In Brief:

      • Wall Street firms continue to invest billions into technology in the hopes that high-frequency trading will rake in billions.
      • Quantitative analyst careers are now a hot commodity as investment banks and hedge funds begin to rely more on automated trading algorithms.
      • High-frequency trading allows banks to profit off of arbitrage in the blink of an eye.
      • Some financial regulators have pushed back against total reliance on supercomputers.
      • A flash-crash occurred in 2010 when one trader caused the market to tank by $1 trillion when he used a “spoofing algorithm”.

With a smartphone in hand, hair slicked back, sporting a thousand dollar Armani suit and two-thousand dollar gold, Rolex wristwatch, the Wall Street banker enters any one of the numerous windowed high rises that inhabit lower Manhattan. Like his predecessors, this banker is brash-talking and assertive, cool and confident. He scours the Wall Street Journal for his next lead before picking up his desk phone for the first of many times that day. At least, that’s what you probably think.

The stereotypical Wall Street financier is best depicted in the movie Wall Street. Gordon Gekko is the “it-guy”, typifying the yuppie zeitgeist of the 1980s. Gekko arrives by chauffeur to his Wall Street trading firm in the morning, hashes out million-dollar deals by lunch, and pencils in a tennis match at his exclusive athletic club in the afternoon before making another couple million by the end of the day. When not at the office, the legendary trader can be found in his multi-million dollar Manhattan highrise or relaxing at his mansion in the Hamptons. 

For decades before and after that seminal movie came out, prospective Wall Streeters have mimicked Gekko’s looks, mannerisms, and tastes.  For many years, careers in investment banking and financial analysis have garnered plenty of attention due to their attractive entry-level salaries and number of open positions, but that’s all likely to change. Traditional trading and asset management jobs are rapidly transforming. Traders are now expected to have some background in programming. Quantitative analysts are one group that have seen a large rise in demand.

Wall Street traders sitting

Defining the Quantitative Analyst

Quantitative analysts or “quants” as they are colloquially known, are the proclaimed rocket-scientists of the global banking system, the nerds if you will. As masters of advanced calculus, linear algebra, computer science, and economics, these men and women are tasked with creating complex mathematical models that maximize profit while reducing risk. Many quants choose to pursue careers in finance rather than science or engineering due to the hefty payoff.

If the trader is the Formula 1 driver, the quant is the pit crew, ensuring everything runs smoothly and constantly researching ways to maximize the car’s performance. Front-office quants provide traders with pricing and trading tools to gain an edge on the competition, all in the name of making billions. Similarly, back-office quants model complex derivatives through multiple computer programs and conduct research on new trading strategies.

Wall Street quant working at computer

How In-Demand Are Quants?

The demand for quants is largely due to the 2008 Recession. Up to that point, traders would rely on instinct, sales skills, and a mastery of the phone to turn a profit. Just a decade ago, trading floors bustled with people barking orders into their phones to short stocks, or play the long game.

Veteran John Mack, an executive at Morgan Stanley, would tell his traders, “there’s blood in the water, let’s go kill.” It is exactly this brash approach to trading that tanked investment banking stalwarts Lehman Brothers and Bear Stearns, once considered “too big to fail”. As a result, large investment banks like Goldman Sachs sought to automate the trading-floor as much as possible in order to eliminate human error from the equation.

Although quants are by no means new to Wall Street, their role in day-to-day trading has vastly transformed. In the old days, quants worked with traders to provide solid information on where the market was going. Yet, traders would still act on their own gut-feeling when placing trades. Nowadays, the line between quant and trader has all but disappeared. Traders must be able to interpret the algorithm that supercomputers produce. The days when quants were relegated to a backroom are over. Citigroup and Goldman Sachs now offer introductory computer science courses for traders on systems such as Python and C++.

The mandate for tech-savy workers on Wall Street is clear. The U.S. Bureau of Labor Statistics estimates that the job market for quants will grow 16 percent by 2022. JPMorgan Chase now spends an estimated $10.8 billion per year on tech, more than any other bank. The investment bank also expanded its introductory courses to include data science, machine learning, and cloud computing. In the era of high-frequency trading, coding knowledge is a must.

Wall Street trader at Goldman

What is High-Frequency Trading?

High-frequency trading refers to automated trading done by investment banks, hedge funds, and institutional investors. These automated traders can conduct millions of transactions in mere seconds, providing an advantage to anyone that uses them. They can also spot variations in the market in a split-second and execute the necessary action. 

Because supercomputers can place trades at lightning quick speeds (250 milliseconds), they can take advantage of price deviations between two or more markets. In a financial concept known as arbitrage, an asset is purchased in one market and sold in another for a higher price. Computer systems often correct the price imbalance in a matter of seconds, but high-frequency-traders can spot the error and rack up millions in profit before the error is remediated.

Wall Street supercomputer

Keeping Technology in Check

The irony of the leap toward more automated processes in trading is that the risk may be even greater. In the “Flash Crash” of May 6th 2010, an estimated $1 trillion was lost and then mostly recovered in the span of just 36 minutes. In a brief, yet chaotic moment, the Dow plunged over 1,000 points. British trader Navinder Singh Sarao used a trading practice known as “spoofing” to place orders on thousands of S&P 500 index future contracts.

In total, Sarao’s order amounted to a $250 million bet that the market would fail. Sarao knew that high-frequency trading computers would think that sell orders outweighed buy orders meaning the market was expected to tank. In response, these automated traders would sell their positions in order to lose as little money as possible. When prices rose, Sarao would sell his futures for a massive profit.

Since the Flash Crash of 2010, financial regulators have taken steps to minimize the risk that high-frequency-trading can create. The SEC now requires anyone who designs trading algorithms to become a licensed securities trader. The license means quants can be held accountable for the positions they take.


Looking Ahead to the Future of Wall Street

The days of the brash Gordon Gekko stereotype may be a thing of the past, but that doesn’t mean Wall Street is any less competitive. Like many other industries, technology is reshaping the skills needed to succeed. It’s no longer enough to have a mastery of the phone. Traders now rely heavily on quantitative analysts for their marching orders. The shake-up in the financial industry has thousands of industry veterans and bright-eyed college students all thinking the same thing – “It’s time to learn to code”.

AskMrFranchise first steps to franchise ownership

By Tyler Dikun and Jim Notaris

Tyler and Jim bio

Callens Capital