In the past decade, Wall St. has enlisted some sophisticated money managers who no longer wear business suits or compete for the office with a view. Large, well-known financial services firms now embrace machine learning that has opened up numerous employment opportunities in the construction of robo advisors. Traditionally, brokerage houses and investment banks have always employed developers and data scientists, but those roles largely revolved around information security and operational efficiency. The advent of automated financial consulting opens up a whole new world for computer programmers bent on commanding optimal salaries with rich benefit packages layered on top.
Betterment and Wealthfront, two leading robo advisors founded in 2008, have drawn tenured heavyweights such as Fidelity Investments, Vanguard, and Charles Schwab into the battle for assets under management. With corporate pensions vanishing, individual investors bear greater responsibility in funding retirement and other savings goals. That shifting paradigm should help propel the machine learning as a service market to grow at a compounded annual rate of 43.7% through 2021.
What is a Robo Advisor?
Human portfolio managers and robo advisors both share the same objective: outpace the performance of passive stock market indices, most notably the Standard & Poor’s 500. The end goal might be the same, but the means to that end differs like apples and almonds. Before engaging computers that effortlessly analyze big data and design risk-adjusted portfolio models, investors mostly turned to stockbrokers for investment advice. Times have changed. Clients who realized subpar stock market returns couldn’t quite stomach lousy performance or high transaction fees levied by their living, breathing financial consultants. Sensing opportunity, Betterment’s Jon Stein and Eli Broverman arrived on the scene, utilizing Modern Portfolio Theory (MPT) that seeks superior returns through diversification. Adding to the appeal, minimal human intervention relegates the robo advisor’s ultra-low fee structures to 0.25-0.50% of assets under management.
The new breed of investment company positions robo advisors as a standalone platform. Veteran firms, without skipping a beat, have poured vast amounts of capital into bots, parking them alongside human counterparts who rely on instinct and experience to assist their clients. Goldman Sachs extended its reach into the mass affluent market, targeting prospects whose net worth sits below $1 million. Catering to tech-savvy clientele, the major industry players hope to corral millennials who prefer a digital, set-it-and-forget-it approach to wealth management.
A Day in the Life of a Robo Advisor Programmer
What does a typical day involve for technology professionals who create and install artificially intelligent investment programs? Job duties generally focus on data gathering, clustering and analysis aimed at determining the direction of the stock market. Taking approaches such as the Hidden Markov Model, programmers can be expected to write algorithms designed to predict the future behavior of stocks. The Markov model examines day-to-day changes in a stock’s price along with the high and low price levels achieved by the issue during the previous trading session. Along with measurable constants, some predictive models factor in subjective data such as investor sentiment and news content. The goal of the individual programmer becomes creating programs that analyze millions of market-related data sets, effectively teaching a machine to forecast stock values based on past performance and real-time events. Naturally, not all programs run right the first time. Some time each day will need to be set aside for debugging, ensuring correct outcomes.
While coding occupies about half the day, mornings give data scientists the chance to strategize. Discussions might include how current assumptions could be tweaked to create the optimum asset allocation model from a client’s risk tolerance, time horizon and investment goals. Since portfolio returns always merit investor concern, programmers may pursue talks with financial analysts about asset pricing theories such as the Fama-French Three Factor Model. This concept suggests that the broad markets can be outperformed through buying and holding small-company and value stocks. Incorporating that theory into a practical software application rests in the hands of the information technology expert.
Tech Jobs & Salaries in the Robo Advisor Realm
The companies below are evaluated using Paysa’s CompanyRank – which is a measure of talent flow. The hierarchy developed by Paysa bases its rankings on 7.45 million job changes between 198,000 companies over more than 15 years. As lower-rated firms hire talent from higher-ranking industry counterparts, that migration of employees boosts the ranking of the acquiring company. The opposite also holds true. Talent hired from lower-ranking companies dilutes the ranking of entities that sit higher in the Paysa hierarchy. Some emerging trends can be seen below among the investment firms whose robo advisor platforms define or supplement their existing wealth management services.
WealthFront Vs Betterment – Talent Flow
A leading robo advisor firm, Wealthfront, ranks 55th out of 198,000 technology companies. Since November 2014, Wealthfront has witnessed a significant spike in talent flow from higher-rated competitors, driving its Paysa ranking from 4,434 to 55 as of March 2016. Businesses such as Wealthfront have developed a strategy that focuses on an ideal mix of innovators and investment theorists. Annual salaries for Wealthfront technology positions average about $165,000, inclusive of bonuses.
Betterment, another leading robo advisor trails significantly behind Wealthfront according to Paysa CompanyRank. However, the company has steadily acquired recruits from upper echelon technology concerns. In March 2013, Betterment ranked 9,116 on the Paysa scale, trending up to 2,320 over a three-year span. The top 25% of Betterment IT workers average more than $156,000 with equity packages and bonuses thrown in.
More geared toward deploying human advisors, Fidelity, Vanguard and Charles Schwab have still managed to throw their hats in the ring of automated investment services. In stark contrast to the new wave of financial services, the level of talent attracted by the storied firms has seen a prolonged decline in recent years. Vanguard’s talent level, according to Paysa measures, has fallen from a rank of 392 to 763, and Schwab’s standing dropped from 223 to 370 over three years ending in March 2016. While Fidelity has been rebounding since March 2015.
Fidelity vs Charles Schwab vs Vanguard – Talent Flow
Vanguard has rolled out its own robo advisor, Personal Advisor Services, that operates alongside its human financial consulting arm. The investment company offers investors an initial consultation with a person, and the subsequent level of personal contact hinges on client preference.
Seeking investment data scientists and innovation developers, the mutual fund behemoth requires applicants to have experience with Spark, Scala, and machine learning frameworks such as Torch and Tensorflow.
Joining the fray, Charles Schwab created its Schwab Intelligent Portfolios platform. The company’s robo advisor monitors client portfolios daily, rebalancing select asset classes when appropriate. The only fees incurred by investors accrue from the exchange-traded funds that comprise the account.
In March of 2016, Fidelity launched their own version of robo advisor called Fidelity Go, aimed at newer market participants. Individual portfolios take shape after investors answer a few simple questions about time horizons, savings goals and risk tolerance. Managing $2.1 trillion in assets, the 71-year-old company continually looks to fill top tech positions that include software engineers, systems analysts, and project managers.
Opportunities remain plentiful in the IT departments of new and old-school financial service providers, thanks in part to the phenomenon in which computers now pair with people to provide portfolio advice. Machines that interpret and act on big data have made their way to Wall St., where investment returns rule the day. Matching job seekers with job openings, Paysa offers boundless information on tech salaries, required skill sets and company rankings. For those folks not on the hunt, the site also provides a method to evaluate a current job offer or analyze current compensation packages to negotiate a raise. Unlike some personal investment strategies, putting a career on autopilot rarely achieves the desired result. Explore a proactive approach through paysa.com.