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What’s the best way to learn portfolio optimization?
What's the best way to learn portfolio optimization? by Laurent Bernut
Answer by Laurent Bernut:
In theory, theory and practice are the same. In practice, they are not. Most market participants optimise the wrong thing for the wrong reasons.
Modern Portfolio Theory was revolutionary
Half a century Modern Portfolio Theory was revolutionary in its days, that was before they sent someone to the moon, and Nikita Khrushchev became the Soviet Supreme, and Fidel was kicking Batista’s ass.
It made a couple of assumptions that were good back then but that have plagued generations of traders. It equates volatility with risk. Volatility is not risk and risk is not necessarily volatile.
LTCM had low volatility and so did all the Volatility fund (short Gamma funds) back in 2008. They had exceptionally low volatility until the day before the blew up… Similarly, Paul Tudor Jones, Ed Seykota and a lot of the major CTAs have high volatility but no-one will argue with their returns…
Sharpe ratio, Jensen alpha, Treynor and all that jazz are the right mathematical answers to the wrong question. I wrote a post about that. Conclusion: i actively de-friend the savant clowns who ask for my Sharpe.
Second assumption is linearity of risk. This one is not apparent. Mean variance is mathematically elegant, but it assumes strategies work throughout the cycle. Mean variance is observed through the period and when bad, go back to the drawing board. Old school academic laziness
Example: small caps growth do extremely well at the beginning and at the end of a bull phase. Rest of the time, they oscillate between volatile and outright dangerous. Mean variance optimisation would triumphantly conclude small cap growth is a bad strategy. Good luck explaining that to the cohort of managers baby sitting billions on small caps
What to optimise then
Trading edge = Win% *Avg Win% -Loss % * Avg Loss%
Optimisation and back-testing serve three purposes:
- Kill your baby: the earlier you disprove your idea, the less time you waste on it. So, electrocute, drown and punch your baby until it either dies or survives
- Identify when a strategy works and when it stops: no strategy works all the time. Laziness is to optimise so as to make it fit through the cycle.
- Identify design flows: dropping all the indicators, factors into the masala smoothie blender and press play hoping something will stick is unforgivable academic laziness. Optimisation will give a correct mathematical answer to a myopic wrong question.
Torpedoing decades of layered platitudes probably requires a little more substance.
- When: There are times when buying penny stocks or IPOS are spectacular strategies. Other times, it is suicidal. Difficulty is to know when. Optimisation will have you size small or discard the strategy altogether. Buying penny stocks is like wearing bikinis: it is a bad idea to insist on wearing them through the winter
- Design flows: many optimisations go equal weight on all factors and equal weight position sizing. When it does not spit out the right answer, add another factor. Sounds familiar ? That is one bad “bad idea”. Design flows are about subtracting factors, simplification, not addition of complex fragility.
How to optimise then ?
- Segment time periods: run through very long period, then segment by market regime. Run optmisied series through various regimes. That will tell you when regime has changed. The biggest mistake people commit is to re-optimise when strategy stops working. If you have identified when it does not work, then it is easier to accept
- Universe: run large, segment and then large universes again. Few strategies work across large universes. Conversely, humans are notoriously bad at defining the scope of the strategy. If your stuff is mean for small caps, run it for large caps as well. It will give invaluable perspective
- IMPORTANT POINT HERE: Long Short strategy conception starts from the short side and then considers long side. My personal favourite mistake of all times is the demise of quants in the summer of 2007 (this is a chapter in the upcoming book). They went Long quality and short bad quality, logical right ? They assumed shorts were the inverse of Longs. Everyone buys Quality of course, but they failed to notice that no Long holder was selling bad quality anymore. Yet, in order to stay market neutral, they had to short even larger quantities of stocks that no Long Holder owned anymore. Shorts became illiquid and hard to borrow. Risk management as in days to liquidate became an issue, so they had to cover which triggered chain reaction short squeezes. They lost -4-5% in seemingly calm markets, when they were making +0.5%. Investors started to redeem. Those guys were leveraged up to the hilt. VAR went up so PBs rquired more collateral which brought down leverage. Redemption + lower leverage made returns look even more underwhelming. Game over. (Oh and by the way, they had low volatility and excellent Sharpe until they really did not)
Personally, i optimise for different purposes than most people. I optimise for buying power. Performance is not a function of stock picking , it is a function of position szing (i am picking a fight here). Problem becomes a buying power or concentration issue. This type of optimisation looks at exits, sizes of exits, position sizing. Answer is not easy as it searches for regime disruptions. That is too tough of a problem to visualise. I need the machine to show transitions.
Sorry if i appear pugnacious today (might also be those stiff cocktails they serve in NYC). MPT did a great job. It showed the way, thank You very much, but now it is now time to innovate and move on. The problem is that it requires a different way of thinking, involving both hemispheres of the brain.
#Quora: Do most quantitative trading strategies have limited capacity?
Answer by Laurent Bernut:
The best answer to that question comes from my ex-boss, mentor and more importantly dude friend: “You are at capacity when inertia sets in”
This means that when managers become reluctant to take a trade, this is when they reach capacity. It might be at 100M or at 2B. It is after all subjective. The same can be said about algorithmic strategies.
Algorithmic strategies are more scalable than humans. They can be deployed across larger universes and shorter periodicities. So, diminishing returns kick in later. Market impacts happens and returns come down eventually.
There are three reasons:
- Volume market impact: some strategies arbitrage inefficiencies. So, trading naturally correct them. They have built in capacity constraint
- Competition: market participants copy each other. Pie does not grow, it gets fragmented
- Conceptual shortcomings: that is the hardest problem to solve. Problems are often solved at a different level than they were created. There are four ways it can be solved
- go wider: expand your coverage universe
- go bigger: accept market impact as a necessary cost of doing business. This means expand limit orders, but it also means refine signals so as mitigate slippage
- go deeper: elicit trading: bait other market participants to take the other side so as to create volume. This is the new old thing. Remember “Reminiscence of a stock operator” when the veteran trader tests the market by observing how fast his orders were filled. HFT have perfected that craft.
- go different: money management is the new new old thing. Getting in is a choice, getting out is a necessity. Trades do not have to be all-in and all-out. Scaling in and out mitigate capacity issues
Do most quantitative trading strategies have limited capacity?
#Quora: If 90% of traders lose and 10% wins, are those 10% disproportionally made up of very high IQ people?
If 90% of traders lose and 10% wins, are those 10% disproportionally made up of very high IQ peop… by Laurent Bernut
Answer by Laurent Bernut:
No, but for different reasons that the instructive and brilliant answers given by people far more intelligent than yours truly. Making money in the market is a side effect. Yes, You read correctly. Would You like to know why ?
(An entire section of my upcoming book on short selling is devoted to this topic so stay tuned)
The biology of trading:
To all of You who believe markets are efficient and think of yourselves as rational investors, how many times did You check your mails today ? 10–20 times. That is Dopamine in action. This is the reward circuitry. Not even Paris Hilton has a life exciting enough to check mails continuously. We do so because our brain releases dopamine (feel good hormone) for mild uncertain rewards.
Have you ever found yourself overriding your risk limit just right around the wrong time? Overconfidence is the ubiquitous plague of traders. Rational investor, would You like proof of overconfidence? Divorce statistics, i rest my case with your multiple ex-wives
Now, when your performance sinks and you can’t think straight, do you pass up trades? Do you find yourself exhausted, irritable? Cortisol
Your average pension fund manager is the direct descendant of someone who woke up in a cave and started running after mammoths for breakfast. Not exactly savvy with probabilities but the survivors got the girls…
The hard wired mind of trading
In the 60s Michael Gazzanika developed the theory of split brain. We, humans, pre-consciously rationalise our decisions. Take a look at the junk in your portfolio. A solid third of it would not even be there if you had to do it all over again.
Do You find it hard to execute stop losses (Oh, the chapter on the psychology of stop loss is worth the entire book multiple times, i will refund anyone who does not have a aha moment there) ? Ego prevails over profits. Valeant (VRX), case in point…
Subconscious beliefs and fears
Fears exist in the shadows. In his book, Daniel Goleman (the EQ dude) describes elf deception as a built in mechanism that covers its own tracks. we rationalise all the time. Proof? when was the last time you got laid (Maslow pyramid about reproduction)? when was the last time you rationalised a decision ?
Market participants do not trade to make money. Proof?Look at the junk that fester in your portfolio… Some of us trade to prove to someone dead 20 years ago (i-e father, mentor, bully at school, whatever) that they are worthy individuals. Dude, You are beautiful, You are worthy of love.
The floating world of beliefs and fears
Finally, floating at the surface like ice cubes in a single malt are conscious beliefs and fears. Fears of losing your job, fear of missing out, fear of pulling the trigger, fear of inadequacy (smart guys are buying that Enron thing so i will join the party)
Of course, there is the belief You cannot time the market. Who told you that? Journalists and analyst who hug the mike and more importantly yourself when the thing you just bough went south…
Now, let’s quip the IQ myth. Self deception is a mechanism that covers its own tracks. High IQ dudes always have spectacular excuses. I know two types of traders: those who make money and those who have excuses. Which one are You
Bottom line: born to lose
Bottom line, your biology f@#ks you up. Your beautiful mind comes delivered with amazing features, most of which will get You killed on the markets (try fairness for instance). Then, your ego, your subconscious deep rooted fears will supersede your best intentions. Then, there is this floating junk of unchecked beliefs irrational fears.
So, no wonder 90% of the people lose money.
Now, why do 10% succeed? The hero’s journey
They succeed simply because of their inner alignment of their biology all the way up to their daily routines. Great traders are not smarter, they have smarter trading habits. Making money is just the yardstick of inner alignment.
Would You like to know about the three scientifically proven methods to re-align yourself? Then, please follow, or subscribe to my (free) website, or help launching the book
As Arnold, Ze Great Governator said: “Ze hardest part of putting on muscles is getting to ze gym, jaa”
If 90% of traders lose and 10% wins, are those 10% disproportionally made up of very high IQ people?