Maths to Money – Quantitative Investment
December 5, 2009
I attended the Quant Invest 2009 event for the first time last week in Paris. The event is unsurprisingly about quantitative investment strategies, but with an institutional asset manager and hedge fund focus – so not so much about ultra-high frequency trading (although some present) but more about using quantitative techniques to manage medium/longer-term investment decisions and applied portfolio theory. A few highlights below that I found interesting:
- Pierre Guilleman of Swiss Life Asset Management gave an interesting 1/2 day workshop entitled "A random walk through models":
- He is a strong supporter of the need to understand more about the data and statistical assumptions upon which any quant investment model is based and how these fit with the desired investment objectives (similar to the Modeler's Manifesto)
- He made the point that good models can sometimes be almost annoyingly simple, and cited the example by a Professor Fair of Yale who had determined that US elections were predictable based on simple parameters such as past results, inflation and gdp and that policy did not seem to be a key factor at all - annoying for the politicians anyway!
- Pierre seems very concerned that the Solvency II regulation applied to Life Institutions will negatively influence the investment policies of many institutions – applying sell-side risk measures like VAR to the insurance industry will drive a more short-term approach to investment. He strongly believes that VAR applied to his industry should have an expected return parameter introduced to fit with longer term investment horizons of 10 to 25 years.
- Bob Litterman of Goldman Sachs Asset Management opened the first "official" day of the conference:
- Bob put forward his "scientific" approach to investment modelling going through the stages of hypothesis, test and implement. He warned against overconfidence in investment (apparently 70% of us think we are "above average"…) and impulsiveness (quick impulsiveness test: "if a bat costs $1 more than the ball, and the bat and ball together cost $1.10 then how much does the bat cost?…")
- He said that the failure of quantitative investment models in 2007 needed to be understood given the success of quant models over past decades. In particular he thought that quant investment became the "crowded trade" of 2007 with every hedge fund having a quant investment strategy. In terms of why this became a "crowded trade" Bob thinks that the barriers to entry into quant investment (particularly technology) have lowered significantly recently.
- He noted that factor-based investment opportunities decay quicker than they used to due to increased competition - implying the need for a more dynamic and opportunistic investment approach.
- GSAM are now looking at new markets and new investment instruments, trying to find areas of market disruption but without following what others are doing in the market.
- He pointed out the conflict between investors wanting more transparency over what is done for them, against the need to be more proprietary about the investment models developed.
- Next there was a talk on regulation from the French regulator that was dull, dull, dull both in terms of content and presentation style (when will regulators actually prepare well for the talks they give?)
- Panel debate was also pretty average, with the word "alpha" being used too much in my view – asset managers of a certain type seem to hide behind this word as an opaque "magic wand" to justify what they do.
- Jean-Phillippe Bouchard of Capital Fund Management did a great talk called "Why do Prices Move?". Some points from the talk:
- He started off with a reminder about the Efficient Markets Hypothesis (EMH) and how it says that crashes and market movements are caused by events outside (endogenous to) the market such as news, events etc.
- He then said this was not born out in the data, where extreme jumps in prices were only related to news only 5% of the time.
- Volatility looks like a long memory process with clustering of vol over time – similar to behaviour in complex systems
- The sign of order flow is predictable but the price movement is not, with only 1% of daily order volume accounting for price movements over 5%
- Even very liquid stocks have low immediate liquidity, meaning that price movements can play out over many hours and days as liquidity is sought to "play-out" some change in fundamental price levels.
- Joseph Masri of the Canadian Pension Plan Investment Board then did a good talk on Risk Management:
- Jo said that sell-side risk was easier to deal with in some ways since it involved fewer strategies in high volumes, and hence could be better resourced.
- Buy-side quantitative risk was harder due to its reliance onsell-side research and risk tools, the outsourcing of credit assessment to the credit rating agencies, the loss of Bear and Lehman's having caused the buy-side to have to do more risk management itself (and through third parties) rather than rely on the sell side risk management tools.
- He said that sell-side risk models are a good start for an asset manager, but need to be adapted to give both absolute and relative risk (to a benchmark fund for instance). All models are no substitute for risk governance.
- He described the cross over from risk methods: VAR, stress testing, factor-based and their applicability to market risk, credit and counterparty risk.
- Like Pierre he was not a fan of 1 or 10 day trading VAR being applied to investment managers since this risk measure was not suitable for long term investment in his view.
- On stress testing he said this needed to be top down (using historical events etc) as well as bottom up from knowing the detail of strategy/portfolio.
- In terms of challenges in risk management he said that VAR needed more stress testing to cope with the fat tails effect in markets, that liquidity risk both of counterparties and of illiquid products was vital and the importance of stress testing (he mentioned reverse stress testing) plus also the feedback (crowding effects) of having similar investment strategies to others in the market.
- Dale Gray of the IMF gave a very interesting talk on how he and Bob Merton have been applying the contingent claims model of a company (looking at equity in terms of option payoffs for shareholders and bondholders) to whole economies:
- He said that some of his work was being applied to produce a model for the pricing of the implicit guarantees offered by governments to banks
- He said these models were also applicable to macro-prudential risk
- Very interesting talk, and if he really has something of macro-level risk then this is great relative to the wooly approach by the regulators so far
There were some other good talks from Danielle Bernardi on Behavioural Finance, Martin Martens on Fixed Income Quant Investment, Vassilios Papathanakos on Stochastic Portfolio Theory (seemed to be a "holy grail" of investment model, giving good returns even in the crisis – begs the question why he is telling everyone about it?), Claudio Albanese on unified derivative pricing/calibration across all markets (again another "holy grail" worth more investigation) and Terry Lyons on speeding up monte carlo simulations.
Overall a good conference although the quality of the asset managers present seemed very digital from those who really seemed to know what they talking about to those who plainly did not (in my limited view!). Along this line of thought, I think it be good to test whether there is an inverse relationship between the quality of the asset manager and the amount of times they use the word "alpha" to explain what they are doing…