With the implementation of MiFID II in Europe and similar standards in other jurisdictions, wealth advice has effectively shifted from a “single-instrument approach” to a “risk-based portfolio approach”. This blogpost is an introduction to what this means.
The arrival of MiFID II in Europe triggered some substantial changes in wealth advisory. Wealth advice now shifts from a “single-instrument approach” to a “risk-based portfolio approach” in order to be regulatory compliant. This paradigm shift leads to the usage of quantitative risk analysis and investment proposals on portfolio level.
Client risk profiling = categorizing
As part of the client on-boarding process, a risk profile needs to be explained to the client. Client risk profiling means categorizing. Most investors are categorized in groups which reflect risk tolerance: conservative, moderate and aggressive, with in-between categories such as moderately aggressive. In fully automated portfolio construction, an algorithm then determines the type of model portfolio and asset allocation that is suitable for a client’s risk profile and generates investment proposals that match.
Asset allocation helps balance portfolio risks and returns. Each asset within a portfolio carries its own risk, and while high-risk investments may have a higher potential for returns, they also have higher potential for losses. In other words, higher returns typically mean higher risk. In an investment portfolio, a variety of assets are combined to reduce overall risk. Diversification strategies help balance risks.
Risk diversification
Risk diversification draws from mathematical models based on modern portfolio theory. This quantitative approach towards risk and return is considered the best risk management method because it describes sets of risk factors for each asset. It involves setting target allocations for asset classes and automatic rebalancing of the original allocations if any significant deviations are identified due to differing returns from asset classes. A risk-based approach also involves mapping instruments to risk factors on a regular basis, resulting in continuous monitoring.
Despite relying on fully automated processes, a risk-factor-based approach allows some flexibility. Some solutions allow utilizing qualitative research capacities to define the asset shelf. Others allow choice in use of strategic asset allocation or model portfolios. Once constructed by the algorithm, an investment proposal can be changed, e.g. by considering client constraints. A model portfolio can also form the basis of a custom portfolio. However, custom portfolios are usually associated with a larger amount of attention, research and experience. Investors and their advisors are more actively involved in decisions. With machine-generated portfolios, on the other hand, investors completely rely on the investment allocations and recommendations they receive based on a profile.
Control through customization
However, automated investment advice does not mean a complete loss of control. Many tools now offer further customization options, for example allow processing of both bank and client constraints or preferences. These include definitions of a blacklist to avoid cluster risk. If a client expresses the wish to exclude assets from certain industries, this can be considered by the investment proposal as a restriction. Taking clients’ preferences as well as interests into consideration helps them see a direct relation between their preferences and the investment proposal.
Furthermore, these solutions have the ability to rigorously adhere to business rule management. For example, an algorithm can be taught to validate cross-border or increase tax efficiency, thus maximizing returns. An automatic reporting process can be in place for all of these ‘man-made’ investment decisions as well as daily post-trade risk monitoring and alerting for breaches, thus also abiding to MiFID II rules.
