By Veryan Allen | 3 November 2007
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I've heard so many times that quant investing will replace humans but conversely I am hearing, yet again, that "This is the end of quant"! Both are wrong. There is nothing new about BAD quantitative models running into problems. Recent events are similar to portfolio "insurance" of 1987, the mortgage-backed securities pricing "models" of 1994 or LTCM's option pricing "geniuses" in 1998. Just as there are good and bad human stock-pickers, there are good and bad human quants. Show me a human based 'investment strategy' that hasn't also run into problems at some time. The quants are dead— long live the quants.
Investing successfully is hard. It makes sense to use all available tools. A systematic, replicable [[tuneable: normxxx]] investment process using qualitative AND quantitative analysis is surely the foundation of any successful hedge fund, though how they weight the two varies. The simple fact is there are GOOD pricing and trading models around and there are BAD ones. It usually takes bear markets and volatility to show which is which. But whether the models produce positive or negative alpha is ENTIRELY up to human input and human specification. Garbage in, garbage out or quality in, quality out.
I think investors should be wary of everything. Considering the non-quant problems and dire risk management policies on display recently, the faith in the value of human discretion seems ironic. Sure there are plenty of poorly designed and badly tested quant trading systems out there and there are delusional pricing models, but that does not preclude the existence of quality, robust products. A computer making the trading decisions rather than a human does NOT mean an increase in systemic risk or a decrease in the persistence of a good strategy. It just puts the emphasis on ensuring the computer is making decisions in a different way [than] other computers.
One of the biggest risks for any fund and a critical issue for any allocator is that the human assets walk out of the door each day. Computers can monitor the world 24/7/365. Their loyalty is absolute and they don't lose interest after the IPO. Computers can absorb and react to information on 100,000 securities instantly, unlike a human trader. They can analyse new data, have the order in and executed, before a human has even noticed the information. Computers also don't think they are a genius when they fluke a lucky trade. They don't take lunch or vacations. They don't quit and try to set up their own fund with proprietary information. They don't have clandestine meetings with competitors. They don't complain about colleagues, clients or bonuses. They don't get sick or crash their Porsches. And once the programming and testing is done you have eliminated key-person risk. There is a lot in favor of purely systematic strategies IF they are good.
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Why the fuss about "ALL" quant funds? It comes down to the fear of the unknown and traditional hatred of opacity. Discretionary investors can be reasonably open about how they pick stocks since the edge is the skill in implementing the strategy. Good systematic strategy developers cannot be so open since 1) the edge is the strategy 2) no-one outside quant land will understand 3) those inside quant land will steal it— leading to the [detected] inefficiency disappearing and trade crowding problems. The main distinction comes down to whether the human decides or the computer, programmed by humans, decides. But is that really a distinction? If a systematic trading model needs adjusting to "new" phenomena then it wasn't properly tested in the first place.
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Any successful investment strategy needs a robust decision-making framework and elimination of emotions. The best way is a division of labor between humans that are good at gathering data and machines that are good at processing that data in the way a human asks them to do. The more short term the trading the more useful artificial intelligence is going to be. It makes sense that PROVIDED the algorithm has been put together competently to ask the computer to trade IF time is of the essence. High frequency trading is very dependent on low latency and incorporating a human override slows things down. With high frequency, the speed of execution and reduction of slippage often is THE primary profit driver.
Just because public domain quant strategies using the same methodologies will identify the same stories and opportunities does not invalidate other proprietary methods. The models that ran [sic] into trouble— either 1) find pairs of stocks historically cointegrated and take the other side when they are X sigma apart or 2) throw every fundamental and technical variable you can think of into the hopper and data mine for what patterns worked in the past— are now very crowded. Apart from some now very large hedge funds (a few good, many bad), there were investment bank proprietary desks heavily in the statistical arbitrage and factor model strategies.
Some multistrategy hedge funds that couldn't unwind illiquid credit instruments were forced to unwind what was liquid to meet margin calls. The risks of comingling liquid and illiquid securities is one reason why I think there will always be demand for single-strategy hedge funds, despite all the "expert" predictions. The situation was also exacerbated by some of the 130/30 crowd panicking when their shorts began to tick up on all the short covering. I wonder how many of them knew beforehand that short positions get bigger as you lose money. I wouldn't be surprised if some of the less experienced 130/30 entrants were temporarily more like 120/40 or even 110/50 in early August.
Models are only as good as the assumptions humans give them and the programmer's [[modeler's?: normxxx]] representation of reality. Unfortunately reality is rather complicated. To put it mildly the facts have not been kind to the theories [[or theorists: normxxx]]. If you code up some C++ or C# and tell the computer that we live in a nice "normal", "standard" world of rational entities that spend their days maximizing their utility and immediately changing prices accurately to [accomodate] new information, then you will run into trouble. The computer only knows things that YOU choose to let the computer know about. If you lose money beyond statistical expectation then that is a human error, not computer error. Try typing =850*77.1 into Microsoft Excel 2007. Is it a human design error or the computer's fault if you get 100,000 instead of the correct 65,535? No computer would [[should?: normxxx]] have "valued" Facebook at $15 billion either.
However gatekeepers who avoid ALL quant strategies are doing a very poor job for their clients. Intermediaries should earn their fees by identifying the good and bad managers in a strategy NOT avoiding it altogether. Process driven investment decisions are the foundation of EVERY robust strategy. Given the large amounts of data that silicon based computers can analyse that their carbon based masters decide to provide, it makes sense to outsource such work to them. Even odder are those investors who make an allocation to "quant" and then refrain from any other quant funds. As if all quant strategies were the same!
Computers are just a cool tool. Humans design "discretionary" investment strategies and they design "systematic" investment strategies. If they are good or bad is all up to human ingenuity or human stupidity. Whether they data mine the past or test hypotheses of the future is up to human skill. Computers are good at information processing but can only analyse the data they are given in the way humans designate. Quantitative risk management is only possible based on the factors input to the system; if the machine is blind to a new factor there will be errors often of a non-linear order of magnitude. Computers are simple creatures; if you only tell them about bell-curves and the "rarity" of 3 sigma moves, then they obviously are not going to perform very well when 25 sigma moves come along.
There are no axioms or proofs in real world markets. Asset classes don't read the textbooks unfortunately. An IQ test can be coached, but the market is an IQ test where the questions and answers change while you are taking it. If you assume randomness and rationality on a deterministic chaotic process like the markets then your models are going to be wrong. As we have seen recently everything is connected, so therefore models that rely on independence are headed for trouble. A good model is one that provides a persistent trading or pricing edge, can cope with a non-linear, dependent, varying factor world and whose underlying theory and equations have NEVER been published. Some quants like to use something called "stochastic calculus" which is useful for many things but certainly NOT financial modelling.
As simple tools computers are not good at complex event analysis because most programming hasn't focused on that area. Unless its human owner has informed it that most CDO pricing is wrong, that if Andy defaults then the chance of Bob and Chuck also defaulting is much higher than the credit models "imply", and that there are a bunch of other people out there running very similar equity mean reversion programs, then the model won't pick up that maybe it should change things. It just follows orders.
If quants neglect to tell their computers that if a weaker player with a similar model is forced to unwind then the opposite of what "should" happen might occur, then that is also human error. If the computer doesn't know that liquidity is very variable and can even evaporate then whose fault is that omission? CDO and CLO mispricing was primarily based on the Gaussian copula model. Quick investment tip: never, ever risk money on anything with the word "Gaussian" in it. Gaussian things make the mathematics easy which is why they don't work. Bank CEOs might bear that in mind; there are quite a few careers still being bet on the multivariate normal curve.
Even if you buy into the "normal" nonsense, 95% VaR estimates mean that about 1 day every month on "average" you will lose more. While $480 million losses may look bad, on $10 billion notional it is only 4.8%. If the Morgan Stanley quants made the human decision to run $2 billion notional cash at 5x leverage, losses of that magnitude, while serious, are not beyond the realms of expectation. The valuation noise on large portfolios is going to be tens or hundreds of millions even in relatively quiet times let alone [during] market stress. Strangely no heads have rolled, yet, at Goldman Sachs' Global Negative Alpha "hedge fund" despite squandering over $3 billion of client money on its disastrous and poorly designed factor "models". $8.4 billion losses, mostly from buying market share in CDOs and structured credit with little concept of risk or trading acumen, are another matter. The Merrill Lynch losses were due to human decisions.
Few investment managers admit to using that big institutional no-no called technical analysis despite the fact that so many do. But calling it quantitative analysis is still ok, just. Computing power allows detection of predictive patterns and structure; we've long ago moved on from public domain moving averages, breakouts, candlesticks, RSI and MACD. Technical analysts look at patterns of prices and volume while fundamental analysts look at patterns of earnings and book value. Growth investors are trend followers while value investors are basically countertrend. Are fundamental analysis and technical analysis that far apart or is it just a change of inputs to the models?
Computers are at the mercy of what data their programmers choose to give them. Even genetic algorithms and neural nets rely on the system constraints, parameters and data sets provided by humans. Computers have solved simple finite systems like a chess game because it is a closed and rational problem. There is always an optimal move in any situation. But financial markets are much more complex, require decision-making under uncertainty and the rules change WHILE you are playing.
I think sports and investing are similar. Hard work, talent and variables [are needed] that a robot, with current technology, is not going to be able to handle. We are a long time away from artificially matching the kinesthetic intelligence of a basketball or soccer player. The computation necessary to master ball games is far beyond that required for board games. In financial markets computers are just a basic aid to human decision-making and will ONLY be that for quite a while. Some humans create good pricing models and black box trading systems but other humans create bad ones. Due diligence on qual funds and quant funds, YES but totally avoid them, NO.
Normxxx
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