Denice R. Preston
Robo-advisors have gained much attention in the fintech industry in the last ten years, encouraging greater involvement of young investors and the general public in investing and saving. Robo-advisors seek to open up financial advice and provide quality services previously accessible only to sophisticated, wealthy investors. In simple terms, the term robo-advising refers to a method of investment management that delegates portfolio strategies and portfolio management to an algorithm. Rebalancing and portfolio construction are made more accessible with computers, resulting in lower-cost wealth management services and potentially reducing human mistakes and bias.
Many startups in the industry struggle to reach profitability and demonstrate their brand distinctness. What is the best method for robo-advisors to open up investing to the masses and make an income while revealing the risks associated with an investment portfolio?
Where did Robo-advising originate?
Betterment and Wealthfront are among the top well-known robo-advisors, the latter having been the first to debut in the year 2008. In 2019, the industry was estimated to contain 440 billion of assets under management worldwide, and over time, traditional wealth managers, such as Vanguard, are also adopting the same strategies. Although they are distinct from trading platforms that only execute trades, such as Robinhood and Vanguard, both industries promote financial empowerment. It is being adopted by investors of younger age, who typically haven’t had a keen interest in savings for retirement until later in their career.
One of the most essential advantages of robot advisors is that they assist clients in understanding the risks and costs of portfolios rather than focusing solely on the returns. The main argument against traditional wealth management by financial advisors is the misalignment between incentives. Often, costly and unreliable funds are distributed to investors who cannot interpret the numbers objectively to gain an understanding of the performance. In this regard, robo-advisors have been advocates of passive investing by avoiding the expensive funds managed by actively affordable index funds and ETFs. (ETFs).
Risk Management and Robo-advisors
While most robo-advisors use modern theories of portfolios (sometimes together with other methods that have been thoroughly researched) to create portfolios for investors, they also employ different ways of expressing the levels of Risk associated with these portfolios. Many investment experts believe that Risk is just as crucial as a return in the selection of portfolios. The mean-variance optimization model still influences most of them, as demonstrated in Nobel Prize winner Harry Markowitz’s 1952 dissertation on the choice of portfolios.
However, Risk is generally not as widely understood by investors as the expected returns. This is because an individual’s risk tolerance can be determined not just by previous performance and expectations but also by individual situations and other aspects like fear and hope. Additionally, a person’s risk tolerance isn’t an indicator of static values. Most people will believe their risk tolerance is lower in 2020 due to the risks posed by COVID-19, unlike at any time in the last decade. The value of a portfolio is determined by the investor primarily based on their perception of the portfolio’s Risk. This is why a robot must demonstrate Risk so that investors understand the risks and connect it to their personal tolerance goals, as well as emotional preferences.
Robo-advisors employ qualitative or quantitative measures to aid clients in understanding the risks. All efforts have their pros and drawbacks.
Qualitative Risk Levels: Aggressive or High Growth?
Most robo-advisors assign a quantitative risk score based on how investors respond to a set of psychometric tests. The rating will typically range on a scale of numbers between “Very Conservative” and “Very Aggressive.”
A qualitative risk rating offers qualitative risk rating assessing the Risks of different portfolios about one another simply for investors. For instance, one portfolio with an “Aggressive” rating may be more risky than one “Conservative.” The psychometric questions can help investors narrow their risk tolerance and help determine the right risk level.
However, a qualitative score is not a reliable knowledge of the potential volatility the fund will exhibit. It is not always clear how volatile an aggressive portfolio will be compared to a more moderate portfolio. A risk score of 6 might not mean that a portfolio is twice as risky as one rated as 3. Additionally, risk perception can differ according to how it is communicated. The investors may perceive a risky portfolio differently, based on the “High Growth” classification or “Very Aggressive.” Therefore, categorizing a portfolio adds an element of subjectiveness to the perceived value of the portfolio.
My issue with robo-advisors who over-emphasize the Risk of qualitative quality is that it could create a false impression of security about the current portfolio improvement. An arbitrary risk score across an aggressive/conservative range may be too broad and ultimately result in suboptimal financing planning decisions by investors whose circumstances may be more complex than previously thought. Risks that are over-simplified are being raised in regulatory complaints concerning robo-advisors that engage in systemic fraudulent sales practices and a lack of understanding by investors to comprehend the real nature and the specifics of their product.
The increased adoption of quantitative risk management (used by funds, banks, and even family offices) and customer education could be essential in the next level of automated advising. This will propel the industry forward and coincide with national trends to increase financial literacy.
Taming Volatility: Value at Risk
Valuation at Risk, also known as VaR, is the most well-known indicator of the volatility in the portfolio. In simple terms, VaR measures the minimum losses expected to occur at a specific probability level (also called the confidence level, or at times, percentile). For instance, if the VaR of a portfolio’s 99% of 12% is the same, that implies that there’s the possibility that losses incurred by this portfolio won’t exceed over the period. Also, there is a 1 percent chance that the portfolio’s losses will exceed 12 percent. VaR is used by robo-advisors. An example comes from Singapore’s StRobo-advisors, who using VaR99% guidelines in a measurement called “the ” Risk Index.”
VaR can be calculated by various methods. The historical process for various methods can calculate VaRby magnitude. It also identifies the percentage of recovery measured at a specific rate (typically 95 percent or 99 percent). The variance-covariance approach is based on the assumption that returns are generally distributed, and it uses the standard deviation of the portfolio to determine where the worst five percent or one percent returns will be within the bell curve. VaR can also be calculated through simulation, which produces the lowest five or 1 percent returns based on probabilistic results.
