What happens to the good traders?

Thursday, 26 October 2017
Melbourne, Australia
By Kris Sayce

  • Many people knowingly choose poorly. Here’s why…

According to eFinancialCareers.com:

The technologies revolutionizing Wall Street have left traders and Washington regulators alike playing a perpetual game of catch up. In the current age of algorithmic trading, even those who can’t quite hack it as a quantitative trader may have a future working for a regulator such as the Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC)…

To keep up [with the fast-changing industry], agencies like the SEC and CFTC are changing who they hire. They could take a page from hedge funds and investment banks that have been turning to people who know how to write computer code, not just traders with MBAs.

“What you really need are computer geniuses who failed as algorithmic traders and are willing to switch sides,” Brad Bennett, who was head of enforcement at the Financial Industry Regulatory Authority until earlier this year, told Bloomberg. “You have to be able to deconstruct the code.”

If failed quants go to work for the regulators, the natural question is: What happens to the successful quants?

The answer is that they retire at 37, and then, when they’re bored of retirement, they come and work for us! It was a bit of a coup when, three years ago, our colleague, Greg Canavan, convinced a retired quant trading expert to come out of retirement.

He agreed, although not immediately. But once he realised he had the opportunity to help ordinary Aussie investors invest using the techniques often used by big Wall Street hedge funds, he jumped at the chance.

The quant in question is Jason McIntosh. He runs one of our most successful and popular premium trading advisories, Quant Trader. And next week, Jason has kindly agree to ‘train’ budding investors in the art of trading the quant way.

Stay tuned for details. You won’t want to miss it. We’ll share more information with you on Monday. Until then, check out the latest from the markets, followed by a guest essay from the aforementioned Jason McIntosh.

Markets

Overnight, the Dow Jones Industrial Average fell 112.3 points, or 0.48%.

The S&P 500 fell 11.98 points, or 0.47%.

In Europe, the Euro Stoxx 50 index fell 19.23 points, for a 0.53% fall. Meanwhile, the FTSE 100 lost 1.05%, and Germany’s DAX index fell 0.46%.

In Asian markets, Japan’s Nikkei 225 index is up 35.36 points, or 0.16%. China’s CSI 300 is up 0.58%.

In Australia, the S&P/ASX 200 is down six points, or 0.1%.

On the commodities markets, West Texas Intermediate crude oil is US$52.10 per barrel. Brent crude is US$58.37 per barrel.

Gold is trading for US$1,279.78 (AU$1,659.13) per troy ounce. Silver is US$16.99 (AU$22.03) per troy ounce.

The Aussie dollar is 77.14 US cents.

Bitcoin is US$5,736.90.

Now read on for the guest essay from Jason McIntosh…

Cheers,
Kris


Many People Knowingly Choose Poorly. Here’s Why…
Jason McIntosh, Editor, Quant Trader

Algorithms get things wrong…

Yes, you read that correctly.

Just like you and me, algorithms aren’t always right.

GPS devices are a classic example. They mostly direct us with ease and efficiency. But occasionally they’ll get ‘lost’ and require a human to sort things out.

Take this for instance…

A trio of Japanese tourists got a lesson in algorithmic errors in 2012. The students set off on a daytrip to Queensland’s North Stradbroke Island. But it all came to an abrupt end.

You see, their GPS didn’t account for the water crossing. The tourists got 500 metres into Morton Bay before getting stuck. They say the device was directing them to a road.

Laugh, you may.

But this story isn’t a one-off. You’ll find many instances of GPS devices getting it wrong.

And then there are driverless cars…

The algorithms behind these vehicles are amazing. But just like their GPS cousins, they aren’t infallible. There’s already been one fatality, along with a string of lesser incidents.

So, should you be wary of algorithms?

In a moment, I’m going to show you mistakes from a trading algorithm — Quant Trader. You’ll then be able to weigh these against the potential benefits.

But first, I’m going to tell you about some research I saw this week.     

Man versus machine

Imagine the setting…

You’re the chief admissions officer for a university’s MBA program.

Demand for the course is high — there’ll be many who miss out.

Your job is to select applicants who’ll likely be most successful after graduation. Get it right and you’ll receive a financial reward. Get in wrong too often and you’ll lose your job.

There are two ways of making your selections:

  1. Go through each application and handpick the students; or
  2. Use an algorithm to make forecasts based on past student intakes.

Which would you use?

Remember, there’s a lot riding on this. Do you put your job in the hands of an algorithm, or should you back yourself to make the best decisions?

This was the choice researchers gave participants in a recent study. It probably comes as no surprise that most people backed themselves.

But this next bit may surprise you.

People continued backing themselves even when they knew the algorithm had greater accuracy.

Researchers at the University of Pennsylvania (Dietvorst et al. 2014) found that many people opt for human forecasts, despite knowing an algorithm is better. This is in line with other studies.

The researchers call this ‘algorithmic aversion’.    

So what causes this?

Well, it all comes down to confidence.

People are quick to abandon an algorithm after seeing it make a mistake — even though the overall outcome may be better. They’re also more forgiving of their own shortcomings. 

You can read the research paper here.  

Another interesting study involves the game of chess.

The highest ever rating for a human is 2,882. This was by world champion Magnus Carlsen in 2014. By comparison, today’s supercomputers have a rating of around 3,300.

A victory for algorithms, you may think.

But not so fast.

According to Tyler Cowen in Average Is Over, the best players aren’t human or machine — it’s a pairing of the two. You’ll see this happen in freestyle championships.

Interestingly, the top teams are often a computer and a strong club player. Cowen notes some big failures by grandmaster/computer pairings. Their downfall is often due to an overconfident human ignoring the computer.  

Chess isn’t the only area of algorithm/human pairings.

An example you may be familiar with is Quant Trader — my algorithmic trading system for ASX stocks. The algorithm identifies the trades, you then decide which ones to take.

Just as in chess, this can potentially be a potent combination. 

Be honest…

So how does algorithmic aversion relate to trading?

Well, the previous study notes unrealistic expectations. It says that many people were expecting the algorithm to be perfect, and were put off when it wasn’t.  

I believe this mindset makes algorithmic trading difficult.

Have a look at this:



chart image

Click to enlarge

This is a recent algorithmic ‘error’ — a trade in US Masters Residential Property Fund [ASX:URF].

Quant Trader gave a buy signal for URF at precisely the wrong time. The system’s entry point was at a peak in the share price. The stock hit it exit stop last month.

I’ll occasionally get emails about trades like this.

People tell me the entry signal was obviously wrong. They’ll also note the trade should have been cut much sooner. The algorithm, they believe, clearly didn’t do its job.

Then there are examples like this:



chart image

Click to enlarge

This was a trade in Hansen Technologies Ltd [ASX:HSN].

Unlike URF, Quant Trader got the entry right. The shares rose by 92% within a year. 

The problem this time was the exit.

You see, Quant Trader calculates a unique exit point for each trade. This removes any guesswork about when to sell. It also helps you manage your trades consistently.

But it’s not an exact science.

You’ll notice HSN hit its exit stop on the day it made a lasting low. The shares then rebounded the next day and didn’t look back. If only the algorithm had set the exit point 5 cents lower.   

Situations like these make some people wary of algorithms. As the researchers found, many people focus on what an algorithm got wrong — not what it gets right.

Have a look at this:



chart image

Click to enlarge

This is another of Quant Trader’s live signals — a trade in Smartgroup Corporation Ltd [ASX:SIQ].

Now, I’m going to ask you a question…and you need to be honest with yourself.

Could you trade like this without an algorithm?

Here are some thinking points (align the numbers with those on the chart):

  1. Would you have held on during the sharp 21% drop a few days after entry?
  2. Would you have resisted a quick 30% gain in the first month?
  3. Would you have resisted a 100% gain within four months?
  4. Would you have held your nerve during the 29% correction in early 2016?
  5. Would you have been patient during 20 weeks of sideways trading?
  6. Would you have had a plan to cash in a 141% profit after 14 months?
  7. Would you have even known about SIQ in the first place?

Yes, an algorithm can get things wrong. But it can also get a lot right.

The researchers say this:

Many decisions require a forecast, and algorithms are almost always better forecasters than humans.’

I agree.

I know that my own trading is better with an algorithm. My decisions are more consistent, my use of time is more efficient, I find more opportunities, I have less stress, and I make more money.

This could be a reality for you as well.

I believe your advantage is that you know the inside story. You know an algorithm could lift your trading to a level few humans can attain on their own.

The age of the algorithm is here. I encourage you to use it to the fullest. 

Until next time,

Jason McIntosh,
Editor, Quant Trader

Editor’s Note: Remember, look out for your invitation next week to join Jason McIntosh in a quant trading ‘training’ masterclass.

All graphics produced by Quant Trader