Stories

Why Wall Street still needs human traders

A rush into algorithmic trading is setting up a concentration of faith in computerized investment, a trend that could backfire in intensified volatility and lowered returns.

What's going on: By 2025, Wall Street firms are expected to shed 10% of their work force — 230,000 jobs — as they embrace intelligent algorithms, trading that relies on crunching mountains of data and sometimes anticipating the actions of human traders (see chart, below), according to the financial consultancy Opimas.

Data: Opimas; Chart: Andrew Witherspoon / Axios
  • Such trading is soaring: Quantitative hedge funds, or quants, doubled their share of U.S. stock trades to 27% from 2013 to 2016, and are now just under the 29% carried out by individual investors, the WSJ reports.
  • The leading edge of this movement uses machine learning, programs that write their own algorithms for use in trading. Humans continue to play a role in all of these trades.
  • But results can tend to decay: Programs inadvertently converge on the same strategies, engaging in algorithmic group think. When that happens, returns fall.

These trends are visible in other data: A recent J.P. Morgan analysis showed that 60% of all equity assets are now owned by passive investors (think index and exchange-traded funds) or quantitative funds, double the less than 30% that they held 10 years ago.

Why to worry: There is evidence that as more and more algorithms are employed to find inefficiencies in the market, their tendency to arrive at similar conclusions puts the market at risk of dangerous swings in asset prices.

  • Humans strike back: Though the returns for traditional Wall Street methods like the analysis of corporate fundamentals and economic trends are falling, asset managers are finding ways to adapt. Big hedge funds like SAC Capital and Tudor Investment have been hiring algorithmic trading experts, while startups like financial-data platform Elsen are offering services that help smaller money managers do traditional data crunching more efficiently and take advantage of machine learning technology to supplement their insights.