Introduction

As institutional investors with increasingly complex pools of assets seek to manage performance and risk, along with liquidity, they are contending with a major challenge: the convergence of public and private assets in portfolios means asset managers and asset owners are facing unstandardized, decentralized data and less liquid assets.

To take on this difficulty, institutional investors can consider enhancing their data management capabilities. In order to measure performance and risk, in particular, they can bolster their front-office analytics capabilities. On the liquidity management front, asset managers and asset owners would do well to employ a tool that can forecast transaction costs given various liquidation scenarios. Third-party tools that utilize automation can help institutional investors be better positioned to make anticipatory, strategic trade decisions and meet their payment obligations to investors.

Risk-Averse Business

During times of volatility, institutional investors aim to build resilient, high-performing portfolios and meet or exceed benchmarks.

Traditional methods of measuring performance may require error-prone and time-consuming manual data entry and reconciliation. Institutional investor front offices are looking to evaluate a portfolio's performance against benchmarks and analyze data to make informed, proactive trading decisions. This means modernizing the approach to data analysis with a clear public-private look-through. Given that institutional investors are contending with portfolios of increased scale, volume, and complexity, institutional investors want access to actionable, normalized data. This means creating a dashboard that feeds portfolio managers granular analytics that might help them generate more proactive trade ideas and potentially boost their returns.

Beyond enhancing data management capabilities to assess risk and performance, institutional investors may want to improve their ability to evaluate the consequences of exiting positions.

Liquidity Management

Amid precipitous market drops, institutional investors may need to generate liquidity due to investor redemptions. That is increasingly of the essence given the increase in the proportion of portfolio assets in private markets, assets that are inherently less liquid. When exiting positions, though, institutional investors have to be mindful of the costs involved – whether selling off immediately or at a slow drip over a particular time horizon. When rebalancing, the investor gets charged, and in this risk measurement, the institutional investor must assess the transaction cost.

A swing pricing tool can help determine what the transaction costs will be for institutional investors, including tax burdens and administration fees, in addition to what the cost will be to end investors. This adds transparency and serves as an anti-dilution mechanism for institutional investors to ensure they can fulfil their obligations to their investors. Essentially, swing pricing facilitates the ability for Open-Ended Funds (OEFs) to toggle the NAV per share up or down depending on the volume of net inflows or outflows of securities. That passes the costs of trading onto the investors executing those trades, rather than to the existing shareholders, thus protecting the latter from diluting their holdings and fairly allocating the costs to investors driving the redemptions. Swing pricing can be a linchpin tool for beating benchmarks: if an asset manager lacks the ability to utilize swing pricing, there is an increased likelihood its funds could face higher levels of dilution, thus potentially lowering their long-term performance.¹

Decode, Demystify, Decipher

As institutional investors strive to seek alpha, build resilient portfolios, neutralize liabilities, and manage liquidity, enlisting a third-party may be helpful to build a more robust data management framework, especially during times of elevated volatility. J.P. Morgan can help asset owners and asset managers enhance their front-office analytics capabilities to measure performance and risk. In addition, J.P. Morgan is able to assist institutional investors in managing their liquidity through a transaction cost measurement tool.

In terms of performance and risk measurement, it is possible for institutional investors to generate valuable insights with powerful analytics by using Fusion by J.P. Morgan, a cloud-native data technology solution for institutional investors that provides end-to-end data management, analytics, and reporting across the investment lifecycle. The platform seamlessly integrates and combines data around positions and transactions from multiple sources – including J.P. Morgan Securities Services lines of business Custody, Fund Accounting and/or Middle Office, as well as external providers – into a single, normalized data model that delivers the benefits of scale and reduced costs, along with the ability to more easily unlock timely analysis and insights. Fusion Analytics, a module within the larger Fusion platform, integrates multiple providers to deliver powerful analytical capabilities in a managed solution featuring a full-service operating model that extends from data ingestion through analytics and reporting delivery.

Fusion integrates advanced calculations capabilities from FactSet (a premier performance measurement platform) and MSCI (a premier risk measurement platform), and J.P. Morgan itself to deliver a powerful analytics ecosystem, enabling institutional investors to produce impactful insights.

In a recent initiative, J.P. Morgan Securities Services has focused on developing a bespoke swing pricing solution in partnership with MSCI. The solution offers to support our clients’ liquidity risk workflow and manage transaction costs effectively under various market conditions. By utilizing position-level fund accounting data and MSCI’s advanced liquidity risk calculation engine, the service supports multiple asset class funds, including global equities, bonds, and derivatives, helping clients meet regulatory reporting requirements. Using MSCI’s liquidity models, the expected transaction cost is calculated for each position within the fund, based on order size and liquidation horizon. The aggregation of position level data into fund level transaction cost supports 55 swing pricing thresholds across multiple liquidation horizons.

As asset owners and asset managers seek to meet or beat benchmarks and achieve their various other goals, institutional investors might consider outsourcing data management capabilities to manage risk and performance on the one hand and liquidity on the other. Partners like J.P. Morgan can help institutional investors make strategic, proactive decisions that aim to enhance returns and mitigate risk while also running stress-tests.