Friday, 29 January 2016

Computers vs Humans - considering the median


Or why you aren't, and will never be, John Paulson


The systematic versus discretionary trading argument is alive and well; or if you prefer, computers versus humans*. In this post I pose the question - who is better the average systematic trader, or the average discretionary human?

* Though even fully discretionary traders will be relying on a computer at some point; fully manual trading and back office settlement systems not available in modern market.

My new hedge fund will completely eschew computers... and casual dress (source: http://www.officemuseum.com)


I've thought, and spoken, about this quite a lot. Upfront I should say that there is no simple answer to this question. There are some extraordinary human traders; celebrities like Soros and Paulson. At the same time there are some incredibly successful systematic funds: high frequency firms like IMC and Virtu; also the likes of Renaissance and DE Shaw; and of course the systematic CTA business that I cut my teeth in of which Winton and AHL are just two examples.

But reciting this diverse group of household names is of no real help. It's the median that fascinates me.

Firstly as an individual trader reading this and trying to decide which route to go down, it's unlikely you are the next Soros or Paulson; or for that matter someone with the resources of Jim Simons. As a journalist, you don't know if this group is really representative of all the systematic and non systematic traders and fund managers that are out there. As an asset allocator you may struggle to identify the stars of the future (who may not be the stars of the past); you'll have to allocate to at least some ex-post average managers.

To put in geeky terms if we're trying to work out if the mean or median of a distribution is larger than another, just looking at the outliers isn't going to help and may even be misleading.

We need to think a little more deeply.


What are computers good at?

Source: cartoonstock.com

  •  speed. Duh.
  •  sticking to a plan, and not being freaked out by losses or annoying clients
  •  repeatability - doing the same thing given the same inputs
  •  getting their position scaling correct
  •  managing large portfolios
  •  not being hit by the proverbial bus, or leaving the firm
  • teaching other computers how to do exactly what they do

Researchers using computers can also be good at:
  •  identifying persistent patterns
  •  identifying unusual, non intuitive, patterns 


What are humans good at?



It might be more accurate to say "What are humans who are good at trading good at". Not everyone can do these things.
  • "deep dive" analysis on a small number of assets
  •  processing complex, novel, information
  •  interpreting non quantifiable information
  • adapting to novel, changing, environments
  •  genuine forecasting (rather than extrapolating the past, or assuming it will repeat)
On the last point, it's worth elucidating. We could never write a computer system that would put a CDS short on in 2006, based on the sort of fundamental analysis that Paulson and the rest of the cast of "Big Short" did*. Asset bubbles, backed by novel securitisation techniques, do not happen every month.

* I suppose it's plausible that you'd put on a credit short based on a simple technical model which assumed that CDS prices mean revert from extremes. Nevertheless there would probably be insufficient data to fit such a model, given the relatively short history of CDS as an asset.


If Paulson had been in the Big Short film, Kevin Spacey would have played him. (source digitaltrends.com) (....and why wasn't he? Answers on a postcard, or via twitter)


Which trading arenas are computers likely to be good at?


  •  High Frequency Trading (Duh, again)
  •  Scalping (sure humans can do this, but you'd die of boredom, wouldn't you?)
  •  Systematic technical analysis (persistent patterns)
  •  Equity neutral - where the portfolio is too large for humans but requires only quantifiable factors
  •  Stat arb (finding the weird non intuitive relationships)
  •  Passive index tracking, and 'smart' beta
  •  "Vanilla" arbitrage or near arbitrage eg index vs constituents, on the run vs off the run treasuries
  •  any other strategy that boils down to smart or alternative beta, where a simple set of rules does the trick



Which trading arenas are humans likely to be better at?


  •  Fundamental economic analysis, eg global macro type bets (Soros and Paulson again)
  •  Subjective technical analysis (which, frankly, I am personally very skeptical of)
  •  Weird, one-off, pure or near pure arbitrage trades (eg cash-CDS in 2009, French Gold linked bond in Bonfire of the vanities...)
  •  Special situations / event driven, eg merger arbitrage
  •  Activist investing (computers can't harass the board into doing what you want)
  •  'Deep dive' stock analysis, on the long or short side, where you go right into the nitty gritty of the business
  •  Anything where every trade is different and or relies on analysing a lot of non quantifiable information 
  • Illiquid, and "real" assets, or anything for which data is sparse, unreliable  unavailable


Note that the human edge in the later arena will be reduced or depleted if they don't have the discipline and the knowledge to set up their position sizing and management correctly and stick to it. Imagine if Paulson had put on too large a position in 2005*; and then had to close it in late 2006 due to investor pressure (which was starting to heat up). All his analysis would have been for naught.

* Yes I know he pretty much couldn't have put on a bigger position, and had to scratch around for ways to increase it by doing stuff like this, but please don't spoil my example with the facts.


Average Computer versus Average Human


So, I think we have a small number of instances where humans can't compete with computers:

  •  High Frequency Trading
  •  Scalping
  •  Equity neutral
  •  Stat arb
We then have some situations where humans could compete, but it would be pointless and labour intensive:

  •  Systematic technical analysis
  •  Passive index tracking, and 'smart' beta
  •  "Vanilla" arbitrage or near arbitrage (not high frequency)
  •  Alternative beta

Then there's a group of strategies that computers will really struggle with:
  •  Fundamental economic analysis
  •  One-off, pure or near pure arbitrage trades
  •  Special situations / event driven / activist investing
  •  'Deep dive' stock analysis
  • Illiquid, and "real" assets


We can split the investing and trading population into four groups depending on whether they have the skills to do the kind of 'human only' strategies in the last group, plus the discipline to implement and stick to the correct risk and position management.

  • Super Traders with both the skills and the discipline
  • Committed and useless; with the discipline but not the skill
  • Clever and Chaotic, skills but no discipline
  • Useless and Chaotic, no skills or discipline.
 In the interests of full disclosure I probably fall into either the second or fourth category; with very occasional visits to the third category.

Clearly only those in the first category should contemplate discretionary trading. The rest of us should trade using a system.

Let's assume that a quarter of the investing population are in each category; even though this is a gross exaggeration*. We'll also assume that, generally speaking, people don't realise which category they are in.

* In fact I would say the proportions are more like 0.01%, 10%, 10% and 79.99%

Firstly, in terms of deciding whether to trade with a system or not, you only have a 25% chance of being in the first group for which discretionary trading is the way to go. This isn't great odds. Unless you're an idiot, a gambler, or incredibly over confident you should trade systematically.

Secondly, to answer the original question, we know that the world is split between people managing money with computers, and people not doing so. From the above analysis only 25% of those in the discretionary group should be doing it. They'll be making great returns, especially if they focus on the stuff computers can't do.

The rest will be making a terrible job of it, either because they lack the skill or the discipline. The median will sit in the middle of this group. The mean will be a little higher.

For those that are a bit slow, yes, that's why I chose the median right at the start of this post!

Meanwhile in the computer group there will be much less dispersion; a much narrower distribution. It's true that in many fields of investing and trading the very best computers will be not quite as good as the very best humans.

But the average (both mean and median) of this group will be lower than the top 25% of the human group, but higher than the overall human average.

I would argue that this simple model is pretty close to the truth, especially if you pull in all the amateur traders, many of whom are making a pretty terrible fist of discretionary trading, and fall mainly into the "useless/chaotic" bracket. In the professionalised segment of the business things may be slightly better; as even discretionary managers will be using some systematic rules (which I discuss more below).


The best of both worlds?


source: Dilbert.com


Is there some way we can get the best of both worlds? After all pilots of planes manage it. Autopilot does the boring stuff, but in an emergency humans are able to interpret the situation and act on it much better. I can think of two main routes:


Mechanical system - human override and scaling


Quite a few so called systematic managers seem to operate on the basis of "Yeah, we have all these systems, but we decide when to turn them on and off depending on our judgement". Even firmly committed managers do, occasionally tinker (meddle, risk manage, refit or improve - pick your verb).

Is there a back testable systematic way of switching models on and off that works? If so (and I doubt it) then you're firmly back in the systematic camp.

If not... well it strikes me as just plain stupid that people who know they can't predict market movements think they can predict trading system performance in the same markets.


Discretionary calls - human


This is a much nicer idea. Use your human judgement to decide that Apple is a good bet. Then use a system to decide how much of Apple you should buy, how you should adjust the position as market conditions change, and when you should close. Stick to the system, even when it hurts.

I liked this so much I even put it into my book, where it appears as the the "Semi Automatic Trader" character.

It's also tailor made for the clever and chaotic. Sadly for the committed / useless and useless / chaotic there is nothing that can be done. Use a system, please, for your own sake.


Conclusion


Most humans should trade with systems. Some,who have the skill but not the discipline, can use discretionary trading combined inside a systematic framework. The average human will not be as good as the average computer. Unless you know, for sure, that you'll be well above average as a human trader you should get out your keyboard and start coding.


11 comments:

  1. There is far too little humor in finance. Thanks for mixing numbers and fun in just the right ratio.

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  2. Thanks Matt I really appreciate that.

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  3. I will only say that to paraphrase Justin Mamis. In the endcomputers are programed by humans, therefore they eventually express human error and frailities

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  4. Rob, I would like to second Matt's comment...great stuff. I just read this post while half way through http://www.amazon.com/Your-Money-Brain-Science-Neuroeconomics/dp/0743276698 ... it really is true what old Ritholtz says: "We are monkeys with shoes"!!

    Cheers, Donald (down here in NZ)

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    Replies
    1. Donald,
      I like the sound of that book. Must add it to my reading list.

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  5. Hi Rob
    Really really appreciating your work and my comment is not related to the above post and you may have answered the question before so apologies in advance. The question I have is, do you have a simple method of converting breakout strategies to a forecast. Obviously some things like MA crossovers easily translate to a forecast but as far as I can see I only translate a breakout into + 20 go long, - 20 go short. Any thoughts?
    Many thanks ..

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    Replies
    1. You're right, it's not possible. For this reason I tend to avoid strategies like this. Or try and adapt them so they are continous in nature.

      So, I use a stochastic as my "breakout" signal (price - mid range of price / range of price). This means that I'll be fully long at the point of the breakout, but I'll have started going long the moment the price is in the top half of the recent historical range.

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    2. OK that's interesting - so you could do a forecast depending on where you are within a 26 week price channel with the EMA being the midpoint and the channel defining the forecast extremes for example (or use a Bollinger Band) - I'll try that - thanks

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  6. Hi Rob - first off, great blog resource here. I'm a big fan. Currently reading your book. Very interesting.

    I have a question. DO you have any experience with systematic trading specific to niche or a single set of markets ? (e.g. crude markets)

    I'm a junior paper trader in a physical commodity house. I trade Brent, Gasoil, Gasoline, Heating oil. I've been playing about with and testing various technical ideas like composites of moving average crossovers with some varying/interesting results.

    I was wandering what advice you might have with regards to applying systematic or semi-automated trading to a much smaller universe of markets ? Is it even advisable ? Would you need a big library of different technical or fundamental strategies to compensate for the lack of market diversification ? Or is that a dangerous game ?

    I cant prop in FX, STIRs, Equities etc.. just energy markets you see. Interested in your thoughts. Thks.

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    Replies
    1. Hi Simon

      It's true that for basic technical trading much of the benefit comes from diversifying across asset classes. Not doing so will cut your expected returns by around a factor of 2.

      There are certain kinds of niche technical models that one can run in energy markets. For example mean variance of the crack spread. Calendar spreads and butterflies in crude oil also show interesting behaviour. These are all relative value strategies, so negative skew / high leverage, and you should be wary of putting too much into your portfolio.

      As you say the other advantage you might have is developing a system that combines fundamentals and technicals. For the former you might decide it's too much work to make it fully systematic, but come up with some kind of forecast score. You can then combine that with the forecast from technical models.

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    2. Ok. So it would definitely cut my expected return. Thanks for the clarity. Much appreciated.

      Cracks and spread diversification is an interesting idea. But they can still experience periods where they are quite flat price dependent I find. So probably keeping it as a smaller separate strategy set within the portfolio is logical.

      A forecast scoring system for both fund/tech sounds like an interesting route. I guess I need to read your book further to discover the full meaning and how to apply a forecast scoring model.

      Thanks very much.
      Simon

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