Quant is dead. Long live quant. Outperformance by GOOD systematic managers has been a consistent feature of skill-based alpha capture for over 250 years. However the area is highly kurtotic. 1% of quant strategies are good. 99% of quant strategies, including “passive” index tracking, are bad. There is no “average” quant fund.
Hedge fund geeks bearing greeks? Some models don’t work so all models don’t work? Quant is complicated so stay with “simple” beta? Humans do the programming at quant funds so it is their sagacity or stupidity driving results. It depends on the questions people ask their computers. If you input wrong questions with wrong assumptions then the “answers” will be wrong.
Computers can monitor the world 24/7/365. Their loyalty is total and they don’t lose interest after buying the yacht, the Ferrari and the IPO for “permanent” capital. Computers analyze and react to information on a 100,000 securities in microseconds unlike a dumb, slow, sub 300 IQ human trader. Silicon-based managers can analyze new data, have the order in and executed before a carbon-based life form has even noticed. Are humans even good enough to make investment decisions nowadays? Let me know if you find one.
Computers also don’t think they are “star traders” when they fluke a lucky profit. They don’t take lunch, vacations or calls from search firms. They don’t quit and try to set up their own fund with proprietary information and clients from their current employer. They don’t have clandestine meetings with competitors. They don’t complain about colleagues, clients or bonuses. They don’t get sick or crash cars. There is a lot in favor of purely systematic strategies if they are good. It is amazing so many investors still avoid them.
Pablo Picasso once said “Computers are useless. They can only give you answers.” I consider quant strategies, including managed futures CTAs, equity market neutral, statistical and volatility arbitrage and high frequency trading strategies to be essential to include in ALL portfolios. The fact that they take rare skill and superior PROPRIETARY mathematical models to implement should not scare away any investor. Quant funds are “finished”? Nonsense.
I’ve heard 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 having problems. Implosions by “geniuses” include portfolio “insurance” of 1987, the mortgage-backed securities “models” of 1994 or Long-Term Capital Management’s options mispricing “Nobels” in 1998. Just as there are good and bad human stockpickers there are good and bad human quants. Show me a fundamental strategy that hasn’t run into a deep drawdown at some point.
The biggest risk taken by investors is KEY PERSON risk. A critical due diligence issue is human assets go home each day. Investing requires process rigor and eliminating emotion. I’ve invested successfully in thousands of securities and hundreds of funds for a long period of time. But I am not smart or quick enough to make investment decisions. My colleagues, silicon-based life forms Hitachi-san and Toshiba-san, were responsible for all the alpha we produced over the years. All I did was the programming. They did the work.
Due diligence on a quant strategy is EASIER than evaluating a fundamental strategy. I don’t need to see formulae or resumes to determine if someone knows what they are doing. Computers are just an analytical and execution tool that every good fund manager uses to varying degrees. The darkest, least reliable and most misunderstood black box is the EMOTIONAL and therefore inconsistent human brain.
Hyperbole and generalization often lead to misunderstanding. Presumably that is why some financial “professionals” think quants are in a quagmire, derivatives are dangerous and leverage is lunacy. There are far more bad qualitative hedge funds than bad quantitative hedge funds so beware of the QUALIS as much as the QUANTS.
Some multistrategy hedge funds that couldn’t unwind illiquid credit instruments were forced to unwind what was liquid to meet margin calls. The risk of mixing liquid and illiquid securities is one reason why there will ALWAYS be demand for single-strategy hedge funds despite the “expert” predictions for the dominance of multistrategy funds.
The situation was also exacerbated by rookie short sellers from the 130/30 newbie crowd panicking when their short positions began to tick up on the short covering. I wonder how many of them knew beforehand that short positions get LARGER as you lose money. I wouldn’t be surprised if some of the less experienced 130/30 managers were temporarily more like 120/40 or even 110/50. Mandate infraction! NEVER let a formerly long only asset gatherer learn shorting on YOUR or your pension funds’ nickel.
Funds of hedge funds that avoid all quant strategies are wrong. Intermediaries should earn their fees by identifying skilled and the unskilled managers NOT eschewing systematic strategies period. Process driven investment decisions are the foundation of EVERY successful fund. Given the large amounts of data that silicon-based computers can analyse if their carbon-based masters deign to provide it, it makes sense to outsource such work to them. If the strategy blows up, is the hammer or the handyman to blame? Computers are just a dumb tool that simply follows what a HUMAN decides to input and analyze.
Even odder are those investors who make an allocation to “quant” and then refrain from other quant funds. As if all quant strategies were the same! Investing successfully is hard. It makes sense to use every available tool. A systematic, replicable 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 specification. Garbage in, garbage out or genius in, genius out.
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 as there are delusional pricing models but that does not preclude the existence of robust quant fund 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.
With quant funds it comes down to the fear of the unknown and 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 will understand 3) other quants will copy or steal it leading to the inefficiency disappearing and trade crowding problems. The 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 or tweaking to “new” phenomena then it wasn’t properly tested in the first place.
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 automatic execution and market-making technology 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 in such strategies will miss opportunities. With high frequency the speed of execution and reduction of slippage often is THE profit driver. People wonder how Renaissance Technologies’ Medallion Fund performs so well but it is clear where its competitive TECHNOLOGY advantages are.
Just because public domain quant strategies using the same methodologies will identify the same ideas and opportunities does not invalidate other proprietary methods. The models that ran into trouble – 1) find two paired stocks historically cointegrated and take the other side when they are X sigma apart or 2) throw every fundamental and technical variable you can find into the hopper and data dredge for patterns that worked in the past – are now very crowded. Apart from some now very large hedge funds – a few good, most bad, there were investment bank proprietary desks heavily in the statistical arbitrage and factor model strategies using higher leverage.
Models are only as good as the assumptions humans give them and the programmer’s representation of reality. Unfortunately interpreting reality is rather complicated. To put it mildly, the facts have not been kind to the theories. If you code up some VBA, 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 incorporate new information then you WILL run into trouble. The computer only knows things that YOU choose to let the program know about. If you lose money beyond statistical expectation then that is a human error NOT computer error.
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. Yet another example of semantic arbitrage, like calling something market neutral when it isn’t remotely market neutral. Humans using computing power allows detection of predictive structure and we’ve progressed far beyond moving averages, breakouts, candlesticks, RSI, MACD and Elliott waves. 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 countertrend. Are fundamental analysis and technical analysis that far apart or is it just a change of inputs to the model?
Computers are just a tool. Humans design “discretionary” investment strategies and they design “systematic” investment strategies. If they are good or bad is all up to humans. Whether they data mine the past or test hypotheses of the future is up to us. Computers are good at information processing but can only analyse the data they are given in the way their human masters 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 error propagation of non-linear orders of magnitude. Computers are simple creatures; if you only tell them about bell-curves and the “rarity” of six sigma moves then they are obviously not going to perform well when 25 sigma moves inevitably come along.
There are no axioms or proofs in real world markets. Asset classes don’t go to business school or finance class. A standard IQ test can be coached but the markets are an IQ test where the questions AND answers change while you are taking it! If you assume randomness and try to impose rationality on a deterministic, chaotic process like the markets then your models are wrong. Everything is connected so models that assume 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 risk factor world and whose underlying theory and equations have NEVER been published.
As simple fools, 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 models are wrong, that if A defaults then the chance of B and C also defaulting is MUCH higher than “assumed” 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 YOUR orders.
Correlation crisis? If quants neglect to tell their computers that if a weaker player 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 variable and can even vanish then whose fault is that omission? CDO mispricing was primarily based on the gruesome Gaussian copula model. Quick investment tip: never, EVER risk capital on any model that assumes a “Gaussian” world. 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. “Passive” index funds rely on the ludicrous random walk Brownian motion supposedly exhibited by stocks. There no limit to human stupidity,
Even if you buy into this “normal”, “independent” market prices nonsense, 95% VaR estimates mean that about 1 day every month on “average” you will lose more than that. 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 quiet times let alone market stress.
Strangely no heads have rolled at Goldman Sachs’ Global Negative Alpha “hedge fund” despite squandering over $3 billion of client money on its disastrous trading “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 losses were entirely due to human decisions and inexperience as are many of the yet to be announced severe credit drawdowns from other firms.
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 their controllers. 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.
Sports and investing are similar. Hard work, talent and coping with variables that a computer, with current technology, is not able to handle. We are a long way from artificially matching the kinesthetic intelligence of a basketball or soccer player. I saw a robot try to play tennis recently; it wasn’t very good. The intelligence necessary to master ball games is higher than that required for board games. Chess is easy but baseball and football require much higher intellect and computational prowess far beyond that dumb, clunky computer you are now looking at. Chess requires less brainpower than ice hockey or rugby, which both require faster data processing and analytics than ANY computer can currently achieve.
In financial markets computers are just an aid to human decision-making and will ONLY be that for a long time. Some humans create good pricing models and black box trading systems but other humans create bad ones. Due diligence on quali funds and quant funds, yes but avoid all of them? No.
SOURCE: Hedge fund – Read entire story here.