Over the years, there has been significant growth in the breadth and depth of analysis undertaken by performance and risk teams across our industry, as both fund managers and clients seek to better understand the returns generated in their portfolios. Performance, attribution analysis and ex-post risk statistics have all been increasingly used to assess the decision-making and risk-reward payoffs resulting from managers’ investment decisions. Along with this has been an increase in the use of ex-ante risk analysis utilizing factor models, stress testing, VAR and other measures to provide deeper insight into portfolio construction and potential investor outcomes.
The challenge is bringing these investment performance and risk analytics together to provide investment professionals with the full suite of performance and risk analysis at their fingertips.
Measuring investment performance and risk
Those of us who have been in the industry long enough will remember the early days of performance attribution, which was usually produced using monthly return calculations and aggregated up to either a simple asset class, country or sector-based view. It was also mostly for equity and traditional “balanced” portfolios—back then, fixed income managers had to largely fend for themselves if they wanted anything more detailed than a total portfolio return. A big leap forward was the introduction of daily security-level calculations, which opened up the possibilities for greater detail in the analysis available, and also brought with it the first security-level and fixed income attribution models, the latter of which quickly grew and expanded into the multiple and more complex methodologies we see today.
Ex-post risk measures, using historical returns, have been around for some time. Bill Sharpe came up with his famous ratio in 1966, while calculations of absolute and relative return volatility, regression analysis and correlation have long been presented alongside performance data to give greater insight into the risk-reward strategies of investment managers.
For ex-ante risk, fund managers will often use modeling and optimization of forecast measures as part of their portfolio construction process. Managers may specify certain parameters around factor exposures, concentration, liquidity, turnover, and both the total potential risk and how this is spread throughout their portfolio. Back-testing of potential portfolios that meet these criteria using techniques such as Monte Carlo simulations can also help a manager understand the risk profile by looking at, for example, expected tail loss and value at risk measures.
Over time, the need for independent measurement and analysis has led many managers to create dedicated risk teams, often separate to their performance counterparts. The breadth of analysis has grown to cover factor-based models, credit and liquidity analysis, calculating value at risk at different sensitivities, and stress testing both potential economic and market changes, as well as historical scenarios based on major events in the past.
Integrating investment performance and risk
One of the challenges has been to bring all of this information together—for example, are the predictions of our ex-ante risk models accurately reflecting the reality of the ex-post results? What can we learn from the differences between predicted and actual outcomes? Often, managers will have several systems producing different parts of this analysis, and there has been a reliance on spreadsheets and manually maintained databases to do the job of bringing all of this information together. Another challenge is the consistency of data inputs—are the risk and performance systems supplied with data from a common source, or is the former coming from the front office, while the latter is from the accounting platform or even a third-party administrator? Without consistent data inputs, it becomes more difficult to meaningfully compare ex-ante and ex-post results.
Here at SS&C, we have looked at how we might meet these challenges by combining the strengths of Sylvan’s industry-leading performance analysis software and Algorithmics’ state-of-the-art ex-ante risk analysis capabilities. Using dedicated APIs, we are able to feed source data from Sylvan into Algorithmics, then consume the calculated risk outputs, including credit analysis, VAR measures, stress testing, and so on, back into the performance system where we can present a single integrated view of a portfolio’s ex-ante and ex-post risk and performance.
Presenting this information in Sylvan’s customizable dashboards gives users a single endpoint where they can view, analyze and report on all of their risk and performance analyses. The APIs can be called in real-time, so any updates to the data in Sylvan can quickly and easily be fed to Algorithmics to ensure all analyses are kept in synchronization. While we can’t guarantee that the predicted investment outcomes from the risk models will be matched by the results actually achieved, we can hopefully help managers analyze and understand this more easily, knowing that consistency and integrity of data have been preserved across time.
Download our "Sylvan Risk Analytics" brochure to learn more about Sylvan’s connection with Algorithmics, and how it could enhance your portfolio performance and risk analysis.
Written by Ian Searle
Head of Performance & Attribution EMEA