

The Quants are dead...long live The Quants

As I have alluded to before on this blog, the proliferation of cheap computational power and access to algorithmic trading platforms has changed trading, money management, and indeed global financial markets. Quantitative traders ("Quants") are simply not immune from the quotidian business processes of competition, maturity, and commoditization.

We have obvious evidence of the trajectory of firms that base their trading skills on computers and the fantastically intelligent people who program them. This paper by Andy Lo gives a nice explanation of what happened and why during the credit crunch of last year and how it effected hedge funds.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1015987

Many Hedge Funds are simple. They employ computers to search for anamolies in the markets (such as the NAV of closed-ended funds vs. book value, credit spreads of similar debt products, identical equity securities in different jurisdictions, and debt convertible into equity). This type of trade used to be wonderful for sophisticated Quants. Large, expensive overhead was needed to run the models, but profits where simply there for the taking.

Like all businesses that make overplus profits, competition formed. Associates of these funds left to set up their own Quant shops, using very, very similar models.

Soon, thousands of funds were using the same algorithms which searched for the same anomalies. Opportunities appeared and then vanished in seconds instead of minutes. Margins on trades shrank. The competitive advantage for most funds was speed.

The Quant space grew larger. Institutional funds gazed the low-volatility, non-correlated positive returns of Quants and thought "panacea", then invested heavily to seek more market anomalies. With more entrants and huge increases in assets under management, time and profits were both being crushed by the gravity of capital.

And so to combat the thin returns, leverage was employed. A tiny anomaly levered 30 or 40-1 was not so tiny anymore. Competitors copied this practice. Once again, thousands of funds were using leverage to trade on the same anomalies, and devil take the hindmost.

But leverage works both ways. A 1% decline in an investment levered 40-1 is painful. A 10% move in such an investment may require a letter of apology to your investors for losing their capital.

So, once volatility increased across the whole panoply of financial instruments, many, many funds and strategies would be unraveled.

The Victors of this process are those Quants that can maintain and intellectual AND technological advantage. There are always anomalies and strange correlations in the markets. Those funds that can be creative in their use of algorithms, ask the market the most intelligent questions (based on a more scientific curiosity), and have the most sophisticated software will have the lion's share of success going forward.

So, in a certain sense, Quants as we knew them are dead. Long live the Quants.

The convergence of "risk".

"Convergence" has long been talked about in terms of ReInsurance and capital markets...an insurance contract is just a binary put option, for example. A collection of these options will likely converge (Central Limit Theorem) into stable normal distributions. But notice in the article how local (micro) knowledge regarding specific ("idiosyncratic" in the finance lexicon) risks can obviate the need for assessing risk as a truly random variable in a population.

Here is a pretty good (albeit general) discussion of how Reinsurers think about risk, it is not exhaustive by any means, but it does illustrate the fact that "risk" is not "beta" or sigma/SDEV or any single measure. "Risk" has many origins. We just choose to distill its antecedents into something more tractable.

clicky: http://tinyurl.com/2h5akb


interesting section:

"So I think volatility and liquidity concepts are concepts applicable to low-risk, relatively low-return risk classes. Casualties, reinsurance and Cat reinsurance are high-return, high-risk classes. And they demand a different set of technical tools and methodologies to accurately assess the risk and actually -- accurately price the risk.

So when I look at investment banks and I look at VAR, value at risk, which is a daily measure, and I look at [reinsurers], which is capital at risk, which is an annual measure, one measures daily volatility. One measures downside risk. I think, again, for those risks which are long term in nature, which are difficult, which are severe, you're much better off with the CAR concept than you are with the VAR concept.

We have within the organizations, all the reinsurers represented today, we have great quantitative skills. Our actuaries are as smart as the quants in many cases, in all cases. At a number of our capital markets competitors, when a math major leaves college, it can go two ways. They can either go become a quant on Wall Street, or they can become an actuary for the insurance or reinsurance industry. I haven't been able to see any kind of difference in terms of the capabilities of the people who take either one of the two paths. They tend to be very similar.

I think the difference is, is that while the math is generally the same between capital markets and reinsurers, I think the atmosphere within which they work is distinctly different. There is a long culture within insurance and reinsurance companies of asking the actuary to come up and give you the long -- the bad answer, the answer you don't want. The worst thing as a CEO is when the actuary walks into your office and says can I talk to you.

I'm not sure that that always happens sometimes on the capital markets side, where in fact it is difficult to see how the actuary or the quant has the ability to withstand the blandishments sometimes perhaps of the trader. In our shop and in most reinsurance shops, the actuary and the underwriter are coequal in terms of their analysis and coequal in terms of their authority to put the Company at risk."