April 10, 2019 | Written by: Rustom Barua
Categorized: Banking | FinTech | IBM RegTech Innovations
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Why alternative assets
Unless one has been in a Rip Van Winkle-style cryogenic state over the past few years, it would be hard not to notice the shift in asset allocations toward “alternative assets” for investment portfolios of buy side institutions. There are several reasons behind the tilt.
Pension and Insurance funds suffered from the pressure that comes of prolonged low interest rate environments to maintain high enough investment returns to meet the targets against their liabilities. Asset managers, in addition to seeking higher investment returns in the face of low interest rates, also contended with high volatility and unreliable market sentiments, and sought diversification away from traditional investments. Higher returns, low correlations to public markets, and better risk adjusted performance were some of the drivers toward a shift to alternatives.
And it has been a decisive move. Endowment funds, in particular, have led the way in investing in alternative assets and have reaped the benefits of consistently positive returns. As an example, Yale University’s endowment fund was allocating 20% of its investments to alternatives back in 1985, but by 2013 the allocation was closer to 80%.
What constitutes “alternative?”
Although we lack a gold standard for a definition, an alternative asset can loosely be thought of as an investment in anything that is not a traditional publicly traded stock, bond, or cash instrument. In principle this excludes very little, from art collections, aged whiskey to financial derivatives! More conventionally, when discussing the topic, the focus is on types of investments that institutions make for large portfolios. These are typically:
- Real assets: These include both tangible assets such as Real Estate, Infrastructure, Commodities, farmland, forestry, as well as intangible assets such as intellectual property and copyrights.
- Private equity/debt: These are investments in equity and debt of firms that are not publicly traded. This category covers distressed debt, venture capital, and leveraged buyouts.
- Hedge funds: A fund that uses leverage and short selling to engage in a variety of investment strategies on publicly available assets such as stocks, bonds, derivatives, commodities, and foreign exchange.
- Structured products: A financially engineered product designed to achieve specific exposure to an underlying asset. A popular example is a Collateralized Debt Obligation (CDO).
Access to these assets can take various forms, including direct investments, through funds, by partnership interests, or through derivatives (particularly for commodity/energy assets).
The challenges in measuring risk of alternatives
Alternative assets are characterized by high heterogeneity, low liquidity, and opacity. As seen from the list above, the categories of investments are quite broad and, within a given category such as those involving Real Assets, there are large differences in the risks between specific investments, e.g. a shipping port in Australia compared to a toll road in Manitoba. Unlike traditional investments, the time horizon for an investment is longer to realize the expected returns. Alternatives are often not tradable assets and it can be much longer to exit a position.
Each alternative asset class presents varying degrees of valuation challenges depending on how they rank for the above 3 characterizations. For example, a real estate exposure of a private REIT can be valued using similar techniques as for comparable public assets. Risk systems often easily extend a factor model to analyze such an instrument. In contrast, an Infrastructure investment such as a sewer company for a city, relies on subjective valuation through appraisals that require analysis of net assets, comparative sales, discounted expected cashflows and so on. This appraisal approach introduces concerns around the valuation including reliance on potentially outdated or old data (lagging), lack of data in the first place, not incorporating changes in macro conditions that can impact the discount rate, and gaming of the output data by people involved in the appraisal process. The result is potential for understating the risk and correlation due to overly smoothed returns.
“’Alternative assets’ are characterized by high heterogeneity, low liquidity, and opacity”
Buy-side firms are required to look at top-of-the-house valuation as well as risk of investments. This means understanding how the alternative portion of the portfolio could stand to lose money under various economic scenarios, both within an asset class as well as across asset classes. This is a significant challenge calling for employing multiple valuation model types, creating economic scenarios that reflect a correlated view of the underlying risk factors across a longer horizon, and a risk platform that can coherently combine a scenario-based framework across traditional and alternative assets. For measuring risk, the industry appears to move past crude factor model approaches initially used across all alternative positions.
Where there are analytical challenges, data challenges follow. In the alternative space, these include not seeing the underlying assets of a structured product, opaque return data from hedge funds (or funds of funds), poor availability of historical data, and simply lack of any comparable investment to proxy appropriately.
The picture ahead
The current expectation is that the alternative investment industry will grow by some estimates 59% by 2023, reaching USD 14 trillion in assets. The primary areas of growth are expected to be in real assets such as infrastructure and real estate.
As the competition for alternative investments increase, technology will continue to be leveraged to squeeze returns. We are seeing the next generation set of tools such as machine learning being deployed for those investment areas where publicly available datasets can be used. Newly established funds are attempting to improve valuations of structured products and real estate instruments by creating machine learning models with thousands of variables of borrower behavior in cities and neighborhoods. Another example is using local data such as traffic and weather for modeling expected cashflows in say an investment in a shopping mall.
Large financial institutions are looking for the right tools and expertise as they ramp up their alternatives allocations. The ideal path is to incrementally tackle the newer asset class by leveraging a platform that is flexible enough to handle public assets as well as the evolving best practice analytics around valuation and risk computation of alternative assets. Other than platforms like IBM Cloud-based financial and risk services, not many solution providers are currently in a position to meet buy-side firms’ ongoing requirements. It’s a tall order to be able to provide full market data and simulation models for traditional assets as well as best of breed alternative asset risk models from a chosen consultant or specialist firm, combined with next-gen data such as weather.
The race is on for both the investment side to capture shrinking alternative investment opportunities as well as on provider side to deliver the needed models, data, and platform.