While the Correlation Matrix summarizes a single number for the full period, the relationship between two managers rarely stays constant. This chart tracks how one selected manager’s correlation to each of its peers has moved over time, using a rolling window of monthly excess returns.

Chart Elements

• Manager selector: Choose the manager whose relationships you want to trace; every peer line is recalculated relative to that manager.
• Window selector: Choose the rolling window length, 12, 24, 36, or 60 months. Shorter windows react faster to recent change; longer windows smooth out noise.
• Chart lines: Each colored line is the rolling correlation between the selected manager and one peer, recalculated and plotted for every month in the date range.
• Hover detail: Hovering over the chart shows the exact rolling correlation for every peer in that specific month.
• Summary table: Below the chart, Latest, Avg, Min, and Max summarize each peer’s rolling correlation across the full history shown.

How It Works

For each month, Aapryl computes the correlation of the selected manager’s trailing excess returns, over the chosen window, against each peer’s trailing excess returns over that same window, then plots that value as a single point on the corresponding line. Sliding the window forward one month at a time produces the full time series, so each line traces how a relationship has strengthened, weakened, or reversed.

Key Insights to Spot

A line that stays consistently high signals a durable, structural similarity between two managers. A line that swings between strongly positive and negative territory signals an unstable relationship that a single-period correlation would mask. Compare the Min and Max columns in the summary table: a wide range means the current reading may not be representative of how the pair behaves going forward.

Actionable Uses

Use this chart to confirm whether a diversification benefit seen in the Correlation Matrix has been persistent or is a product of a particular period. It is also useful for spotting style drift, since a manager whose correlation to peers changes markedly after a certain date may be behaving differently than in the past, and for choosing an appropriate rolling window length when monitoring managers going forward.

This chart shows the full-period correlation between every manager (and the benchmark) in the current view, calculated from each series’ excess return relative to the benchmark. It’s the fastest way to see which managers tend to move together and which ones offer genuine diversification.

Chart Elements

• Rows and columns: The same list of managers and the benchmark appears on both axes, so every series can be compared against every other series.
• Correlation value: Each cell shows the correlation coefficient, from -1.00 to +1.00, between the two series’ excess returns over the full period selected at the top of the page.
• Diagonal: The diagonal is always 1.00, since each series is perfectly correlated with itself.
• Color scale: Blue shading marks positive correlation and red shading marks negative correlation, with darker shades toward the extremes.
• “—” cells: A dash means the correlation is undefined, typically because the two series have less than 12 months of overlapping history.
• Returns toggle: The Returns selector above the chart switches the calculation between Excess Returns (vs. the benchmark) and raw Returns.
• Series (Rows) checkboxes: These add each manager’s Static Clone and/or Dynamic Clone return series to the matrix alongside its Actual returns.

How It Works

Aapryl calculates the monthly excess return for each manager and the benchmark, then computes the pairwise correlation of those return streams across the selected date range (or across however many months of overlapping history the two series share). Because the calculation is based on excess returns, the matrix reflects how similarly managers behave relative to the benchmark, not simply how similarly they move with the broad market.

Key Insights to Spot

Dark blue cells between two active managers point to redundant exposures worth consolidating. Red cells highlight the pairs doing the most to offset one another. Dashes flag manager pairs with too little shared history to compare reliably, which is common for newer managers.

Actionable Uses

Use this chart when building or rebalancing a multi-manager lineup to identify which managers overlap and which pairings add real diversification. It is also useful in manager due diligence, and for comparing an active manager to its own Static or Dynamic Clone to see how much of its behavior is captured by a lower-cost systematic alternative.

This chart maps a fixed income manager’s positioning across four monetary and credit cycle phases, defined by two axes: Duration Risk (higher on the left, lower on the right) and Spread Risk (higher at the top, lower at the bottom). Each quadrant represents a distinct macro regime — Easy & Easing, Easy But Tightening, Tight & Easing, and Tight & Tightening — reflecting the combination of financial conditions and Fed policy in effect. The green dot marks the manager’s current positioning, while the red diamond marks the Bloomberg Barclays Aggregate Index, serving as the neutral benchmark reference point.

Chart Elements

• Four quadrants: The circle is divided into four phases — Easy & Easing (upper left), Easy But Tightening (upper right), Tight & Easing (lower left), and Tight & Tightening (lower right) — each representing a distinct combination of financial conditions and monetary policy direction.
• Duration Risk axis: The horizontal axis measures Duration Risk, with higher duration exposure on the left and lower on the right. A manager positioned to the left carries more interest rate sensitivity than the benchmark; one on the right carries less.
• Spread Risk axis: The vertical axis measures Spread Risk, with higher credit spread exposure at the top and lower at the bottom. Positioning above center reflects a greater tilt toward credit-sensitive sectors relative to the benchmark.
• Phase descriptions: Each quadrant is annotated with the macro conditions that define that phase — covering financial conditions, the direction of the Fed Funds rate, corporate profit trends, credit spread behavior, and yield curve shape — providing the fundamental rationale for why certain strategies perform well in that environment.
• Green dot (Manager Product): The green dot identifies where the manager’s current risk profile — relative levels of duration and spread exposure — places them within the cycle framework, indicating which phase their strategy is best suited for.
• Red diamond (Benchmark): The red diamond marks the Bloomberg Barclays Aggregate Index at the center of the chart, representing a neutral, benchmark-level posture with respect to both duration and spread risk. The manager’s green dot position should always be interpreted relative to this anchor.

How It Works

Aapryl derives each manager’s position by measuring their duration and spread risk exposures relative to the Bloomberg Barclays Aggregate benchmark. Those exposures are then mapped to the quadrant whose macro characteristics — Fed policy direction, financial conditions, credit spread dynamics, and yield curve shape — best match the environment in which that risk profile has historically outperformed the Core Bond Index. The green dot’s distance from the red diamond reflects the magnitude of the manager’s active risk tilt, while its quadrant reveals the macro regime where that tilt is most rewarded.

Key Insights to Spot

A green dot in the Easy But Tightening quadrant (upper right) — as shown in the example — signals that the manager carries higher spread risk but lower duration risk than the benchmark, a profile suited to environments where financial conditions remain accommodative but the Fed has begun raising rates. A green dot far from the red diamond indicates a high-conviction active tilt, while one clustered near the center suggests benchmark-hugging behavior. A mismatch between the manager’s quadrant and the current macro regime flags potential headwinds, while alignment between the two supports a favorable tactical outlook. Comparing multiple managers’ positions on the same chart reveals how diversified or concentrated a fixed income portfolio is across macro regimes.

Actionable Uses

Use this chart alongside current monetary policy signals to assess whether a manager’s duration and spread posture aligns with the prevailing cycle phase. In portfolio construction, map managers across all four quadrants to ensure macro diversification and avoid inadvertent concentration in a single rate or credit environment. In due diligence, use the chart to probe whether a manager’s active tilts are intentional and cycle-aware, or a byproduct of style drift. The benchmark anchor makes it straightforward to assess the true active risk being taken on relative to the Core Bond Index.

Chart Elements

• Lines: Track six skill components—Stock Selection Edge (magnitude of outperformance from security picks), Style Timing Edge (from factor/style shifts), Total Edge (combined), and their Consistency counterparts (frequency of positive results).
• Y-Axis: Z-scores normalized to peer-group percentiles (0 as average/50th percentile; positive = above peers).
• X-Axis: Monthly time series across market cycles, revealing trends and regime shifts.
• Dashed Line: Assets under management (AUM) in levels, overlaid to correlate capacity with skill evolution.

Aapryl’s Skill Methodology

Aapryl isolates true skill by subtracting passive “clones” from manager returns. Static clones replicate long-term factor exposures (e.g., quality, value). Dynamic clones adjust over rolling 36-month windows to capture recent style drifts. Excess return = manager minus dynamic clone (pure stock picks) + dynamic minus static (timing edge).

• Edge Metrics: Omega ratio-inspired—rewards large wins over small losses, scaled vs. peers.
• Consistency Metrics: Batting average of positive excess periods, risk-adjusted for track record length and market volatility.
• Z-Score Normalization: Ranks vs. specific peer universe (here, Global High Quality Blend), enabling cross-manager comparisons.
• Forward Prediction: Aggregates into Aapryl Score, forecasting 3-year top-quartile odds (high scores >60th percentile show ~70% hit rate in backtests).

This returns-based decomposition avoids self-reported biases, focusing on repeatable alpha sources.

Key Insights to Spot

• Skill Drivers: Dominant lines reveal if alpha comes from picks, timing, or balance.
• Persistence: Steady high ranks (>70th percentile) signal process strength; volatility flags regime dependence.
• AUM Correlation: Inflows during skill peaks validate market recognition of edge.

Actionable Uses

• Due Diligence: Confirm narrative (e.g., “quality stock pickers”) matches top-ranked selection lines.
• Monitoring: Watch for sustained drops in core components signaling process erosion.
• Manager Selection: Prioritize stable, high lines in mandate-aligned skills over total return alone.

Chart Elements

• Lines:
  • Manager Actual: Growth of $100 invested in the real fund.
  • Manager Clone: Growth of $100 in the Aapryl clone replicating the manager’s style.
  • Comparison: Growth of $100 in the selected comparator (e.g., clone benchmark or peer average).
• Y-Axis: Cumulative value of the initial $100 investment.
• X-Axis: Time periods across the manager’s track record.
• Benchmarks: Clone Benchmark (peer-adjusted style); Actual Benchmark (broad market index)—each tracking $100 growth.

Interactive Options:

• Select/deselect: Actual fund, clones, clone benchmark, actual benchmark.
• Toggle: Comparison line (overlay selected items) or Net Difference line (excess return gaps).

How Clones Work

Aapryl clones are passive portfolios that mirror a manager’s factor exposures (e.g., quality, value). The Manager Clone dynamically tracks recent style, enabling clear separation of active decisions from passive replication. Use toggles to spotlight differences revealing true skill.

Key Insights to Spot

• Outperformance Source: Manager line above clones shows added value from decisions beyond style.
• Style Fit: Clone proximity confirms alignment with stated approach.
• Benchmark Context: Compare against clone benchmark for peer-relative success.
• Excess Gaps: Net difference toggle quantifies alpha periods (e.g., manager reaches $180 vs. clone at $150).

Actionable Uses

• Skill Breakdown: Toggle net difference to measure contributions over time.
• Process Check: Verify if growth aligns with manager’s pitch.
• Risk Gauge: Large divergences highlight active risk levels.
• Peer Ranking: Apply consistent views for multi-manager evaluation.