How Using A Robo Advisor Will Make Your Portfolio More Valuable

taliTali Soroker is a Financial Analyst at I Know First.


Robo Advisor


  • What is robo advising?
  • Is it safer to invest with a robo advisor versus a human advisor?
  • Pros and cons of robo advising
  • How do robo advisors work?
  • The construction of IKF’s robo advising system

An Introduction to Robo Advising

Robo advisor

(Source: Google)

Robo advising is a new way to get portfolio management help without having to pay the higher percentage fees that are typically charged by traditional advisors. They tend to provide more basic wealth management services, not delving into the world of taxes and retirement or estate management as human advisors do for their clients. There are both advantages and disadvantages to this form of advising, but it is a popular tool for millennials and younger investors that are just starting to dip their toes into the financial world. Users are primarily people who feel more comfortable with the modern interface and may not have such large sums to invest.

Robo advisors were just recently introduced to the general population of investors in 2008, though human financial advisors have been using similar automated wealth management systems since the mid-2000s. The concept is simple, though the creation of these systems involves advanced algorithmic technology. Robo advisors are automated systems that perform asset allocation and portfolio management without the help of human advisors. What started as a basic tool in the middle of one of the biggest financial crises of our time to aid in investors’ portfolio recovery has now developed to include additional advanced services such as tax-loss harvesting.

Robo Advising Vs. Human Advising: Which is Safer?

robo advisor

(Source: Google)

Robo advisors each approach the market in a unique way, no system works exactly the same as any other. The algorithms behind the investment strategies vary in the focus of their fundamental analysis and each will invest your money in a different way with a different weighting of asset allocation. In this way, robo advisors are incredibly similar to human advisors, this is nothing new.

Just like human advisors, robo advisors personalize the program to minimize risk for the investor, reduce variability, and maximize returns based on the investor’s own preferences, needs, and goals. As a new investment technique, the amount of historical data that can be tested for performance is limited. However, even human advisors have started to use these automated advising systems to help manage their clients’ assets.

Robo advisors have clear benefits, particularly for young investors and those without the time and resources to dedicate to hands-on investing with a human advisor, such as ease of use and cost efficiency. At the same time, it is important to understand the risks involved in relinquishing total control to an automated system. When users open an account with a robo advising service, there are questionnaires that help to personalize the system and assess the level of risk to be taken with the investment strategy, but at the end of the day, there is still a computer system that is making impactful decisions with investor money.

This can be seen in both a positive and negative way. For many investors, the idea that there isn’t a person somewhere analyzing and tweaking the computer’s decisions is nerve-wracking. On the other hand, high-level analysts have also come out praising the abilities of algorithmic systems that are modeling the stock market. Algorithmic models of the stock market can take into account years and years of financial information and find patterns in it that humans can only dream of seeing in such inordinate amounts of data. The system can then analyze risk factors, variability, and other fundamentals to manage an investor’s portfolio and can automatically rebalance the portfolio when the market shifts.

Considering the similarities and overlap that exists between robo advisors and traditional advisors, the biggest downside to using a robo advisor seems to be unrelated to the actual financial advice that investors receive. There are no guarantees when investing in the stock market, anybody thinking about investing money should be well aware of this fact, but that doesn’t make it hurt any less when the market plunges. Robo advisors can’t be there to hold your hand and ease your fears when this happens, but that doesn’t mean that they can’t be useful investment tools for a wide variety of investors.

The Ins and Outs of Your Robo Advisor

Just like human advisors, each robo advising service has a unique approach to analyzing market conditions and

robo advisor

(Source: Pixabay)

therefore will construct client portfolios differently from any other system. Despite differences, all robo advisor firms construct their automated systems based on the use of Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH), and questions to determine their clients’ risk profiles.


Modern Portfolio Theory is an investment theory that was first put forth by Harry Markowitz in 1952. The theory is based on the idea that investors can optimize their stock portfolios to maximize returns for their given level of market risk. A large part of MPT involves looking at the expected risks of many stocks and seeing how the risks play off of each other. For example, looking at one stock that will go up given certain weather conditions and one that will go down for the same conditions. By doing this, investors can reap the benefits of a diversified portfolio by positioning assets in order to decrease the overall risk of the portfolio.

Firms design algorithms that use many advanced formulas included in Modern Portfolio Theory, as well as Sharpe ratios, Post-Modern theory, and other variations to minimize risk and reduce variability. One popular model for robo advising algorithms is the Black-Litterman Model, which is a combination of the Capital Asset Pricing Model (CAPM) and Markowitz’s mean-variance optimization theory.

Efficient Frontier algorithms analyze a large variety of possible portfolio configurations and pick the one that presents the best past returns with lower volatility. These algorithms don’t try to predict future trends, instead looking only at historical data and building the best portfolio based on that information. Smart-Beta algorithms, on the other hand, look at possible portfolios from different angles in an attempt to exploit market inefficiencies. These algorithms use multiple criteria and various metrics in order to choose the best portfolio. Each robo advising system approaches the challenge from a different way as they are designed by people with opposing views and are adjusted to optimize returns based on the views of the people designing them.

robo advisorOther enticing features of many robo advising services include automatic portfolio rebalancing and tax loss harvesting. Automatic portfolio rebalancing will help keep your asset allocations at the right percentages after-market shifts. Tax loss harvesting is an interesting concept and a useful practice for investors to reduce their tax liability. The practice is done by selling a security that experienced a loss, and then “harvesting” the loss by offsetting taxes on gains and income. After selling the security, a new one is bought that is similar to the previous one to maintain the asset allocation and expected returns. Portfolios that use tax loss harvesting are outperforming regular ETFs by more than 2% without any increased risk because of the tax advantage that they offer.

Constructing I Know First’s Robo Advisor

I Know First is working now to incorporate our proprietary algorithms into a state of the art AI-Beta advising system. We currently provide customizable solutions for investors and companies based on artificial intelligence and machine learning. Our robo advising system will use similar techniques as other firms, but with the added benefit of our predictive algorithm being a crucial part of the system. Our clients, then, have fully optimized portfolios based on Modern Portfolio Theory and other variables plus predictions of future market trends from the algorithm. Thus, we combine the power of the self-learning algorithm with the ease and efficiency of robo advising.