How trading differs from allocating and why traders have lost their edge to trading bots.
In this episode you’ll learn:
- How stock exchanges have changed in the last few decades.
- Why most trading is done by computer algorithms instead of people.
- How stories drive stock markets.
- How information can change the leading marketing narrative.
- How traders differ from allocators and what does it take to be successful at each.
Why You Shouldn’t Trade
In the late 1990s, I was invited to visit the floor of the New York Stock Exchange (“NYSE”). This was a heady time for the stock market with the ongoing Internet boom.
At that time, there was much talk about how the NYSE was being overtaken by an electronic exchange called NASDAQ, where most of the new dotcom and other Internet startups listed their stock shares.
The NYSE was a traditional exchange. On the trading floor there were specialists—men and a few women who were responsible for facilitating the trading in specific stocks.
They did this by displaying their best bid and ask prices, executing trades, and assuring a liquid market by occasionally trading their own inventory of stock when there was large mismatch between the volume of buy and sell orders.
I don’t remember much about my visit to the floor of the exchange. It seemed quite orderly so it must have been a quiet market day.
I do remember eating at the NYSE Luncheon Club, a member only club on the 7th floor of the exchange. Apparently, this club didn’t install a women’s restroom until 1987, about twenty years after women were admitted to the NYSE.
I don’t think the NASDAQ has a luncheon club. Mainly because there isn’t a central location where trading occurs. Rather, market makers disbursed across the country facilitate trading electronically.
NASDAQ was only the first computer-based exchange that started eating the NYSE’s lunch. Now there are many other public and private electronic exchanges where institutions can trade stocks. These private exchanges are called dark pools.
Why do these alternative exchanges exist?
Because large institutional investors that trade millions of shares of stock want to be able to do so without impacting the price.
Recently, I received an email from a professional trader who has traded oil futures for Wall Street and European firms for years.
I mentioned to him I admire anyone who can make money trading oil futures given the random way prices seem to jump around.
He replied with something very telling. “No one can trade oil without customer flow.”
What he meant was there is a great deal of value in knowing how badly someone wants to buy or sell something and how much they want to buy or sell.
An institutional investor who wants to buy millions of dollars of a particular stock will do everything it can not to make that intention known by breaking up the order into smaller chunks and trading on various public and private exchanges.
That way they are less likely to push up the price and signal to other investors and market makers what they are doing.
On the flip side, there are many institutional traders and liquidity providers who seek to profit by knowing what the order flow is.
It is a game of cat and mouse. And much of it is played out using computers and quantitative algorithms.
The Rise of the Trading Bots
Ben Hunt, a long-time institutional portfolio manager and currently Chief Risk Officer at Salient Partner, recently wrote, “As much as 70% of the trading activity in markets today—activity that generates the constantly changing up and down arrows and green and red numbers [that retail investors] see and react to on CNBC—is just machines talking to other machines, shifting shares around for liquidity provision or millisecond arbitrage opportunities.”
Financial economist David Rosenberg commenting on the recent market turmoil said, “The rapid down move in equity prices…likely had less to do with fundamental macroeconomic factors and more to do with the vagaries of technical algorithms and program trading, dark pools and market liquidity.”
In such a world, individual investors who think they can compete trading stocks, commodities, or currencies are sadly mistaken. They will be outgunned in this global trading arms race.
Perhaps in the late 1990s during the Internet boom it was possible to make money day trading, and I am sure there are still a number of individual retail traders who turn a profit. Many of those same traders make even more money teaching others how to do trade.
Still, the reality is with so many institutions competing with each other for a competitive trading edge by using quantitative algorithms, including many who have access to order flow data, the odds of becoming successful trader are miniscule.
Even many professional traders on Wall Street have given up trading to join hedge funds and other firms to help quantitative analysts create and program computerized trading algorithms.
It reminds me of one of the closing lines from the 1983 science fiction film War Games when the supercomputer states, “The only winning move is not to play.”