Traditional Asset-Based Allocation Approach Faces Challenge From Multi-Factor Risk-Based Method Arising From Data Science And Analytics
When the ancients looked up at the night skies, they had everything figured out – everything but the wandering stars that didn’t rotate around the Earth in fixed patterns like the sun, moon and fixed stars. For much of human history, astronomers kept asking – how can we predict the path of these wandering stars around the Earth?
So long as they put the Earth at the center of the math, they never derived an answer. Similarly, advisors and wealth managers rely on asset allocation models that may range from the standard 60/40 to models that include a percentage in alternatives or add other complexities, but all start with an asset class-centric viewpoint.
Two Sigma, which applies data science and quantitative investing to financial markets in fields as varied as real estate, insurance and securities trading, established Venn by Two Sigma, an investment analytics platform that places risk as the central question rather than asset class. The firm provides a returns-based multi-factor risk analysis platform for wealth managers to support portfolio analysis, manager due diligence, scenario analysis, proposal generation and performance reporting.
To understand why the traditional asset allocation approach may fall short, how risk factor analysis works and the benefits it can provide to advisors and professionals in portfolio and investment decisions, we reached out to Venn’s CEO, Marco Della Torre.
WSR: You told us that asset allocation is moving from traditional asset classes to a risk-based approach. What is insufficient about the traditional approach?
Della Torre: We consistently heard from clients that existing risk tools failed to address the unique needs of allocators and managers of multi-asset portfolios. Part of this was due to the shortcomings of a traditional asset class approach to risk.
Put simply, allocating a portfolio across asset classes may provide only an illusion of diversification. For example, we have found that “diversified” portfolios can sometimes have their risk dominated by only a few risk factors, such as an equity factor. This may leave investors surprised when their portfolio performance is undifferentiated or leads to unexpected outcomes.
Risk factors, on the other hand, can be found across and within asset classes, which makes them powerful tools to measure the true drivers of risk, return and diversification across multi-asset portfolios. For example, examining a portfolio through a risk factor lens can help one identify where their equity risk is originating from, even if it is coming from their alternative or fixed income sleeves.
WSR: Describe how the risk factor-based approach works and what benefits it provides for clients’ portfolios.
Della Torre: A risk factor-based approach utilizes common systematic risk factors that can be found across and within asset classes. This allows investors to analyze multi-asset portfolios holistically, without needing to first separate out their equity, fixed income, alternative or other portfolio sleeves.
In practice, this can be done by looking at the relationship of investment returns with factors, using them as tools to explain exposure, risk and return of managers or portfolios over time. Think about factors such as equity, interest rates, foreign currency, quality or trend following, for example.
If you use only a small set of highly explainable factors, and construct each to be unique and independent, investment analysis can result in highly actionable allocation insights rather than analysis paralysis. Independent factors also help to decompose risk using distinct measures, limiting the illusion of diversification that might come from simply holding many different asset classes.
We built a platform to help capital allocators and advisors quickly ascertain which risks in the market are most important for their portfolio, as well as identify how sleeves, managers or individual holdings contribute to said risks. This includes understanding the unique risks that are not identifiable, which may be associated with idiosyncratic manager alpha. Ultimately, identifying true drivers of risk may lead to investment performance more in-line with expectations and more confidence when expressing views on managers or markets.
WSR: How are data science and analytics helping wealth managers with portfolio monitoring, construction and due diligence?
Della Torre: Institutional-level data science and analytics are becoming more accessible outside of institutional investors. Quantitative factor analysis is a great example of this, which can lead to more meaningful manager due diligence, portfolio analysis and client conversations than previously available. But how does one actually access this level of data science and analytics?
One way is through modern technology platforms such as ours. Technology has changed the game by not only democratizing institutional investment analysis, but also streamlining typical investment workflows. This allows a wealth manager or team to do more with less, as long as the technologies they use are intuitive and practical.
This includes making operations easier such as managing data, performance reporting or efficiently collaborating with members of an investment team, which may lead to an increased ability to demonstrate value to clients, more time to build a practice or the opportunity to provide higher quality services in areas such as estate planning and tax management.
Ultimately, this can lead to wealth managers leveling up their investment expertise and the services they offer, becoming even more valuable to a client’s financial plan.
Michael Madden, Contributing Editor & Research Analyst at Wealth Solutions Report, can be reached at mmadden@wealthsolutionsreport.com.
