In the field of investing, knowing about the source of your returns is as important as the return itself. It is important for investors and portfolio managers to understand the drivers behind the investment performance. It is important to understand if the gains & losses are because of smart asset allocation, favorable market conditions, or superior security selection. With the help of performance attribution analysis through reliable portfolio management services, investors can update their strategies and enhance decision-making.
Portfolio performance attribution refers to a technique used for analyzing and deconstructing the returns of a portfolio. This factor also helps in distinguishing between market-driven performance and active management decisions. It can help investors in the following ways:
- Understanding how well the strategy of a fund manager is performing
- Identifying whether excess returns (alpha) are generated due to security selection, allocation decisions, or some other factor
- Improving future investment decisions by looking into past performance
- Effectively capturing style drift, highlighting divergence from the portfolio mandate
An Overview of Different Approaches to Performance Attribution Analysis
The right approach to selecting the desired performance attribution analysis model will depend on the asset class combination. Each model is capable of capturing core performance drivers that are unique to the respective asset class.
With the help of the right portfolio management services, here are some common approaches used:
1. : Brinson Attribution
This serves to be a returns-based performance attribution analysis method, particularly for equities. It helps in dividing the returns into the following effects:
- Allocation Effect: Reveals how underweighting and overweighting specific sectors in comparison to the benchmark affects the overall portfolio performance.
- Interaction Effect: Reveals the combined effect of selection and allocation decisions.
- Selection Effect: Unfolds the extent to which the returns were affected by selecting the wrong or right securities within those sectors.
2. : Campisi Model Attribution
This model was developed by Stephen Campisi. It is a leading portfolio performance attribution technique that is suited for fixed-income portfolios. It divides returns into the following components:
- Income Return: This part reflects the coupon payments earned from holding the bonds. It creates the most predictable and stable part of fixed income returns.
- Price Return: This covers changes in bond prices because of market fluctuations. It is further categorized into the following effects:
- Spread Effect: This analyzes how fluctuations in credit spreads affect bond prices. When credit spreads increase due to increasing credit risk, the values of the bond reduce. This will create a negative spread return. On the other hand, tightening spreads will lead to capital gains.
- Treasury Effect: This highlights the impact of bond price changes or interest rate movements with the help of duration-matched Treasury benchmarks. For example, if Treasury rates increase, bond prices tend to fall. This results in a negative Treasury effect.
- Selection Effect: This looks into part of the price return coming from security-centric decisions like selecting underperforming or outperforming bonds related to their rating peers or sectors.
3. : Factor-based Attribution
It is a type of portfolio performance attribution that divides performance into core investment factors, including:
- Value: investing in undervalued stocks.
- Market beta: overall market exposure
- Size: small-cap and large-cap stocks
- Momentum: determining the wave of stocks that are performing well
- Growth: investing in companies with high earnings growth
What is Surprise Return in the Performance Attribution Model?
In certain attribution models, a surprise return component is included to gather the impact of deviation between model assumptions and actual outcomes. This return comes up when the actual parameters are distinct from the attribution model assumptions.
Using Quantitative Models for Attribution Studies
The current data-driven financial system has caused the emergence of advanced quantitative models. In turn, this transforms performance attribution from a simplified return philosophy into an in-depth analysis of what supports performance.
With the help of time series and econometric models, portfolio managers are able to understand performance with the help of identifiable resources, including sector trends, market movements, and security-centric factors. This helps in making informed decisions and effective alignment of portfolios with the right objectives.
Some examples of quantitative approaches are:
- AI and Machine Learning: AI-centric models help understand complex, non-linear patterns that are usually outside the domain of traditional models. With the effective capacity of computers to organize large amounts of data, portfolio managers have access to advanced machine learning models that use linear regression to collect useful information.
Moreover, there are sentiment analysis models to understand overall market sentiment. This helps analyze social media data, news articles, and earnings calls to understand investor sentiment.
- Multi-factor Models: These models help us understand the source of returns with improved granularity. These models divide portfolio returns into a wide range of sectoral, style-based, and economic factors like growth, value, volatility, and momentum.
This helps distinguish whether outperformance was because of effective security selection, smart asset allocation, or the right market conditions.
Performance Attribution with Respect to Diverse Market Conditions
As market fluctuations continue to take place, it becomes important to understand the source of returns. The knowledge of market and macroeconomic factors, in combination with a portfolio manager’s expertise in choosing stocks and delegating the right capital weightage, can result in major benefits.
In a typical bullish market, portfolio managers can consider choosing growth stocks while increasing ample speculation to gain the market. On the other hand, in case of a bearish market, they might choose diversification and choose stocks with strong fundamentals.
Conclusion
As the investment scenario continues to struggle with multiple changes, the importance of performance attribution analysis also grows. Financial experts and portfolio management service providers are becoming increasingly innovative in leading financial product development.
