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Fixed Income Strategies: Equipping Institutional Investors with Tools to Evaluate in Difficult Markets

March 2012

Table of Contents

Client Conversations: Insight on Evaluating Fixed Income

Results and Feedback

Case Studies


The unrelenting volatility and uncertainty in the economy and markets coupled with interest rates at their lowest levels leaves little chance of steady interest rate decreases that have fueled bond performance over the last twenty years. This environment is problematic for asset owners and managers who maintain dedicated fixed income allocations. As a result, there is an increasing need to diversify interest rate risk without increasing the volatility of a portfolio or the total fund. In addition, investment scrutiny and analysis has become crucial to review fixed income managers and their holdings for risk of issuer default, issuer credit risk and price volatility risk.

The goal of this paper is to identify best practices in which asset owners and managers use performance and risk analytics tools to evaluate fixed income strategies. To help determine these best practices we conducted phone conversations with a subset of our client base that have proven experience and exposure to fixed income.

Client Conversations: Insight on Evaluating Fixed Income

BNY Mellon's Performance & Risk Analytics consultants conducted conversations with targeted clients during the first half of 2011. Twenty–six clients were asked a series of questions about how they evaluate their fixed income managers and investments. Their responses provided insight into their fixed income strategy. More specifically, they discussed their application of quantitative and qualitative characteristics, use of specific reporting tools, employment of derivatives and use of portfolio stress testing. Twenty of the firms interviewed were asset owners and six were asset managers. Of the firms, nineteen had external fixed income managers, four managed their assets in–house and three had a blend of internal and external management.

The following questions were asked during the conversations:

  1. What particular quantitative metrics, characteristics or analytics do you use when evaluating managers? Why?
  2. What quantitative peer group measures do you use for evaluation? How do you like to slice your peer groups when comparing information?
  3. When you evaluate a manager, do you consider the impact to the rest of your Fixed Income portfolio? What factors do you consider? Why?
  4. What is your overall fixed income strategy (immunization, maximum return or risk)?
  5. What qualitative measures do you use for evaluation? What insight can you share?
  6. What types of derivatives are held in your portfolios? How do you analyze them?
  7. What types of risk or stress testing of portfolios do you conduct? What insight can you share?

Results and Feedback

As illustrated in Figure 1, benchmark relative comparison was the primary way our clients evaluate their fixed income strategy. This includes performance returns and holding characteristics relative to an appropriate benchmark. Peer group comparison was also a significant evaluation exercise as clients want to know how their manager compares with relevant peers. In addition, participants stressed the importance of fixed income's overall contribution to asset allocation from a risk and return perspective. This includes correlation with other asset classes as well as its return and risk contribution to the entire fund. Since asset owners made up the majority of the sample, it is important to note that asset managers rely more heavily on knowing how their portfolio compared with peers. Not surprisingly and outlined in Figure 2, from a quantitative characteristic perspective, duration evaluation is the chief characteristic used. Duration relative to the benchmark is a key measure of interest rate sensitivity and its impact on the value of the portfolio. Understanding sector allocation and rotation among fixed income sectors is also a key characteristic used in evaluation.

Most notably, for asset owners, Information Ratio and Sharpe Ratio were named as the most popular risk/return measures to assess their portfolios in comparison to the market and appropriate peer group. Financial Institutions pointed to evaluation of credit exposure and yield–to–maturity as their preferred risk/return measures. Credit quality was also identified as an important characteristic, weighing more heavily for internally managed portfolios, as opposed to those who oversee externally managed portfolios.

Post the 2008 financial crisis, those who we spoke to are suspect of the quality of the rating agencies information and tend to use those ratings in conjunction with their own internal credit exposure analysis. For example, one asset owner changed their investment guidelines, placing less emphasis on the ratings from the agencies and more on the credit exposure analysis of their manager's research.

Among the qualitative aspects used in fixed income evaluation, investment manager team track record, organizational continuity and organizational structure were deemed as leading considerations. This is a logical response since most respondents leaned toward a manager with tenure, experience and a cohesive organizational structure. Also central to quantitative analysis is to understand the construction of the portfolio — top-down or bottom–up. Respondents look for diversification in how managers construct portfolios — their process to construct a portfolio in addition to diversification and correlation differences in returns over time. Lastly, in an effort to spread fiduciary responsibility, asset owners also rely on their consultant's perspective of their managers.

The most commonly used derivatives identified include interest rate swaps, credit default swaps and futures. Use of these instruments tends to be for the purpose of increasing or decreasing duration, inexpensively and easily managing exposure to a sector or spreading risk throughout the portfolio.

Conversations also revealed that stress testing on a portfolio is important. Scenario analysis, volatility stress testing and correlations on current holdings are near the top of the list for all asset owners and externally managed portfolios. Financial institution responses included scenario analysis, volatility and credit risk analysis.

Case Studies

The following case studies illustrate how BNY Mellon's products and services can help in the analysis of fixed income portfolios. We analyzed the following four fixed income scenarios to provide a number of relatable perspectives:

  1. Asset Owner: Externally managed U.S. Active Core strategy
  2. Asset Owner: Externally managed U.S. Credit strategy
  3. Asset Owner: Externally managed Global Strategy
  4. Financial institution: Internally managed U.S. Government/Credit strategy

Case Study #1
Asset Owner: Externally Managed U.S. Active Core Fixed Income Strategy

Our first case study evaluates a sample of three asset owner, externally managed U.S. Active Core Fixed Income Managers, over a three year period. All three managers are benchmarked against the Barclays Aggregate Bond Index and make up the Total US Bonds Active Composite as a part of this study.

A popular method of assessing managers is to evaluate excess returns and compare returns to appropriate peers. Figure 3 is a quartile chart from BNY Mellon's charting tool that graphically depicts managers against a backdrop of peers and compares them using a variety of measures. Annualized alpha during the three–year period is highest for Manager C, with Manager A producing superior returns in recent periods. Peer comparison data also provides evidence of superior returns over a three–year period. Manager C is in the top quartile or 22nd percentile. However, Manager C's one–year ranking falls to the 61st percentile with quarter ending remaining at the 61st percentile.

Another common way to further evaluate performance is to compare relevant characteristics. Within Figure 4, the chart depicts the Sharpe Ratio of a portfolio's returns. Sharpe Ratio measures the excess return per unit of volatility typically represented by standard deviation.

Within Figure 5, Option Adjust Duration (OAD) over the same time period confirms the same outcome as the Sharpe Ratio. Manager C's OAD over three years has consistently been highest, a sign it has the largest interest rate and market risk. Greater risk of the portfolio is further validated by correlation analysis found in Figure 6. Manager C's returns had the highest correlations to the Total US Bonds Active Composite, which is comprised of Managers A, B and C. This leads us to believe that Manager C produced less diversification effect than the other two managers, while possibly increasing standard deviation and risk.

In order to test this assumption and to better understand the interaction of these managers, scenario analysis was done by viewing the Active Core Managers without Manager C (Figure 7). This was accomplished using Composite Builder within Charts. The actual composite with the three managers is named Total US Bonds Active while history of core active bond portfolio without Manager C is named Pro Forma Composite Active.

The Active Core composite without Manager C confirms there is little or no diversification effect by Manager C. Total US Bonds Active and Pro Forma Composite Active both have correlations with the Total Fund of 0.37. In addition, Pro Forma Composite Active without Manager C had consistently higher Sharpe Ratios over time than the actual Total US Bonds Active (Figure 8). Therefore, it would not be unreasonable to assume the Total Fund risk adjusted returns based on standard deviation would have been higher without Manager C.

Beyond return level analysis, holdings and structural assessment regarding Manager C's out–performance can be seen using BNY Mellon's Profiles and Fixed Income Manager–at– a–Glance reports. The main driver of returns for most fixed income portfolios is the term structure of the yield curve and, therefore, Contribution to OAD is highlighted. Manager C's primary strategy, especially during the dramatic market changes in November of 2008, was the yield curve. A bullet strategy around the 3–5 year part of the yield curve seems to have been the main contributor of excess returns as shown in Figure 9. Contribution to OAD was 2.1 of the total 5.02. Therefore, 42% of the OAD was concentrated around one part of the term structure while others had a more laddered approach. In addition, relative to the benchmark, the manager was underweight the 1–3 year portion of the yield curve.

Other sources of returns were derived from sector allocation (Figure 10). Compared to the benchmark and the other managers, Manager C was overweight in Corporates and Securitized securities. Specifically, Industrials, Financials and Securitized Instruments were significantly overweight relative to the benchmark in November 2008.

In Figure 11, lower quality rating bets were another source of returns. While the benchmark had 27% or 1.1 OAD in securities rated Aa1–Aa3 or lower, the percentage in those same quality ratings for Manager C was 34% or 1.73 OAD.

Lastly, in November 2008, Manager C's bullet strategy on the 3–5 year portion of the yield curve, sector allocation and quality rating bets contributed to a higher return over a three year period. However, the cost of superior excess returns was higher volatility as measured by standard deviation and greater interest rate and market risk.

In Figure 12, the OAD for Manager C is overweight the 3–5 year and underweight the 1–3 year portion of the yield curve. The Maturity Breakdown in comparison to OAD highlights the impact of embedded options on the portfolio. Furthermore, overweight Aa1–Aa3 or lower and Financial and Industrials could quickly be grasped by reviewing Figure 12.

Is the past success of Manager C sustainable? The future is difficult to predict; yet, by viewing the placement of current holdings using the Profile report (Figure 13), it may be possible to glean potential future results. In July 2011, Manager C's OAD is very similar to the benchmark, 4.66 for Manager C compared to 4.62. The main difference is a bet with 5 securities, 21% of the OAD, in the 15–20 year portion of the yield curve. However, compared with the past, Manager C's returns should be more similar with the benchmark. It appears that in July of 2011, Manager A may have wider– term structure variances because of an over allocation to the 5–7 and 7–10 year portions of the yield curve.

Figure 14 illustrates that there do not seem to be any significant sector allocation bets for Manager C in July 2011 while Manager B has significant bets in Corporates. Over 20% of Manager B's OAD are allocated to Financials and nearly 37% to Industrials as well as over 25% to Mortgage Backed Securities (MBS) Passthrough. This allocation effect is further magnified because Manager B is significantly underweight in Treasuries and Sovereign Debt. Thus, it could be assumed that much of Manager B's future returns will be determined by Corporates and MBS.

Manager B's variances in quality allocation compared to the benchmark and other managers are notable as well in Figure 15. Barclay's Capital US Aggregate Bonds have 34% (addition of Contribution in OAD within categories Aa1–Aa3 to Other divided by Total Contribution to OAD) of the OAD in securities with Aa1–Aa3 or lower quality ratings while Manager B's weighting is 73% of the OAD. It is interesting to note that Manager A has the opposite strategy of overweighting quality issues. Manager A's OAD Aa1–Aa3 or lower quality rating is only 25%.

In conclusion, Manager C's returns could be expected to be more in line with the benchmark going forward compared to the prior three years, while Manager B seems more likely to differ. Manager A has a larger allocation to the 5–10 year portion of the yield curve compared to the benchmark, while Manager C placed bets in key sectors and quality ratings. Greater variances from the benchmark and volatility could be expected from these two managers. The summary view can be seen in Figure 16. This shows that the main driver of fixed income performance, OAD, is much closer to the benchmark than in November of 2008. Visually, the low variance with the benchmark can be seen in Maturity Breakdown and the higher variance can be seen in Credit Exposure. The relevant Fixed Income Characteristics are listed below for a quick comparison.

Case Study #2
Asset Owner: Externally Managed Corporate Fixed Income Strategy

In our second case study, an asset owner's externally managed U.S. Credit (Corporate) portfolio was constructed in late 2009. We will compare how it looks to its benchmark early in its construction and then a year and half later.

In Figure 17, while Quality Ratings are similar to the benchmark, the weights among particular ratings vary. Certainly, the manager is building a high quality portfolio and does not appear to be sacrificing quality for yield. Option Adjusted Duration (OAD) is lower by comparison. This means the portfolio has slightly less sensitivity to interest rate changes, a signal that the manager is taking a more defensive posture in respect to interest rates. The manager is underweight in the 1–3 year maturity portion of the yield curve. This may mean shorter–term securities are not providing enough yield for the long–term view of the portfolio. The manager is overweight in 3–5 year maturities and 7–10 year maturities, indicating the manager anticipates an initial increase in interest rates, but expects a decrease in rates in the long– term as maturities move out. By overweighting maturities on this portion of the yield curve, the manager is looking to enhance portfolio yield.

With the benefit of hindsight, we can select a few characteristics in 2011 to determine how the portfolio responded to changes in the bond market when compared to its respective benchmark. We will review the change in Option Adjusted Spreads (OAS), a measure of credit and liquidity risk. Within Figure 18, at the onset of the portfolio, the OAS was comparable to the benchmark. Over the next few months, it dropped significantly when compared to the benchmark. Typically, when the OAS is lower than that of comparable securities, this signals that securities could be overpriced. What appears to be happening in the portfolio is that the manager is moving into particular sectors and making bets on the higher–quality instruments within those particular sectors.

Some managers attempt to add value to their investment proposition by rotating into sectors that are deemed to have value in the marketplace. The manager will seek to add return through a "sector rotation" style. The strategy would be to look at particular sectors such as governments, corporates, asset–backed securities and mortgage securities and move in and out of each as the market dictates. The comparative valuation between sectors is an important consideration. The goal is to add sectors that offer good absolute and relative value with consideration given to the sector's performance outlook. In this case, the sector concentration for Manager D, in Figure 19, is with the financials and industrials. The manager appears to be making bets on companies and industries that will succeed, picking higher quality within those sectors.

As seen in Figure 20, from a performance perspective, the manager has outperformed the benchmark for all time periods in excess of a month. Average quality continues to be closely aligned with the benchmark. Option Adjusted Duration (OAD) is in line with the benchmark. Current yield is slightly higher than the benchmark, but the Effective Yield is lower. This may signal the manager is purchasing securities at higher prices with lower actual yields. In addition, the OAS is much lower compared to December 2009, meaning the securities are potentially overpriced. One way of validating these assumptions would be to review the portfolio's turnover rate. The portfolio's turnover was 57.2% for 8/31/2011 compared to 9.9 % for the comparable BC Credit Index. Clearly there was greater trading volume within the portfolio when compared to the benchmark. This would lead us to the reasonable assumption of higher trading costs and greater tactical risk for the portfolio.

We can also analyze performance results through multi–factor attribution analysis. Using multi–factor Global Credit Performance Attribution Analysis, we can decompose the portfolio and index returns, attributing them to several factors including yield, duration (i.e. parallel shifts in the yield curve) term structure (i.e. changes to the slope and shape of the yield curve), sector, quality and other spread such as pre–payment spreads and volatility spread.

In Figure 21, it is clear the higher yield of the portfolio, 2.60 vs. 2.47 for the benchmark, had a positive effect on this portfolio's 6–month return compared to the benchmark. Yield curve changes also buoyed the account's performance. This is exhibited by a combination of the duration and term structure effects, which led to a net positive management effect of +10 basis points (adding together the +0.01 and +0.09 from the upper right portion of the table).

On the downside, management's decision to underweight bonds issued by supranationals, as well as utility and telecom companies, hindered benchmark relative performance. Note the -4 basis point management effect attributed to the Sector factor.

Viewing the Sector Attribution Detail in Figure 22 provides a closer look at the allocation effects of each individual sector.

A relative underweight allocation towards issues rated investment grade and below during a period of narrowing credit spreads was also to the portfolio's detriment, as per the –5 basis point management effect attributed to the Quality factor found in Figure 23.

Referring back to Figure 21, we can see that how the portfolio was positioned against the index on a factor by factor basis did not produce significant tracking error in the aggregate (+3.06 model return for the portfolio, +3.07 model return for the benchmark). However, +36 basis points of tracking error can be attributed to security selection. Fortunately, this analysis allows us to examine each security's effect on performance. This detail is illustrated in Figure 24. Examining each security's contribution to management effect, we find a single issue comprising +4 of the aforementioned +36 basis points of selection effect. Given the bond's indicative data, the multi–factor model calculated an expected return of 34 basis points for this security. Fortunately for this manager, the bond produced an impressive buy–hold return of +6.65 over 6 months. Therefore, the analysis validates and further explains the assumptions that superior returns have been generated by superior security selection. Higher frequency of trading to acquire superior securities appears to have paid off for the periods analyzed.

Case Study #3
Asset Owner: External Managers with a Global Fixed Income Strategy

Our third case study focuses on two asset owner externally managed portfolios with a global mandate benchmarked against the Barclay's Capital Global Aggregate Index. The analysis is for a three year period through July 2011. With the current uncertainty in the European debt markets, it is important to understand how to evaluate sovereign and corporate debt in countries outside the United States and pinpoint risks. Within Figure 25, over three years, it can be seen that both managers are roughly comparable in returns, outperforming the benchmark by just over 2%. However, Manager E has been the better performer over the last year, consistently achieving a positive alpha, while Manager F has underperformed, especially over the last three months.

These returns are compared to peers in Figure 26. Of twenty-two managers in the peer group, it can be seen that Manager E has been in the top quartile or better over the last year and is ranked number one in the last month, while Manager F has been in the bottom quartile over the last quarter and year. Both managers are in the 2nd quartile over three years. It is interesting to see that the spread of peer returns ranged from 3% to 13.3% over the one-year period, which shows the wide dispersion of returns achieved over a volatile year. The median of the peer group has also consistently outperformed the benchmark.

We can see a more detailed picture of how these returns were achieved and which manager is taking more risk by reviewing ex-post analysis. Within Figure 27, it can be seen that standard deviation of the managers' excess returns has reduced greatly over the last two years. Manager F (Figure 28) shows a slightly lower standard deviation over the past year, meaning they have had more consistency in their absolute returns. Tracking error or volatility of excess returns has also reduced over the period. Manager E is significantly lower. This can be useful to view for passive managers to ensure they are close to the benchmark; but here, it can be seen that both managers have an active mandate and have an element of volatility in their excess returns. Information ratios can be viewed to see a risk adjusted return defined as expected active return divided by tracking error. It can also be seen that Manager E has constantly been above Manager F. As a similar risk-adjusted statistic, Sharpe ratios for Manager E and F are comparable over the last few months.

Figure 29 identifies Manager E's underweight to Governments and their allocations to futures and swaps, while Manager F as shown in Figure 30 is almost 80% exposed to Governments with minimal derivative exposure. At a currency exposure level, Manager E has 56% exposure to Euro and 42% to USD, where Manager F has 62% exposure to USD and no exposure to the Euro. Additionally, Manager F exhibits a longer duration than Manager E, indicating more exposure to interest rate risk.

Further cause for the differences in the diversity of investments can be seen by monitoring investment guidelines applied to each of the managers. Within Figure 31 (an example of our Compliance Monitoring reporting), Manager F operates with the rules that less than 15% of the portfolio can be invested in emerging markets, less than 3% in Corporates or Municipals, and less than 15% in below investment grade non-sovereign debt. Manager E as shown within Figure 32, however, is only limited by the mandate that bonds must be rated investment grade Baa3 (Moody's) or BBB- (S&P). The results of these tests can be viewed to confirm managers are adhering to their mandates and not taking any unauthorized exposure or risks.

Figure 33 represents a Fixed Income Profile report that incorporates the Merrill Lynch Global sectors. Here, the fundamental differences in investment styles can be easily seen. Manager E holds 750 different positions, including a large number of securitized positions, such as MBS and Collateral Mortgage Obligations (CMOs), where Manager F holds only 86 positions, with minimal securitized positions. Manager E also has a large number of CDS and Swaps, with futures particularly contributing to the Option Adjusted Duration (OAD).

In summary, Manager F manages a more traditional mandate concentrated on long dated government bonds, while Manager E invests in lower–rated corporate bonds, securitized instruments and more derivative exposure. Manager E's portfolio structure was successful over the last year, yielding higher returns, for a similar amount of absolute risk.

Case Study #4
Financial Institution: Intermediate Fixed Income Strategy

In our final case study, a financial institution's Intermediate Fixed Income Portfolio benchmarked against the U.S. Government/Credit index is evaluated. Manager G uses the Barclay's Capital Intermediate Gov/Credit index for their intermediate fixed income strategy instead of the Barclay's Capital US Aggregate Bond index to further differentiate themselves. In Figure 34, it is easy to see this portfolio is not a "closet indexer." Rather, the portfolio has taken some decidedly different bets than the benchmark: much less weighted to Treasuries 12% vs. 54% , overweight to Corporates 45% vs. 13% and better yielding securities 4.18% vs. 3.21% , than the index. Also, the strategy has a higher weighting to short duration securities than the index, 9.7% vs. 1% . Looking at the performance of the portfolio, it appears these bets have not paid off in the short term, underperforming by 18 basis points, but longer-term this strategy has paid off handsomely, outperforming the benchmark by 45 basis points over a five year period.

Although outperforming the benchmark over longer periods, within Figure 35, this portfolio is right at or below the median manager return in the Russell Intermediate Bond Fixed Income Portfolio universe. A good portion of this peer-relative underperformance can be attributed to the decision to shorten the portfolio's duration.

Looking at the trend of Manager G's duration, in Figure 36, it is easy to see that just recently the portfolio has taken a much shorter-term strategy after several quarters of increasing duration. Given the uncertainty in the current interest rate environment, it is not surprising this portfolio shortened its duration, and it would be expected the duration would lengthen as rates go back to their normal trading range. Should that occur, this portfolio is well positioned.

Within Figure 37, this Fixed Income Attribution report is a great way to better understand how various portfolio bets impacted the quarterly performance of the portfolio. It is easy to see the three drivers of Manager G's underperformance relative to the Barclay's Government/Credit Index. The decision to shorten the portfolio's duration cost 3 basis points, but the bigger contributors to the underperformance were the decisions to allocate differently between sector and quality, costing a total of 32, 20 and 12 basis points, respectively. Helping offset these three decisions the portfolio gained 11 basis points by focusing on higher-yielding Corporates and Agencies. In aggregate all of these factors contributed to Manager G underperforming its benchmark by 23 basis points for the quarter.

Within Figure 38, further analysis into the portfolio sector bets reveals the biggest driver of the 20 basis points of underperformance was to overweight the Finance and Bank sectors, detracting 10 and 8 basis points, respectively.

Examining credit quality compared to the benchmark, the decision to focus less on Treasuries and more on Corporates cost Manager G 12 basis points, as illustrated in Figure 39.

Even though Manager G's strategy is currently out of favor and has not performed well relative to its benchmark and peers, this portfolio is consistent on bets in Sector and Quality as shown in Figure 40. Duration allocations over the past five quarters have decreased due to a decrease in duration exposure to Treasuries & Sovereign.


Our Findings

The goal of this paper was to identify best practices that both asset owners and managers utilize to evaluate fixed income portfolios and to illustrate examples of how BNY Mellon's tools help to evaluate these approaches. Through a series of client conversations we noted the metrics that are most valued when reviewing and evaluating fixed income instruments. These conversations revealed several key fixed income strategies, primary quantitative characteristics used, qualitative aspects reviewed, and the use of derivatives within portfolios. The four case studies helped illustrate how these kinds of comparisons can aid in the analysis of fixed income portfolios as they relate independently or as part of a larger portfolio. Moreover, the case studies illustrated how fixed income characteristics are applied through analysis and how to compare and evaluate these results through various reports and applications. The case studies were selected in order to address a number of key points raised during our client conversations:

  • Benchmark relative comparison was the primary way our clients evaluate their fixed income strategy. This includes performance returns and holding characteristics relative to an appropriate benchmark. Fixed Income Profiles and Manager at a Glance were used to address these points. They are constructed in a manner to help asset owners and managers understand key points of a strategy. For example, in Case #1 it was concluded that Manager C's returns would be more in line with the benchmark going forward than compared to its prior three years, based on its current structure shown in the Fixed Income Profile.
  • Peer group comparison was also a significant evaluation exercise. Clients desire to know how their manager compares with relevant peers. In addition, participants stressed the importance of fixed income's overall contribution to asset allocation from a risk and return perspective. This includes correlation with other asset classes as well as its return and risk contribution to the entire fund. The Charts tool with universes and risk characteristics were used to meet these client needs. Case #4 provides a solid example of using Charts beyond performance returns. Manager G's duration was evaluated in a Trend chart. It was recently shortened after several quarters of increasing duration.
  • Duration evaluation is also a chief characteristic used by clients. Duration relative to the benchmark is a key measure of interest rate sensitivity and its impact on the value of the portfolio. In Case #2, Fixed Income Multi-factor Attribution decomposed the portfolio and index returns, attributing them to several factors including yield, duration, parallel shifts in the yield curve and term structure. Yield curve changes buoyed the account's performance. The reports pinpointed the combination of duration and term structure effects, which resulted in a net positive management effect of +10 basis points of the portfolio against the benchmark.
  • Credit quality was also identified as an important characteristic, weighing more heavily for internally-managed portfolios, as opposed to those who oversee externally managed portfolios. Compliance monitoring confirms managers adhere to their investment guidelines, such as sector or country exposure and ratings credit quality. Case #3 showed that one of the managers invests with limits in emerging markets, corporate or municipal bonds, and below investment grade non-sovereign debt. Compliance alerted the client to any violations, allowing them to address unauthorized exposure or risks.

How BNY Mellon Can Help You

The information and figures used within this paper were generated using BNY Mellon's Performance & Risk Analytics reporting capabilities. What makes BNY Mellon's reporting capabilities unique is the ability for both asset owners and financial institutions to use the same reports, and underlying data for consistency, but for different purposes. While an asset owner is reviewing an entire asset class for over/under performance and intentional/ unintentional bets, a financial institution uses the reports to demonstrate their investment philosophy and confirm style and consistency over time.

These capabilities include:

Fixed Income Profile

The Fixed Income Profile takes a bottom-up approach to analyzing the key characteristics of a fixed income portfolio. It begins with using an industry leading methodology for calculating characteristics of the constituents within the portfolio, and aggregates these characteristics to determine the overall structure and risk profile of the portfolio under a variety of market conditions. The profile's side-by-side format allows clients to compare and contrast their portfolio's returns, composition and characteristics to a composite, benchmark, or other portfolios.

Manager At A Glance

The Manager at a Glance report is a dashboard style report that provides a snapshot of your equity and fixed income accounts. The report combines performance, risk statistics, characteristics and universe information along with customized commentary all on a single page to effectively communicate key information on the equity account. Fourteen different tables and charts can be displayed on a single page creating an executive summary of your account data. The drag and drop functionality makes it easy to produce these high quality summary reports.


A sophisticated web-based graphics and reporting capability that enables clients to compare, understand, and communicate portfolio performance, risk statistics and profile characteristics. Comparisons can be made against universe peer groups, market indices or in absolute terms. This capability provides easy access to information along with a dynamic and flexible display of published and on-demand calculated results.

Multi-Factor Attribution

To complement our core attribution capabilities, we have a strategic alliance with Wilshire Associates to provide multi-factor attribution on both fixed income and equity instruments. The fixed income model provides a full breakdown of outperformance into the impact of yield curve movements, country and currency exposures, quality and sector bets. In addition, by incorporating the same factor model used in its advanced attribution solutions, Wilshire also decomposes risk by the same portfolio manager decisions, enabling assessment of performance and risk in the same factor framework.

Compliance Monitoring

Compliance Monitoring is an essential component of the risk management and investment oversight processes. Our solution is a post-trade, rules-based compliance tracking tool designed to provide a consistent approach to monitoring investment policy goals and guidelines. Monitoring can take place on an absolute basis with specified thresholds or relative to industry benchmarks, empowering you with in-depth analysis of your managers' investment activities.

  • Monitor against established constraints within your account
  • Review exposure to categories of investments or asset types on an absolute or relative basis
  • Manage accounts with environmental, social, and governance investment criteria

About BNY Mellon
BNY Mellon is a global financial services company focused on helping clients manage and service their financial assets, operating in 36 countries and serving more than 100 markets. BNY Mellon is a leading provider of financial services for institutions, corporations and high-net-worth individuals, offering superior investment management and investment services through a worldwide client-focused team. It has $25.8 trillion in assets under custody and administration and $1.26 trillion in assets under management, services $11.8 trillion in outstanding debt and processes global payments averaging $1.5 trillion per day. BNY Mellon is the corporate brand of The Bank of New York Mellon Corporation. Additional information is available at and through Twitter@bnymellon.

Third Party Data and Services

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