Investors' increasing interest in alternative investments is leading to a need for advanced analyses in the risk and portfolio context. Appropriate risk management systems provide support for this.
In the continued low interest rate environment, institutional investors are increasingly looking for worthwhile investments. Alternative investments, such as real estate, private equity, and, increasingly, infrastructure investments, have become well established. Private equity in particular has achieved strong growth, with global assets under management rising from less than USD 1 trillion to over USD 8 trillion between 2002 and 2020.
Source: Preqin, Credit Suisse
This increasing demand, along with recognizable inflation trends in the area of alternative investments, is leading to an increasing need for better risk and portfolio management systems. It is also explained by the fact that the current market standards are not yet sufficiently developed and are mainly based on manual and controlling-based approaches. In addition, limited standardized data streams prevent analysis over a long investment time horizon, which is useful for less liquid asset classes such as alternative investments. It is about big data, i.e. data volumes that are too large or complex for evaluation using conventional data processing methods.
The potential of big data is still underexploited in certain sectors, such as the real estate sector. The analysis of client data facilitates enhanced client service, for instance. Information about buying decisions and risk assessments can also be obtained at market level.
In addition, the demand for better risk management systems is likely to be supported by the increasing regulatory requirements regarding sound analyses for alternative investments. In this respect, the focus will increasingly shift to the interaction between different risk categories, such as market, liquidity, and reputational risks. Suitable tools can be used to simulate stress tests and thus analyze, for example, the impact of property purchases/sales on cash flows and the overall portfolio.
A simulation tool is a good way to supplement existing tools to carry out retrospective and forward-looking analyses of alternative investments. The integration of macroeconomic data in stochastic models enables analyses such as portfolio scenarios, stress tests, and risk/return and risk contribution analyses to be carried out.
These can be used in the area of real estate, for example, to make portfolio decisions that take into account appropriate models and current real estate and macroeconomic data.
Credit Suisse Asset Servicing is increasingly focusing on new technologies in its risk management activities. Integrating big data can be an effective support as it enhances the meaningfulness of models. In the real estate sector, the analysis of personal client data could thus help to offer tailor-made services. In addition, the use of artificial intelligence can be used to analyze dependencies of data points in depth and to take them into account in the models.
Comprehensive data evaluation can help investment managers decide which property belongs in which fund. In addition, technological innovations have the potential to support the integration of sustainability criteria into alternative portfolios and to simulate various factors. Credit Suisse Asset Servicing is therefore already using the technology in the area of ESG (environmental, social, and governance) criteria for a multi-factor analysis.
Patrik Marti is Head Fund Companies at Credit Suisse Asset Servicing. Credit Suisse Asset Servicing specializes in providing services to institutional clients such as pension funds, independent asset managers, other financial institutions and high net worth individuals.
