Brett Hutley's Blog » finance

The Weirdness of Equity Markets

brett — Thu, 10 Nov 2011 14:04:07 +0000

Equity markets constantly surprise me. It's looking increasingly likely that the Eurozone will disintegrate - leading to potential bank failures, stagnant economic growth and increased unemployment - and the equity markets here in Europe are pretty much shrugging it off today. The FTSE100 is up over half-a-percent at pixel time.

It's like the equity participants are saying to themselves "Well, we had a down day yesterday, so it must have been oversold, right?". During the last financial crisis it was like that as well. There would be a big fall one day, and then the stocks would rally somewhat, and then another big fall... rinse and repeat.

Maybe everyone is afraid of missing out on the bottom. Don't worry - I think it'll be a while before you have to worry about that.

Estimating correlation with missing data

brett — Wed, 27 Oct 2010 14:25:59 +0000

There's a great article here on estimating correlations and means when you have missing data in your datasets. It uses the Expectation Maximisation algorithm to calculate the missing values, and what is interesting is how much the implied correlation changes after applying EM.

Evaluating Bio-Techs

brett — Fri, 13 Nov 2009 11:15:49 +0000

Clinton Chee has posted a list of factors he thinks is important when analyzing Bio-Tech stocks. This list is summarized below:

P : Price of stock
NTA : Net Tangible Assets (Total Assets minus Intellectual property and other intangibles)
P/NTA : Ratio of Price to NTA - a value of 3 or below is considered underpriced and worth buying. A value of 9 or above is definitely not worth buying
TEAM : Rating system based on the qualifications of the directors
1. Professor = 1.5
2. PhD = 1.0
3. Master = 0.8
4. Hons = 0.7
5. MBA = 0.6
6. Biotech Exp (10year) = 0.5
BurnPeriod (Runway) : the number of quarters left before cash runs out, based on the previous quarters cash burn rate. Over 4 quarters is fair. Over 8 quarters is worth investing.
ProductPipe : the stage of the product development ranging from pre-clinical tests to commercialization. Over a value of 5.0 is a good sign.
1. Phase 1 = 1
2. Phase 2 = 2
3. Phase 3 = 3
4. Market Approved = 4
5. In Market =5
ForeignMarket : Companies with products that sell internationally are more highly rated. (see key below). Over a value of 3 is a good sign.
1. Plan to Enter = 0.5
2. Just Entered = 0.9
3. 1-3 years = 1.5
4. Over 3 years = 2.0
Cash/Debt : the ratio of cash to debt of the company.

Financial models need more complexity?

How the Market Cap of Financial Firms has changed

brett — Mon, 14 Sep 2009 16:23:33 +0000

The New York Times has a great interactive graphic on How the Giants of Finance Shrunk, then Grew, Under the Financial Crisis. It's really interesting seeing how, if the Market Capitalization of each firm is represented as an area, the each firm shrinks massively during the financial crisis, and now how the firms are rebounding.

Financial models need more complexity?

brett — Mon, 14 Sep 2009 09:23:33 +0000

A post over at the New York Times is arguing that one of the main causes of the financial crisis was inadequate quantitative models - models that tended to understate risk because they failed to provide a realistic model of the way the world works - neither incorporating risks such as a failure of liquidity, nor the complexities of human behaviour.

I certainly agree that the current stable of models which are in widespread use are inadequate given that the competitive market has made the spreads on trades so tight that there is no longer any buffer to cover the many short-falls in the models. Back when vanilla options were an exotic trade, the trader would incorporate plenty of fat in their options trades. Intense competition, a market that has steadily grown over the past 20 years (notwithstanding small glitches), and increased familiarity with the trades has served to camouflage the risks the traders were running in their options books.

Quantitative Finance is a very young science. I remember back in the early 90's; the Black-Scholes model was the height of financial sophistication. I did not start working with finite difference methods for valuing American Equity Options until 1994. It was after Barings collapsed in 1995 that regulators started forcing banks to calculate VaR figures and allocate capital based on their calculated risk numbers. How far we have come in the last 20 years!

Of course, nearly all the models created in the early periods of finance have been based on the assumption that returns are normally distributed. Unfortunately this assumption does not correspond to observed reality. Attempts that DO try to create models that reflect the observed distribution lead to models that incorporate jumps, or contain stochastic volatility. Once we lose the normal distribution of returns assumption, we also have problems with correlation as an adequate measure of the relationship between the market factors. Suddenly we are in a whole world of quantitative pain.

There are also the practicalities of having neither the sheer computational power nor the software to incorporate the latest financial models. For some reason, the models currently in widespread use are not the most sophisticated models, but those that strain the limits of computation, software and trader/quant understanding.

It's interesting to contemplate how hard finance will be with far more complex models. We are currently farming out our risk calculations onto large grids of computers. Advances in leveraging graphics cards to create cheap super-computers are arriving just in time, and are a hot topic in finance. The traders are demanding real-time risk figures (and so they should), and yet it is already hard to reliably provide these numbers with the simple models in vogue. It will be much, much harder as models increase in complexity.