Performance Attribution Software for Better Investment Decisions

Performance attribution software can be a game-changer for investors, helping them make better decisions and optimize their portfolios.

By using performance attribution software, investors can gain a deeper understanding of their investment strategy's strengths and weaknesses. This is made possible through the software's ability to analyze a portfolio's performance and break it down into its various components.

Investors can then use this information to identify areas of improvement and make data-driven decisions. For example, if a portfolio's performance is lagging due to a particular sector or asset class, the investor can adjust their strategy accordingly.

Performance attribution software can also help investors identify opportunities for cost savings and efficiency gains. By analyzing a portfolio's performance and identifying areas where costs can be reduced, investors can make more informed decisions and improve their overall returns.

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Calculating performance and attribution results without a professional performance measurement solution can be very time-consuming and error-prone.

PEARL automates the full performance measurement workflow, including management of high volumes of portfolio and benchmark data, configuration of a large range of calculation options and attribution models, and flexible reporting of the calculated results.

Analyzing performance for modern investment portfolios based on complex strategies can be a challenge, especially when dealing with multi-asset portfolios and overlay strategies.

PEARL is specifically well-suited to provide insight in these complex strategies, offering a wide range of flexible configurable attribution models.

Improving efficiency, quality, and speed of performance calculation and reporting processes is crucial for any investment manager.

PEARL offers the tools needed to optimize these processes, with intuitive and guiding user interfaces that help you operate the system effectively and efficiently.

Handling high volumes of portfolio and benchmark data can be a significant challenge, but PEARL's extensive data checking framework helps to identify data errors quickly and improve the quality of the calculated results.

PEARL can take ESG data into account and use it for an analysis of the added value of such strategy, showing the added value or cost of an exclusion policy by applying it to the portfolio benchmark and comparing the impact on return.

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Relative Attribution analysis helps identify strengths and weaknesses in a portfolio's performance by breaking it down into allocation, selection, and interaction effects. This detailed analysis aids in informed decision-making when reallocating assets or adjusting strategies to align with a portfolio's intended goals.

Users can leverage the "As-of-Date" functionality to perform point-in-time analyses with options for Daily, Month End, Quarter End, or a custom date selection. This feature provides a flexible way to analyze performance at any given time.

Risk-based performance attribution offers an alternative, more detailed analysis of risk exposures and their impact on returns. This factor-based performance attribution gives a comprehensive picture of what is driving risk and return drivers in a portfolio.

Factor performance attribution breaks down active risk and corresponding return from various factor blocks, further detailing style factor exposures, contribution to return, and contribution to risk. This helps paint a complete picture of a portfolio's performance.

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The Brinson-Fachler method is a popular attribution model that explains the portfolio performance vs benchmark using allocation, selection, and interaction effects. This model seeks to explain the drivers of excess return vs benchmark.

Here's a summary of the key factors in the Brinson-Fachler method:

Risk-based performance attribution offers an alternative, more detailed analysis of risk exposures and their impact on returns. It uses factor risk models to provide insights into active risk and active risk decomposition, factor active risk and return, and specific active risk and return.

This factor-based performance attribution gives you a comprehensive picture of what drives risk and return drivers in a portfolio. By using this method, you can gain a deeper understanding of your investments.

Our Performance Measurement and Attribution solution for asset owners, such as pension funds, insurance companies, and sovereign wealth funds, captures all asset classes and strategies by a wide range of attribution models. This helps you to explain sources of your excess return as well as the impact of investments on funding ratio or solvency levels.

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For asset managers, having a clear understanding of how their investment decisions are impacting their portfolios is crucial. YCharts' PEARL for Asset Managers helps with this by providing a range of attribution models that capture all asset classes and strategies.

This solution is particularly useful for large-scale investors, such as pension funds, insurance companies, and sovereign wealth funds, who need to explain the sources of their excess return. By using PEARL, these investors can gain valuable insights into the impact of their investments on funding ratio or solvency levels.

According to Michael Mitchell, Head of Portfolio Risk at New Zealand Superannuation, YCharts' PEARL has been instrumental in meeting the fund's performance reporting needs.

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Factor performance attribution offers a more comprehensive view of return and risk by breaking down active risk and corresponding return from various factor blocks. This allows for a detailed analysis of the impact of different factors on a portfolio's performance.

Using factor risk models, managers and investors can gain insights into active risk and active risk decomposition, factor active risk and return, and specific active risk and return. This level of detail is not possible with traditional performance attribution methods.

Risk-based performance attribution, in particular, provides a more thorough view of return and risk by accounting for the impact of industry exposures and style factor exposures. For example, a Value-focused ETF had a sizable risk exposure to the Value factor, which led to a positive return contribution of 1.11%.

The Value factor was the fourth-largest contributor to risk, whereas Size was in third place. This highlights the importance of considering multiple factors when evaluating a portfolio's performance.

Factor performance attribution can also help identify potential unwanted exposures, such as a negative exposure to Profit Quality, which detracted from the portfolio's return with a -2.08% contribution. By analyzing these exposures, investors can make more informed decisions about their portfolios.

A comprehensive analysis of a Value ETF's performance using factor performance attribution revealed that almost all the underperformance came from factors, with style factor exposures contributing substantially to the underperformance (-2.82%).

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Accurate performance attribution is crucial for making informed investment decisions. Our software provides transaction-based attribution, using reconciled transaction data in Limina to ensure accurate representation of inflows and outflows.

We include all fees and expenses that contribute to the Net Asset Value (NAV), giving you a complete picture of your portfolio's performance. No black box - inspect figures to see how they are calculated.

Our solution offers a range of attribution models to help you explain sources of excess return. This is particularly useful for pension funds, insurance companies, endowments, and sovereign wealth funds that need to capture all asset classes and strategies.

Here are some key benefits of our performance attribution software:

Our software has helped clients like New Zealand Superannuation, which needed to increase its performance reporting needs. Ortec Finance's innovative solution met these needs, providing stakeholders with a high level of transparency of its performance and portfolio holdings.

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Performance attribution software helps managers understand the sources of risk and return in their portfolios. This is especially important for portfolios that make factor bets, as seen in the Value ETF example.

A granular risk-based performance attribution approach can reveal what's really driving performance, rather than just looking at overall returns. This can be misleading, as systematic factor exposures can be mistaken for stock selection or alpha.

The Value ETF analysis shows that much of the risk was due to unintended bets, despite the main theme of Value performing as expected. This highlights the importance of considering the aggregate impact of individual industry allocations.

Axioma Portfolio Analytics can provide attribution for individual industry allocations, but here we focus on the overall effect.

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