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Will traders still be able to compete with algorithms in 5 years?

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Algos have already taken the lion’s share of trading volume across all markets. 80% of Tokyo Stock Exchange daily volume is done via algos. If investment is a process then automation is the logical conclusion

First, ETFs will displace discretionary participants, not algos

If every person contributing to their 401K asked themselves this simple question: “do i want to retire on numbers, or on stories?”, then the assets under management of the active management industry would melt twice as fast as the arctic polar cap.

Algos are not about to change the way discretionary participants work anytime soon. ETFs will. Investors have gradually woken up to the fact that the only competitive advantage provided by rows of analysts and company visits has been a marketing argument, not a trading edge.

Evolution does not take prisoners

Active managers who want to survive have to up their game. That means better risk control, better stop-loss policy, better bet sizing, better execution, better emotion control. In a nutshell, that means better process. If investing is a process, then automation is the logical conclusion. Discretionary investors will gravitate towards algorithms to remain competitive

Even if they choose not to automate their systems, discretionary traders should formalise their process to the point of being computer coded. This sheds light on the blind spots in their process.

5k Views · 62 Upvotes · Answer requested by Roman Gabay

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Are forex signals useful in making profits?

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This question originally appeared on Quora

 

This great question has done a remarkable job at baiting scam-artists. Whoever answered “Yes”, followed by a link to some website is a scam-artist

The one-eyed scam-artist leading the blind

Everyone wants to gain this mysterious esoteric called trading edge. Well, trading edge is not a story, it is a number and here is its formula:

Gain expectancy = Win% * Avg Win% – Loss% * Avg Loss%

Tattoo it where you can be reminded throughout the day (Tramp stamp on your better-half is probably off limit). There are two modules to any trading strategy:

  1. Signals: exits first, last and very very very least entries
  2. Money management: position sizing

The scam artists who answered yes, referred to hereafter as Yes-men will provide dummy statistics about entries but conveniently forget everything thereafter. Entry is easy. You can buy a boat, a sports car, a vacation home any day of the week. Good luck reselling it. Bad entry can be salvaged, bad exits can’t.

Then comes position sizing. Signals conveniently omit position sizing for a good reason. It is a function of win rate and stop loss, both of which are either omitted or optimistically manufactured…

Implicit adoption of a trading system

Now, who does not like to wake up, receive a bunch of signals, pick one or two, send the trades and watch the money roll in? I certainly would love to, except it does not work like that.

Even if signals are thorough enough to encompass all the necessary elements from entry to exit including risk, it still omits the most important: following someone’s signal is adopting her belief system, formalised in a trading system. Example: as a professional short seller, i am naturally risk-adverse (prudent position sizing) but disciplined (take the trade, follow the system). This philosophical tenet transpires in my algorithms. This may not jive with someone who likes big high conviction bets.

Even supposedly objective systems such as deMark have several subjective degrees of freedom. The best example comes from the Market Wizards series. Those wizards have completely different strategies that sometimes seem to contradict each other. Yet, they are profitable because they have a system that suits their personality. When You follow someone else’s system, remember that you half-heartedly embrace someone else’s personality without fully understanding it.

How to spot a scam artist?

First, those who answered Yes and attached a link are scam artists. Coming out was not so tough after all, was it?

Second, here are classic red flags:

  1. No real money live track record = SCAM: Myfxbook provides live track record, so no excuse
  2. Entries only: no exit, no stop loss, no risk, no stats means SCAM
  3. Rosy stats: 70%+ win rate means SCAM. We all want to follow a system that is right 100% but the reality is the best traders LT average win rate is around is largely below 50%
  4. easy money: no you will not turn $500 into $2,000,000 in 2 years. Veteran pros with decades of experience dream of achieving 20% per annum. Scam artists who promise you can achieve 100% returns= SCAM

Conclusion

Basic rule of thumb: we are in finance, assume everyone is a scam artist until proven wrong. Good news, You can cross a few names off the list

How do I get better at trading?

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How do I get better at trading? by Laurent Bernut

Answer by Laurent Bernut:

“Hope is a mistake”, Mad Max, post apocalyptic road poet.

3 declinations of the same principles: process

A. Trading edge is not a pretty story , trading edge is a number

Every strategy ever traded boils down to this formula:

Gain expectancy = Win% *AvgWin% – Loss%*AvgLoss%

Your survival only depends on how You can tilt the distribution. Regardless of the asset class, there are only two styles: mean reversion and trend following.

  • Open mindset: there is a nugget in everyone’s story
  1. Read the classics: Lefevre, Schwager, Covel, Van Tharp, Loeb
  2. Podcasts: Michael Covel, Andrew Swanscott, Barry Ritholtz
  3. Investment newsletters are to investment what mangas are to literature, Unsubscribe, no exception
  • Stock picking is vastly overrated
  1. Plain vanilla fundamentals is not enough: 3/4 of professional managers underperform; over 3/4 of them claim to be fundamental stock pickers
  2. 90% of market participants focus on stock picking and entry. 90% of market participants fail. Causality and correlation: unsubscribe from all newsletters
  3. never enter w/o an exit policy: Once in a position, there is 100% chance You will exit. W/o exit plan, 90% chance the market has sth nasty in store for You
  4. Money is made in the money management module. The single largest performance discriminant is bet size: process and math

B. Portfolio management process

  • Risk is not a story in China, Risk is a number

Risk is not a story. Risk is not a high Sharpe ratio or low VAR. Risk is how much You can afford to lose per trade and cumulatively. Whatever You think your risk budget is, divide it by two. By the time You have lost half your budget, You will be a different person, gripped in cortisol and CRH, paralysed by fear.

  • Write strict investment guidelines: risk, exposures, objectives

“People live up to what they write down”, Robert Cialdini. Formalise your process in writing. Execute. Simplify. Running a portfolio w/o strict guidelines is like building a house w/o a plan

C. 90% of trading is mental, the other half is solid math

Above all else, any trading system is worthless w/o the right mindset: process over outcome

Examples of outcome vs process:

  1. Stop loss override: ego over process
  2. Close a position too early clear trading plan: outcome vs process mindset
  3. Too big/small bets: euphoria/depression over process
  4. Focus on performance instead of plan execution: outcome over process
  5. Mood swings depending on performance: outcome vs process

Being right is not being profitable (outcome). Being right is following the plan (process)

Conclusion:

This is an “avant-gout” of the book to come. On the short side, the market does not cooperate. Open and process mindsets are the two keys to survival

How do I get better at trading?

#Quora: How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse than -5%, and has a starting amount of $2…

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How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse t… by Laurent Bernut

Answer by Laurent Bernut:

Experience has taught me that people like this are a plague. They are not risk adverse. They are conservative to the point of being risk seeking: by refusing to accept moderate waves of volatility, they invite left tails tsunami. If You cannot afford to turn his money away however, here are the formulas

A. Psychology of conservative people

If You can’t stand losing, then You shouldn’t play. When they say they are willing to accept modest returns as long as You don’t lose much, what they mean is they do not want to lose at all.

Conservative is not synonymous with risk adverse. They are opposite in fact. Risk adverse means You have articulated and pieter_bruegel_the_elder_-_the_parable_of_the_blind_leading_the_blind_detail_-_wga3512quantified your risk appetite. Conservative means You are afraid of taking any risk. You are ready to discount your ambitions to the point they will be met with certainty. Kodak, Nokia were risk adverse…

It also means they are afraid and think everything is risky. It is your responsibility to educate them on risk. Do not step into dissertation mode about China, the Fed, Venusians landing in Central Park and Yoyogi park in Tokyo (i will sacrifice myself and volunteer if those sexy Venusians want to perform tests on my body). Risk is not a story, risk is a number.

Secondly, if You deliver, they are likely to demand more over time: 8–10% turns to 10–12% etc. Two reasons for this: you will be put in competition with other managers who promise they can deliver better with the same risk. Since your returns are underwhelming, You will be in constant competition. Secondly, and this is more insidious, they become overconfident. Since they believed everything was risky but now are making money, and they still don’t understand risk, they turn euphoric, literally drunk on testosterone and dopamine. They are laughing their way to the bank until the day you start losing again.

B. Market’s money

The strategy is to start small with minimum risk and increase gradually as you generate performance. Then, before year end, you reduce risk so as to start the new year afresh.

Many people do the gradual increase well, but forget about the decrease. Investors think in calendar years.

Example: first quarter, you generated 2%. You can now increase risk by x% of your gains (10–33%). So instead of risking 0.10% per trade, you would risk, 0.12% and so on and so forth.

Comes November, You are currently risking 0.20% per trade. Now, it is time to de-risk down gradually down to 0.10% so as to start January with a low risk, low concentration portfolio, ready for the new year. Remember: in the investors mind, January is the beginning of a new year, not the continuation of last year’s market.

C. Risk:“how much is enough?”, Steven Seagal, obese mythomaniac

You will often read that you should not risk more than 1–2% of your capital per trade. This does not mean position size, but equity at equity at risk. In your case, if you apply that rule over 5 stocks, one bad month and game over for good. So, get a better bad idea …

What is the maximum risk You can afford on each trade? This is one of the thorniest questions in financeI have pondered that question for years, until one day i came up with a simple elegant solution. Input variables

  1. Drawdown tolerance: If Your investor redeems, game over. he said he would tolerate -5% max drawdown. So, in order to be safe, you should probably calibrate your risk to a fraction of this. If You calibrate at 100%, he will redeem and this is one time where being right is bad, very bad. Besides, you need to rebound from drawdown, so let’s say somewhere between 50%-66.67%, say 2/3
  2. Avg number of positions: over 1 turnover cycle, what is your average number of positions? let’s say: 50
  3. Loss rate: over 1 turnover cycle, what is your average loss rate. In case You don’t know use 60% as loss rate (Yes, it means you lose more often than win, and it is called prudence)

Equipped with this:

Max risk per trade = Drawdown tolerance / (Loss rate * Avg #positions)

= 5% * 2/3 / [50* 60%]

Max risk per trade = 0.11%

Now, that was the max risk per trade. Let’s move to the min risk per trade. This is a fraction of that: usually 40%. So, your min risk per trade would be around 0.05%

Add trading has a cost: 0.036% blended avg, between DMA and high touch at Credit Suisse for example (as a good friend complained again this morning while we were naked in the gym shower !?!).

Now, You probably start to understand why i mean that those customers are toxic. When You go through a drawdown and you will have rough periods, You will simply not be able to dig yourself out.

Conclusion

Once in early 2013, i was cruising at a hedge fund party nursing some nasty Chardonnay and some dude who just launched was explaining his strategy:

-“fundamentals pairs trading”, he proudly said

-“So, You must be Long Toyota and Short Mazda, right? Mazda has gone up 400% and Toyota 30%. That must be a painful trade? ”, i asked

-”Nah, positions are small anyways, so no it does not hurt”, he confidently replied

-”Well, if they are too small to hurt, do You think they are big enough to contribute?”, i candidly asked

And he did the unthinkable rudest thing someone can do in Japan. He gave me back my business card and walked away

How should I manage a client’s portfolio if he wants a 8-10% return and no negative years worse than -5%, and has a starting amount of $2…

#Quora: Do most quantitative trading strategies have limited capacity?

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Wasting water leaks into overfilled glass photo against white

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:

  1. Volume market impact: some strategies arbitrage inefficiencies. So, trading naturally correct them. They have built in capacity constraint
  2. Competition: market participants copy each other. Pie does not grow, it gets fragmented
  3. 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
    1. go wider: expand your coverage universe
    2. 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
    3. 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.
    4. 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?

I’m good at shorting, is it possible to start a short biased hedge fund?

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I’m good at shorting, is it possible to start a short biased hedge fund? by Laurent Bernut

Answer by Laurent Bernut:

Excellent answer from Bill Chen, to which there is little to add. In my opinion, short-selling is widely misunderstood even by professional sophisticated investors. To add insult to injury, FED and ECB are the official sponsors of the longest bull market on record.

Short-sellers are like sharks…great-white-shark-sharkspictures.org_

Did You know that deep in the quiet comfort of your house, there is something 150 times deadlier than a shark ? It is called a bed. The probability of dying after falling out of bed (literally) is 1/2*10^-6, while ex-sanguination from an exploratory shark bite is 1/150*10^-6. Sharks are misunderstood and fragile. So are short sellers.

Adding a short component to a long strategy reduces volatility of performance, increases leverage and reduces drawdowns in terms of magnitude, frequency an period of recovery. Now, this is neither how they are perceived nor marketed.

They are perceived as predatory, nefarious and risky in nature. I lost half of my pension in 2008 to supposedly buy and hold low risk mutual fund #de-friendBuy&Hold.

Short biased funds are relative players, just as your average mutual fund. Their benchmark is just the inverse of the index. When the market goes up by 10% and they clock +8%, effectively they have outperformed. The only problem is that investors lose -8% in absolute. No-one likes to structurally lose money, right ?

Wrong, 3/4 of mutual funds trail their benchmark year in year out. Investors make money in absolute but lose versus index funds. Worse even, mutual funds lose both in absolute and relative during bear markets. So, here is an interesting perception gap:

Short biased funds lose money during bull markets but make some during bear markets. Rationally speaking, they serve a more important purpose than mutual funds. Logically speaking, you do not need a mutual fund, but you do need a short biased fund in order to protect you from downturns.

The problem is that most of them will have died by the time we hit a bear market. The second problem is that short selling is still perceived as evil, when all they do is provide hedge in good times and absolute performance when no-one else does

BTW, small difference of opinion with Mr Chen, redemptions do not come during outperformance during bear phases. They come during early bull markets, when bearish sentiment as well as AUM peak. Short funds handle regime change quite poorly. The same happens with Long Only. Managers can underperform and still grow assets in bull markets because everyone makes money. Long Only underperforming in bear markets is bad…

Prometheus and capitalism

Monetary authorities around the world have mutually decided that bear markets are bad. So, they play god with the markets. They do whatever they can to prop them up. Their latest trick was a sucker punch to the very foundation of capitalism: negative interest rates. In what parallel universe does it make sense for a borrower to be paid to borrow money ?

This begets an interesting question: if they are blind enough to think they can tame the markets, will they be competent enough to solve the problems they have created ?

Anyhow, the point is the only trade in town is: when a tired bull market wants to go down, buy everything and wait for the monetary cavalry to show up with their QE heavy artillery.

This market manipulation is damaging for all market participants. It rewards complacency and bad behavior on the long only side (double down on losers) and wipes out short sellers. In March, i lost -15% in 3 weeks. This bull is tired but central bankers will not admit it until …

What’s the solution then ?

Once upon a time, mortgage bonds were boring. Then, someone figured out a way to package and MARKET them as low risk / high yield investment securities. Shortly thereafter, every other math wiz kid and smooth salesman became fluent in CDO linguo.

Same with short selling. As long as we are the “usual suspects” and default scapegoats for markets falling, speculation, corporate greed, obesity, erectile dysfunctions and all other evils on earth, small or large, then AUM will have wild swings. Picture a young promising executive pitching a short fund during an investment committee Monday morning 9 am

-“ things look a bit dicy here, i would like to start allocating to a short biased fund”, says the newbie

-“sure, buy a dividend fund or allocate more to government bonds funds”, replies the chairman

-”3% dividend is not going to cushion much when the market goes down -50%. Actually, government bonds are the reasons i am getting a bit nervous”, replies the young man

-”Now young man, how do you think our investors will perceive us if they know we are buying a short fund ? They will think we have no confidence in our economy. They will cease to see as patriotic, hard working, disciplined investors, prudent risk managers. They will think of us as short term traders, evil speculators. Rest assured they will redeem”, slams the chairman

-”You are right sir, but our clients will redeem anyway if we lose them money. Short sellers are just as patriotic, hard working and probably more disciplined than Long Only. Short sellers just happen to make money when no-one else around does. At least, if we make money for our clients, we have a chance to keep them. If we are one of the few who make money during downturns, maybe we could even grow assets. Look at Mr Paulson”, whispers the newbie as he shrinks and recoils away

-”You are a bright and talented young man. May i suggest a bit more optimism and team spirit for the rest of your career ?”, concludes the chairman with a paternalistic threat

The reason why John Paulson’s AUM exploded is: he made money when no-one else did. He stood out as a clear winner in a crowd of losers. Short selling is the most important and most valuable skillset. It just has bad press and needs to be repackaged.

Speaking of which, I am writing a book about short selling. 3/4 done already. It is about trading edge: statistical, mental and portfolio construction

I’m good at shorting, is it possible to start a short biased hedge fund?

#Quora: How does one address the issue of regime shift in algorithmic trading?

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How does one address the issue of regime shift in algorithmic trading? by Laurent Bernut

Answer by Laurent Bernut:change-of-season

Fascinating topic that has kept me awake for years. It is a thorny issue. As the patent clerk Einstein used to say: answer are not at the same level as questions. Answer is not at the signal (entry/exit) level. These are position sizing and order management issues.

Definition of regime change

Everybody has a savant definition, so i might as well come up with a simple practical metaphor. imagine You drive at 150 km/h on the highway and then all of sudden, this 4 lanes turns into a country road. If You do not react quick, you will go tree-hugging.

There are three market types: bull, bear and more importantly sideways. Each type can be subdivided into quiet or choppy. So, we have six sextants. Regime change is when market moves from one box to another.

It can be either a new volatility regime, or a move from bear to sideways, sideways to bull or vice versa. Markets rarely move from bull to bear. There is some battle between good and evil.

Why it matters?

Many strategies are designed to do well in a particular environment. They make a lot of money that they end up giving back when regime changes. Examples:

  1. Short Gamma OTM: sell OTM options and collect premium. Pick pennies in front of a steam roller. 2008–2009, August 2015, CHF depeg, Brexit etc. 1 day those options will go in the money and game over
  2. Dual momentum: trade the shorter time frame but determine regime on longer time frame. When market hits a sideways period, market participants get clobbered on false breakouts like poor cute little baby seals
  3. Mean reversion: works wonders in sideways markets until it does not. An extreme version of that is Short Gamma
  4. Value to growth and vice versa: that is probably the most lagging one. By the time, my value colleagues had thrown the towel and loaded up on growthy stocks, market usually gave signs of fatigue…
  5. Fundamentals pairs trading: relationships are stable until market rotation. For instance, speculative stocks rally hard in the early stage of a bull market, while quality trails. Example: Oct-Dec 2012, Mazda went up +400% while Toyota rallied +30%

The vast majority of market participants are trend followers, whether it is news flow, earning momentum, technical analysis. Trending bulls they do great, trending bears, they can survive. Sideways is where they lose their shirts on false positives.

The issue often boils down to how to enter a sideways regime without losing your shirt

The value of backtests

I agree that backtests will help you identify when your strategy does not work. This is actually the real value of backtest. This is when you modify the strategy and adapt the position sizing to weather unsavoury regimes. Then trade this version, not the ideal fair weather strategy.

Reason is simple: everybody’s got a plan until they get punched in the teeth. So don’t lower your guard, ever.

Asset allocation across multi-strats

Some market participants like to develop specific strategies for specific markets: sideways volatile, awesome for pairs trading, great for options strangle/straddle short. Breakouts are good for trending markets etc.

How do You switch from one to the next ? Fixed asset allocation is as clunky, as primitive and as MPT as it gets. There is something far more elegant and simple:

  1. Calculate trading edge for each sub-strategy: long Trend Following, pairs etc
  2. pro-rate trading edge for each strategy
  3. Allocate by pro-rata
  4. Allocate a residual minimum even to the negative trading edge strategies

This is a simple way to put money where it works best

Regime change for single strategy

When not to trade

Van Tharp believes that there is no strategy that can do well or at least to weather all market conditions. I believe there is more nuance. Is the objective to make money across all market regimes? For example, sideways quiet are preludes to explosive movements. In order to make any money, you need to trade big and if you are on the wrong side when things kick off, game over

We trade a single unified (mean reversion within trend following) strategy. What follows is our journey through solving the regime change issue. We found that the three best ways to manage regime changes are

  1. Stop Loss:
  2. position sizing:
  3. order management:

Stop Loss

Regime changes are usually accompanied with rise in volatility. Volatility is not risk, volatility is uncertainty #de-friendSharpeRatio

We used to have a complex stop loss rule. Now we have a uniform elegant stop loss. It just lags current position. It is fashionably late, hence its name: French Stop Loss.

The idea there is to give enough breathing room to the markets to absorb changes without giving back too much profit.

Our solution is to consider stop loss as a fail safe and allow the market to switch direction bull to bear and vice versa within the confine of a stop loss.

The result is we end up switching from one regime to the next more fluidly. We have reduced the number of stop losses by 2/3, which in turn has materially increased our expectancy.

Stop losses are costly. They are the maximum you can lose out of any position.So, unless you have a kick ass win rate or extremely long right tail, you want to reduce their frequency and allow the market to transition.

Position sizing

The first thing You need to understand is that You cannot predict/anticipate a regime forecast. You will find out after the facts. This has some immediate consequences on position sizing: always cap your portfolio risk

Many position sizing algorithms use equity in layers or staircase. We base our calculation of peak equity. So, drawdowns however small have an immediate impact on position sizing. This slams the break much faster than any other method i know.

The other side of the equation is risk per trade wich oscillates between min and max risk. When regime becomes favourable again, it accelerates rapidly.

The whole premise is early response to change in regime through position sizing

Another feature is trend maturity. Betting the same -1% at the beginning and at the end of a trend is asking for a serious kick in the money maker. Trends are born, grow, mature, get old and eventually die one day, just like believers in Efficient Market Theory. So, risk less as you pyramid.

Order management and hibernation

Along with the position sizing comes a vastly underrated feature in most order management: trade rejection.

Secondly, we use a position size threshold. When markets get too volatile and we experience some drawdown, stop losses dilate. As a result, position sizes get smaller. When sizes are too small, trades get rejected.

Order management is vastly under-utilised in most systems i have seen. It is often binary, all-in/all-out, or one way scale in or scale out.

In our system, taking profit off the table is a not a function of chart, technical analysis but driven by risk management. Until price goes a certain distance, exits are not triggered. If exits are not triggered, entries cannot be triggered either. Since volatility goes up and position sizes get smaller, the system drops into hibernation.

We have factual evidence that hibernation during an unfavourable regime is a powerful mechanism to weather regime change. It involves a lot more sophistication than muting indicators, slope flattening etc. It encompasses position sizing, order management, stop loss and to a lesser degree signals.

Conclusion

Multi-strat asset allocation based on pro-rated trading edge is the easy way to go.

Single strat across multiple market regimes means acceleration/deceleration but also hibernation. This is not an easy problem. There is one thought that kept me going through the frustration of figuring this out: “Building a system is like watch making. Time will always be off until the final cog fits in”

How does one address the issue of regime shift in algorithmic trading?

Can the Quant Meltdown of August 2007 repeat again in the future?

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Can the Quant Meltdown of August 2007 repeat again in the future? by Laurent Bernut

Answer by Laurent Bernut:

This is an excerpt from the book i am writing on Short sellingimplosion

Start with the Shorts in mind

In August 2007, cross sectional volatility took all markets around the world by surprise. Indices did not move much in aggregate, but constituents jumped and crashed 2-3% across the board for two days. Then, rumours of quants funds, such as Goldman Sach’s flagship market neutral unwinding, started to spread. This was the beginning of the end for quantitative market neutral funds. Their short books were the culprits.

Leverage
Quants had built those models where they had an arbitrage Long good / Short poor quality stocks. Since they were market neutral, the cash proceeds from short selling could be used to leverage up almost ad infinitum. Some funds were levered up 7 times. Leverage was used to magnify otherwise underwhelming returns.
Quality was working well on the Long side. All they had to do was match exposure on the short side. They had to continue short selling in order to match the natural expanding Long side. It all worked well until volume started to dry up during the summer months.

Liquidity on the short side and chain reaction
They eventually realised it would take them weeks to unwind their short positions. So, they proceeded to pare positions down to manageable liquidity. Since everyone with roughly the same models came to the same realisation around the same time, it triggered a chain that culminated into a messy cross sectional market.

Not so safe after all
Back in those days, market neutral funds were marketed as safe investment vehicles: equity returns with fixed income volatility. When some funds started posting -4 to -8% returns during seemingly quiet markets, investors panicked. Soon enough, redemptions started to come through.
Those redemptions forced managers to close positions, thereby adding more volatility. Prime brokers asked for larger collateral as Value At Risk (VAR) increased, thereby forcing funds to reduce leverage. With reduced leverage, increased volatility and piling redemptions, it was game over for market neutral funds.

Morale of the story
It all started with one simple mistake: in their minds, the short side just happened to be some byproduct of the Long side.
Morale of the story: whatever has the power to kill a business is not a sideshow. The short side may command higher fees and reduce risk, but when neglected it has the power to bring a business down.

This will happen again in the future as long as market participants do not take the dynamics of the short side into acount

Can the Quant Meltdown of August 2007 repeat again in the future?

#Quora: What’s the biggest mistake that stock market investors make?

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My answer to What’s the biggest mistake that stock market investors make?

Answer by Laurent Bernut:

Short answer: Absence of exit plan. The only soldiers who go to combat w/o an exit plan are Kamikaze. They don’t need one because they expect to die. Unfortunately so do a lot of market participants.082115-Tradingsecrets-01

The apparent simplicity of the answer hides sophisticated concepts that can be broken down in two part: statistical and psychological trading edge

I Statistical trading edge

A Stock picking is overrated

Most market participants believe that stock picking is the alpha and omega of alpha generation. Stock picking is merely everything that precedes entry. It excludes bet size and exit. Unfortunately, stock picking has little influence on the statistical trading edge

Trading edge = Gain Expectancy = Win% *AVG Win% – Loss% *AVg Loss%

1 Stop Loss is the most important variable in a trading system

Stop Loss has direct impact on 3 out of 4 components: Win%, Loss% (1-Win%) and Avg Loss%. Furthermore it has impact on trading frequency: the tighter the stop loss the higher the frequency and vice versa for Buy and Hope

Oops, this just proved that exit is the most important factor.

Please read further about the psychology of stop loss, even if you do not agree

2 Don’t call the race before the finish line

As can be seen, entry has some influence on Win% and Loss%, but nearly not as much as exit. The only time the amount of money that has been made/lost is known with irrefutable certainty is after exit, when open risk is closed

The corollary is that poor entry can be salvaged, poor exit can’t. If You do not have a proper exit plan, You will fail to appreciate the exit plan the market has in store for you

3 Money is made in the money management module

Secondly, Stock picking excludes bet sizing. Bet sizing is the most important determinant of performance

Here is an interesting story to prove this point. When I was at Fidelity, I was running my algo across all managers’ portfolios. My unbridled ambition was to help them trade better 0.05% at a time (multiply this 0.05% by 20 and you are in the rarefied atmosphere of outperformers).

It soon dawned upon my thickness that the same stocks were featured in in my esteemed colleagues’ portfolios. Nothing surprising there, everyone has access to the same research and there is healthy cross pollination. What was surprising was that despite low dispersion of holdings, there was high disparity of performance and volatility: despite owning the same stocks, some people were making a killing, while others were getting killed.

Conclusion: the difference that made the difference is not stock picking, it is bet sizing

4 Blow-ups and feed free-loaders

Watch your winners, but watch your losers more closely. Interesting story: i once had the opportunity to analyse multiple portfolios over many years across many managers on a trade by trade basis. The most important findings were:

  1. Winners look big to the extent that winners are kept small: If You exclude the worst three detractors, everyone would have outperformed the benchmark every year: 100% outperformance 100% of the time (before costs)
  2. Free loaders are performance killers: winners and losers are visible, so they get dealt with. The problem is to identify positions that are neither one nor the others, free loaders. They do not contribute enough to compensate for blow-ups and do not lose enough to be visible. They mobilise resources that should be deployed on productive assets. The psychological consequences is called capacity: you are at capacity when inertia sets in, when you do not want to take a trade because you think you are too big

The two issues that cause managers to underperform are directly related to Absence of EXIT policy

II Mental edge: 90% of trading is mental, the other half is good math

Optimism peaks before entry. After that, emotions kick in. The ultimate proof of this is divorce statistics…

1 Pre-mortem

Few market participants give bet sizing the importance it deserves. It is often either conviction (feel good) based or equal weight. Once they are in a position, things change. Or, more accurately, their perception change: they have either too much or too little of a stock.

The concept of pre-mortem is finally finding its way in decision making: Nobel prixe winner Daniel Kahneman, Dan Gilbert (positive psychology Penn U), Hal Hershfield (future self). The idea is to visualise the worst possible outcome and plan accordingly.

If for every trade You are about to take, you visualise a stop loss, something interesting will happen: you will size positions smaller. That is a natural reaction.

In other words, the most important question about fund management is: could i live with earning a little less than i could or could i afford to lose a lot more than i should ?

Again, the only way to grasp this is to mentally think about exit first

2 Kubler Ross: market participants grieve their way to selling

I am a professional short seller. On the short side, you make money by selling along people who liquidate their long position. You lose money by selling short crowded shorts…

There are many psychological charts about euphoria to despondency. They are all nice, adorable and lovely, but they are written from people whose perspective is to go Long. They do not understand the psychology of selling. There is one model that grasp the emotions we go through; the psychology of grief popularised by Elizabeth Kubler-Ross

The view from the short-side: how we process emotions and the market signature of the 5 stages of grief Kubler-Ross by Laurent Bernut on Alpha Secure

Market participants grieve their way into liquidating losers and are too complacent when leaving winners.

3 The psychology of stop loss

Even if You do not agree with what you have read so far, You must read the post below.

All diets w/o exception have failed: statistics show that we are all getting fat year after year. Diets address the wrong problem. They talk about what we eat, not about how we relate to what we eat. Diets are a psychological issue, not a physiological one.

Stop Losses are exactly the same. They are an identity issue, not a statistical one. The ego associates being profitable with being right. So, losing money is an attack on the ego. We rationalise, change our beliefs (Festinger and ognitive dissonace, Gazzanikas and split brain theory) rather than kick out losers.

Read this post to learn how to reframe your beliefs and execute stop losses like you brush your teeth.

The psychology of stop loss: how You can be 100% right despite 60% failed trades by Laurent Bernut on Alpha Secure

Conclusion:

Making money in the market is about having an edge. There are two types of edge: statistical and mental. The only ways to tilt your trading edge is to start with the end in mind: think about how you are going to exit before you enter

What’s the biggest mistake that stock market investors make?

@Quora: Do simpler trading strategies make the psychological aspects of trading more manageable thus making them better for rigid implementation …

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My answer to Do simpler trading strategies make the psychological aspects of trading more manageable thus making th…

Answer by Laurent Bernut:

Excellent question. Complexity is a form of laziness. 1) The privilege of simplicity is that it imposes itself, even to those who do not understand its sophistication 2) Simplicity is the exact opposite of easy.

Complexity is fragile

I have met many people trading complex strategies. I have had the privilege of meeting many people with long track record. I have yet to meet trading complex strategies with a long track record.

Complexity gives an illusion of control. It is also highly specialised. So, it tends to fall out of sync. then, traders start drifting and tweaking and add one more widget instead of subtracting.

As the Great Chinese philosopher Bruce Lee used to say: ultimately, perfection runs into simplification.

Complexity is a form of laziness:

People who settle for complex solutions have not worked hard enough to simplify them. It is easy to throw another oscillator in the mix. It is simplistic to optimise for a moving average. Below is my screen, no indicator, no oscillator, nothing, just the purity of the price:

In fact, the opposite of simple is not complex. The opposite of simple is easy.

The privilege of simplicity is that it imposes itself, even to those who do not understand its sophistication. We can understand it. It intuitively imposes itself.

Simple is not rigid, it is fluid

You will know that You are on the right track when You start subtracting instead of adding to your strategy.

I started off with 9 exit conditions. Now, I have 2: trend reversal and stop loss.

I started off with 3 distinct strategies. Now, i have 1 unified strategy. It knows when to suspend trading, reduce risk. Hint: the answer is not in the Buy/Sell signal but in position sizing and rejection of small orders…

But behind this simplicity there is immense relentless kaizen. It took me years to see what was in front of me. We do not see things as they are. We see them as we are.

Simple is a way of life.

The false comfort of complexity

People are intimidated by complexity. If it is simple, they believe that anyone could do it, therefore it cannot work. Picasso once drew a picture on a napkin to a restaurant owner and then asked for an astronomic sum. He then told it took him 30 years to draw those simple lines.

People often mistake simplistic and simple. I often ear that what i do is superficial. Perfect, “let’s step into the math then” and 5 minutes down the road, they have an angelic blank stare and conclude it is too complicated.

Simple rules

  1. Stop Loss: 2nd most important variable
  2. Position sizing: money is made in the money management
  3. Exits: You have to get off the bus at some point
  4. Entries: vastly overrated
  5. Above all: clarity of purpose, of formalisation. Be specific, very specific

Do simpler trading strategies make the psychological aspects of trading more manageable thus making them better for rigid implementation …