What is really interesting about Quantitative Behavioral Finance

photo by Stuck in Customs	via PhotoRee

Quantitative behavioral finance has not been with out for a very long time. As a relatively recent development and area of discourse, it has only begun to gain a following. One very interesting aspect of this field is the use of experimental asset markets. These studies are based on experiments conducted on a small group of people (but with real money hence a real market) to examine where rational expectations and classical game theory fails to explain human behavior. This is basically small-scale version of the prediction markets such as the Iowa Electronic Markets, Intrade, and Betfair. However, unlike prediction markets where the ultimate objective is to predict an external event, experimental asset markets are more interested in the mechanics and patterns of the market itself. Caginalp, Vernon Smith (Nobel Memorial Economics Prize recipient of 2002), and David Porter have a couple of papers on experiments in this mode. Both experiments examine how financial bubbles can happen. Some of the take-aways are that excess cash and information asymmetry due to lack of an open book may exacerbate bubbles. I think the matter of information asymmetry is very salient. Despite all the effort and money invested in improving information infrastructure by banks and hedge funds, ultimately information is distributed non-uniformly to market participants. This is most obvious in the case of the retail investor who neither has the time nor resources to obtain and analyze all the market information.

Max Drawdown

When it comes down to it, finance is about risk and return. One of the purest measures of risk is max drawdown. This is pretty much the worst-case scenario: one bought at a peak (not necessarily the absolute peak) and a trough (not necessarily the absolute trough) such that the drawdown is maximum, i.e., you have maximum loss. I was curious, if one takes a look at the S&P 500 components from max drawdown only, what does it look like? The time period for this simple study spans 5 years from today, February 28, 2013. Choosing a 5 year period does unfortunately eliminate 25 of the S&P 500 right off the bat due to those symbols not having data running that far back.

S&P component	Max drawdown (5 yrs)	Std Dev (daily return)
AZO	0.131292	0.179530
MCD	0.154924	0.141842
GWW	0.191437	0.190390
ABC	0.196378	0.275510
SRCL	0.214233	0.210143
CERN	0.217581	0.338044
WEC	0.232936	0.133349
DTV	0.235117	0.235420
XEL	0.242985	0.103072
FDO	0.246084	0.184564

The above table shows the 10 components with the least max drawdown over the period. An interesting variety of businesses are represented here. It isn't just a list of defensive utilities or consumer staples. We have automotive parts retail, the Golden Arches, industrial equipment, a pharmaceutical, a pharmaceutical waste disposal, healthcare IT, two utilities, and a discount retailer. Note that although these names tend to be low beta, they are hardly low PE. Note that having the least max drawdown doesn't necessarily imply having the smallest standard deviation of daily returns.

S&P component	Max drawdown (5 yrs)
BAC	0.921133
XL	0.925845
GCI	0.932099
TXT	0.940085
HIG	0.950989
FITB	0.955382
C	0.960199
FSLR	0.962171
GNW	0.964527
AIG	0.992874

This second table shows the 10 components with the greatest max drawdowns. This list is certainly more concentrated than the first table. All except for 3 are financials/insurers/reinsurers. We have the old guard news media represented by Gannett (GCI), aerospace/defense by Textron (TXT), and renewable energy by First Solar (FSLR). If risk averseness is the game, then drawdowns is quite an interesting measure. It shows the worst-case for a single roundtrip trade. Note that trading results may be much, much worse than the max drawdown if a strategy gets repeatedly drawn down in a losing streak. Nevertheless, max drawdown gives some insight into the amount of risk we are taking on. In particular, max drawdown should give some insight into what kind of reserves should be kept. This, of course, isn't foolproof since past drawdowns do not necessarily predict future drawdowns.

Another interesting aspect of max drawdown is that there is really no relationship between max drawdown and daily returns. A regression between the two shows an R² of a mere 0.0027.

Finance Research Papers

Here are some papers of interest I ran into recently:

Finance Research Papers

Here are a few hot-off-the-press research abstracts which caught my eye:

• Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World studies news sentiment-based high-frequency trading using an extension of Hasbrouck's market microstructure model (and Chaboud and Tookes) and NASDAQ high-frequency data from 2008 to 2009. The paper seeks to answer whether we can effectively do machine-driven trading based on news sentiment and whether speed matters. The author distinguishes between "hard" and "soft" information events where hard events are price data and soft events news ticker items, blog posts, and Twitter messages (though this paper focuses on news ticker alone). This study only uses Reuters News Sentiment software. The conclusion is that at this point, non-high frequency traders have an advantage in processing the "soft" information events. I think this area is gaining quite a bit of attention with both bigger firms (Thomas Reuters, Dow Jones) and smaller ones (a startup named Stockr.com (invite link) for example) are investing in news sentiment and social media-based trading (respectively).
• Market Making Under the Proposed Volcker Rule
• No Strings Attached: When Giving it Away Versus Making Them Pay Leads to Negative Net Benefit Perceptions in Online Exchanges

Algorithmic Finance

There is a brand new journal combining Computer Science and Finance, Journal of Algorithmic Finance. Both the advisory board and editorial team are ensemble casts with some of the biggest names in computer science and finance. They just published their inaugural issue in December 2011. The top paper (as indicated by number of SSRN downloads) is one on Markets are Efficient if and Only if P = NP, which perhaps is very reassuring to all the traders out there, at least at a theoretical level. For those unfamiliar with the terminology, P means solvable in polynomial time (i.e., we have reasonably smart, efficient algorithms to solve them) and NP means verifiable in polynomial time (i.e., given a solution, we can verify that it works efficiently). NP-complete is a class of problems such that all problems in NP can be (efficiently) reframed in terms of any NP-complete problem. For problems in NP but not in P, we generally do not have efficient algorithms for exact solutions. Instead, we pretty much have to fall back on exhaustive search (i.e., try all the possible answers and see what works). If one can solve an NP-complete problem efficiently in the general case, then one can solve any problem in NP efficiently, which potentially saves some astounding sum of money and enables a lot of previously impractical technologies. The more interesting implication of the paper is that the market can serve as an oracle to solve NP-complete problems. Recently DARPA has decided to invest in crowd-sourcing and gamification to solve difficult formal verification problems. Instead of potentially awkward game embeddings of difficult computational problems, I wonder if market strategies may be more effective and direct.

Top 5 Open Source Quant Finance Projects

In the recent months, I have found the data on open source development projects to be quite fascinating. It turns out that most of the activity in open source quant finance code happens on Sourceforge. I pulled some of that data and charted them.