Robo Advisors: The Changing Face of Financial Advisors

Robo advisors are algorithms developed by financial firms to replace human advisors essentially for speed, reduction of cost and higher precision. Prior to robo advisors, human advisors in investor firms were charging high commissions for taking positions. Often, despite these charges the profits were made proportionate to the profits made.

The other perspective for corporations was that they were using expensive advisors for small investors. They started to develop algorithms to automate the process. They proved to be so cost effective and efficient that they decided to continue with them. Previously these trading software were provided to human investors who charged 1 to 3% commissions on the amount invested. Now the software works without these intermediaries.

Investment firms that had doubled up on human advisors ended up having huge overheads. Firms like Mint who bet on technology made big gains. The rise of robo advisors was also hastened with changing investment patterns.

Robo advisors started by helping in portfolio management and asset allocation and moved onto retirement fund management and tax-loss harvesting. Robo advisors helped make the market more accessible to all types of investors, not just high-worth ones. Cash flow management, tax planning, education loan management, home loan management are all coming under the ambit of robo advising.

Why robo advisors at all? The Financial Conduct Authority in its Financial Advice Market Review says that several million people in the UK desperately need financial advice but cannot avail it because of the prohibitive cost. This is known as the financial advice gap. They believe that streamlined advice from robo advisors can fill this gap effectively. These automated systems will first ask a series of questions and based on the customer response to those, advice will be provided. Early movers in this segment in the U.S (as early as 2008) have been Wealthfront and Betterment. They removed the intermediaries from the investment game by providing algorithmic investment advice.

The concerns regarding robo-advice though is that the advice may be skewed in favor of certain portfolios or only a limited choice made be available. Incomplete information may also lead to wrong selection of products. Despite these flaws, there is much to be gained by using robo advisors. The much touted ‘human touch’ or benefits of ‘face to face’ interactions are not always available. Sometimes objective advice is not at hand when human advisors have an agenda to push certain investment products over others. Plus advice from investment managers does not come cheaply and ends up deterring small investors from seeking financial advice.

Charles Schwab released its robo advisor to realize happily that they opened up an entire new segment of investors for their service. These investors were happy to invest without the jargon of high finance, yet another benefit of Robo advisors.

Robo advisors charge ~0.4% of invested assets as compared to 1 to 2% of traditional human investors. You need as low as a dollar to invest with a robo advisor while an investment manager is looking at a minimum of $25000.

CNBC reports that the investment size by robo advisors will be $489 billion which is 22% of the $2.2trillion managed by individual investment advisors today.

The instruments robo advisors are used in are limited. The future will see robo advisors taking positions in a variety of financial investment products, so much so that it may the way we invest in the future.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.