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	<title>Skill Analysis &#8211; Aapryl Knowledgebase</title>
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	<item>
		<title>Manager Skill vs. Peer Group</title>
		<link>https://knowledgebase.aapryl.com/modules/manager-skill-vs-peer-group/</link>
		
		<dc:creator><![CDATA[Marc Poitevien]]></dc:creator>
		<pubDate>Tue, 18 Dec 2018 19:43:49 +0000</pubDate>
				<guid isPermaLink="false">https://knowledgebase.aapryl.com/?post_type=ht_kb&#038;p=1397</guid>

					<description><![CDATA[The Manager Skill versus the Peer Group chart shows annualized returns for various time periods against the manager&#8217;s Aapryl Peer Group.  Peer groups are broken into quartiles so that users can visually understand how a manager&#8217;s performance compares to that of its peer group.]]></description>
										<content:encoded><![CDATA[<p>The Manager Skill versus the Peer Group chart shows annualized returns for various time periods against the manager&#8217;s Aapryl Peer Group.  Peer groups are broken into quartiles so that users can visually understand how a manager&#8217;s performance compares to that of its peer group.</p>
]]></content:encoded>
					
		
		
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		<item>
		<title>Skill Analysis</title>
		<link>https://knowledgebase.aapryl.com/modules/skill-analysis/</link>
		
		<dc:creator><![CDATA[Damco]]></dc:creator>
		<pubDate>Thu, 05 Oct 2017 10:09:15 +0000</pubDate>
				<guid isPermaLink="false">https://knowledgebase.aapryl.com/?post_type=ht_kb&#038;p=329</guid>

					<description><![CDATA[Aapryl Skill Analysis Module Product Description &#38; Analytical Reference Guide &#160; Overview Aapryl’s Skill Analysis module provides a comprehensive framework for evaluating the true sources of a manager’s performance. Rather than relying solely on raw returns versus a broad market index, Aapryl isolates genuine skill by comparing each manager’s results [&#8230;]]]></description>
										<content:encoded><![CDATA[<table width="624">
<tbody>
<tr>
<td><strong>Aapryl</strong></p>
<p>Skill Analysis Module</p>
<p><em>Product Description &amp; Analytical Reference Guide</em></td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h1 id="overview" >Overview</h1>
<p>Aapryl’s Skill Analysis module provides a comprehensive framework for evaluating the true sources of a manager’s performance. Rather than relying solely on raw returns versus a broad market index, Aapryl isolates genuine skill by comparing each manager’s results to style-matched “clone portfolios” — passive replicas designed to capture the factor exposures embedded in a manager’s strategy.</p>
<p>&nbsp;</p>
<p>This approach reflects a core Aapryl insight: industry benchmarks can be too broad, incorporating styles that perform differently at various points in the economic cycle. By measuring performance relative to a clone that mirrors a manager’s own style, Aapryl produces a more precise measure of value added — one that separates what the market gave the manager from what the manager actually earned.</p>
<p>&nbsp;</p>
<h1 id="learning-goals" >Learning Goals</h1>
<ul>
<li>Understand Aapryl’s basic approach to analyzing manager skill</li>
<li>Understand the components of skill within Aapryl</li>
<li>Interpret the various charts and tables provided in the Skill Analysis module</li>
</ul>
<p>&nbsp;</p>
<h1 id="key-concepts-terminology" >Key Concepts &amp; Terminology</h1>
<p>The Skill Analysis module is built on a precise set of terms. Understanding these definitions is essential to interpreting every chart and table in the module.</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Style Analysis</strong></td>
<td>Regression analysis performed within Aapryl to determine a manager’s exposures to various market factors (e.g., value, quality, momentum, growth, defensive).</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Clone Portfolio</strong></td>
<td>A hypothetical portfolio designed to emulate the market exposure of a manager’s strategy. It is composed of the various factors that influence a manager’s return and serves as the “pure beta” baseline.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Static Clone</strong></td>
<td>Uses the full history of the manager in the regression. Represents the manager’s long-term, fixed style exposures since inception.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Dynamic Clone</strong></td>
<td>Uses only the last 36 months in the regression. Captures recent tactical style shifts and factor tilts.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Beta</strong></td>
<td>The portion of a manager’s return derived from the market. Within Aapryl, it is the return of the manager’s clone portfolio.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Alpha</strong></td>
<td>Value add, or excess returns over the clone portfolio. The true measure of manager skill after adjusting for style.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h1 id="defining-decomposing-skill" >Defining &amp; Decomposing Skill</h1>
<p>Aapryl dissects the non-style portion of a manager’s performance into three distinct categories of excess return. Together, these explain the totality of what a manager contributed beyond passive style replication.</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>The Three Components of Excess Return</strong></td>
</tr>
<tr>
<td><strong>Total Excess Return (Manager Skill)</strong></td>
<td>Return of the manager minus the Manager Clone Return. This is the headline true skill number — what the manager added beyond their market exposures.</td>
</tr>
<tr>
<td><strong>Style Timing Return</strong></td>
<td>Dynamic Clone Portfolio minus Static Clone Portfolio. Measures the value added (or lost) by tactically shifting factor exposures over time.</td>
</tr>
<tr>
<td><strong>Stock Selection Return</strong></td>
<td>Static Clone Portfolio minus Factor Timing. The portion of skill attributable to individual security selection after removing style effects.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p>Aapryl then applies two proprietary measurements of skill to each of these components:</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>The Two Skill Measurements</strong></td>
</tr>
<tr>
<td><strong>Consistency</strong></td>
<td>Analogous to a batting average. Measures how frequently the manager generates positive excess returns across rolling periods. High consistency signals a repeatable, process-driven edge.</td>
</tr>
<tr>
<td><strong>Edge</strong></td>
<td>A proprietary statistic that measures the magnitude of a manager’s skill returns. An Omega ratio-inspired metric that rewards large wins over small losses, scaled relative to peers.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h2 id="skill-decomposition-tree" >Skill Decomposition Tree</h2>
<p>The full decomposition flows from True Excess Return into four measurable outputs:</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Starting Point</strong></td>
<td><strong>Stock Selection</strong></td>
<td><strong>Style Timing</strong></td>
</tr>
<tr>
<td>True Excess Return (Manager minus Dynamic Clone)</td>
<td>Stock Selection Consistency Stock Selection Edge (Magnitude)</td>
<td>Style Timing Consistency Style Timing Edge (Magnitude)</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h1 id="skill-analysis-charts-visualizations" >Skill Analysis Charts &amp; Visualizations</h1>
<p>The Skill Analysis module presents a suite of interconnected charts. Each is designed to answer a specific analytical question about manager quality, persistence, and competitive positioning.</p>
<p>&nbsp;</p>
<h2 id="1-excess-return-table" >1. Excess Return Table</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Aapryl Excess Return Table</strong></td>
</tr>
<tr>
<td>A numerical summary that shows annualized returns for the manager, benchmark, and both clone portfolios — then decomposes the excess into style effects and skill attribution.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p>The table is structured in three sections:</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Excess Return Table Sections</strong></td>
</tr>
<tr>
<td><strong>Returns</strong></td>
<td>Shows annualized returns for: Manager, Benchmark, Static Clone (long-term style benchmark), Dynamic Clone (short-term style benchmark).</td>
</tr>
<tr>
<td><strong>Traditional Excess</strong></td>
<td>Manager vs. Benchmark — the conventional headline excess return figure.</td>
</tr>
<tr>
<td><strong>Excess Decomposition</strong></td>
<td>Separates excess into: Style Environment (Static Clone minus Benchmark) and Return from Skill (Manager minus Static Clone).</td>
</tr>
<tr>
<td><strong>Skill Decomposition</strong></td>
<td>Further breaks skill into: Style Timing (Dynamic minus Static Clone) and Stock Selection (Return from Skill minus Style Timing).</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h2 id="2-skill-attribution-bar-chart" >2. Skill Attribution Bar Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Skill Attribution Chart</strong></td>
</tr>
<tr>
<td>A dual-panel bar chart that visually separates manager excess returns into style effects (what the market gave the manager) and skill (what the manager earned through active decisions).</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Bar Colors &amp; Meaning</strong></td>
</tr>
<tr>
<td><strong>Blue Bar (Top Panel)</strong></td>
<td>Manager excess return vs. benchmark — the total annualized outperformance headline.</td>
</tr>
<tr>
<td><strong>Orange Bar (Top Panel)</strong></td>
<td>Style Clone excess return vs. benchmark — what passive style replication would have delivered.</td>
</tr>
<tr>
<td><strong>Green Bar (Bottom Panel)</strong></td>
<td>Positive skill return — periods where the manager outperformed the clone. Attributable to active decisions.</td>
</tr>
<tr>
<td><strong>Red Bar (Bottom Panel)</strong></td>
<td>Negative skill return — periods of manager underperformance vs. clone. Flags drag from active decisions.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Green bars consistently exceeding red bars is the signal of a skilled active manager</li>
<li>A large orange bar alongside a flat green bar means style drove results, not skill</li>
</ul>
<p>&nbsp;</p>
<h2 id="3-aapryl-skill-components-chart-over-time" >3. Aapryl Skill Components Chart (Over Time)</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Aapryl Skill Components Time Series</strong></td>
</tr>
<tr>
<td>A multi-line chart that tracks six skill component scores over the manager’s full history. Each line is a Z-score normalized to the manager’s peer group, enabling direct comparison of how skill evolves over market cycles.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="4"><strong>Six Lines on the Chart</strong></td>
</tr>
<tr>
<td><strong>Metric</strong></td>
<td><strong>Type</strong></td>
<td><strong>What It Measures</strong></td>
<td><strong>What to Look For</strong></td>
</tr>
<tr>
<td>Consistency Score (Stock Selection)</td>
<td>Frequency</td>
<td>Batting average of positive excess from picks</td>
<td>Stable line above 0 (50th percentile)</td>
</tr>
<tr>
<td>Edge Score (Stock Selection)</td>
<td>Magnitude</td>
<td>Omega-inspired magnitude of security picks</td>
<td>Rising line signals growing alpha from picks</td>
</tr>
<tr>
<td>Consistency Score (Factor Timing)</td>
<td>Frequency</td>
<td>Batting average of positive timing returns</td>
<td>Useful if manager claims tactical rotation</td>
</tr>
<tr>
<td>Edge Score (Factor Timing)</td>
<td>Magnitude</td>
<td>Magnitude of style rotation contribution</td>
<td>Compare vs. Stock Selection to find primary skill driver</td>
</tr>
<tr>
<td>Aapryl Opportunity Score</td>
<td>Composite</td>
<td>Peer-relative ranking of available alpha</td>
<td>High score in favorable market = ideal conditions</td>
</tr>
<tr>
<td>Aapryl Manager Skill Score</td>
<td>Composite</td>
<td>Aggregate forward-looking skill signal</td>
<td>Dashed — aggregates all components into one view</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Y-axis shows Z-scores relative to the peer group; 0 = peer average (50th percentile)</li>
<li>AUM is overlaid as a dashed line to help identify whether assets under management correlate with skill changes</li>
</ul>
<p>&nbsp;</p>
<h2 id="4-growth-of-100-chart" >4. Growth of $100 Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Growth of $100</strong></td>
</tr>
<tr>
<td>Tracks the cumulative value of a $100 investment across the manager’s full track record, comparing the actual fund, its Aapryl clone, the clone benchmark, and the actual benchmark.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Interactive Lines</strong></td>
</tr>
<tr>
<td><strong>Manager Actual</strong></td>
<td>The real cumulative growth of $100 invested in the fund.</td>
</tr>
<tr>
<td><strong>Manager Clone</strong></td>
<td>What $100 in the Aapryl clone portfolio would have grown to — isolates passive style contribution.</td>
</tr>
<tr>
<td><strong>Clone Benchmark</strong></td>
<td>The peer-adjusted style benchmark — the style universe’s passive performance.</td>
</tr>
<tr>
<td><strong>Actual Benchmark</strong></td>
<td>The broad market index (e.g., MSCI World) for context.</td>
</tr>
<tr>
<td><strong>Net Difference Toggle</strong></td>
<td>Switches from cumulative growth view to a line showing the ongoing excess return gap between manager and clone.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Manager line above clone line = genuine skill beyond style replication</li>
<li>Clone line above manager line = the style did the work, not the manager</li>
<li>Use the Net Difference toggle to quantify how much alpha accumulated over specific periods</li>
</ul>
<p>&nbsp;</p>
<h2 id="5-manager-composite-performance-table" >5. Manager Composite Performance Table</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Manager Composite Performance Table</strong></td>
</tr>
<tr>
<td>A comprehensive multi-horizon performance table that ranks the manager against peers across QTD, CYTD, 1YR, 3YR, 5YR, and ITD periods. Includes clone attribution, peer percentile ranks, and R-squared for each horizon.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Table Rows Explained</strong></td>
</tr>
<tr>
<td><strong>Manager Composite</strong></td>
<td>Total annualized return of the fund for each period.</td>
</tr>
<tr>
<td><strong>Static Clone (Long-Term Style Adj. Bench)</strong></td>
<td>What the manager’s fixed, inception-period factor exposures would have returned passively.</td>
</tr>
<tr>
<td><strong>Benchmark</strong></td>
<td>The broad market index return for context.</td>
</tr>
<tr>
<td><strong>Manager vs. Benchmark</strong></td>
<td>Traditional excess return — the headline number.</td>
</tr>
<tr>
<td><strong>Style Effect (Clone Benchmark)</strong></td>
<td>The passive contribution from the manager’s factor tilts vs. the benchmark.</td>
</tr>
<tr>
<td><strong>Peer Adjusted Alpha (Manager − Static Clone)</strong></td>
<td>Pure active return after removing long-term style. The truest measure of skill.</td>
</tr>
<tr>
<td><strong>Peer Quartile Rank (1 = best, 4 = worst)</strong></td>
<td>Manager’s percentile position within the Aapryl peer universe for each period.</td>
</tr>
<tr>
<td><strong>Peer Funds</strong></td>
<td>Universe size for each period. Larger universes produce more statistically meaningful ranks.</td>
</tr>
<tr>
<td><strong>R-Squared</strong></td>
<td>How well the clone explains the manager’s returns. 70–90% is typical and indicates reliable decomposition.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Consistent Quartile Rank 1–2 across all horizons is the gold standard signal</li>
<li>Strong short-term rank with weak ITD rank warrants investigation of process changes or capacity</li>
</ul>
<p>&nbsp;</p>
<h2 id="6-manager-skill-comparison-scatter-plot" >6. Manager Skill Comparison Scatter Plot</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Manager Skill Decomposition Scatter (Rolling 36 Months, Annualized)</strong></td>
</tr>
<tr>
<td>An interactive scatter plot that positions the selected manager (orange diamond) against all peer funds in the universe, using Stock Selection Skill and Style Timing Skill as the two axes.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Quadrant Interpretation</strong></td>
</tr>
<tr>
<td><strong>Top-Right (Positive Both)</strong></td>
<td>Strong stock selection AND strong timing. Ideal placement — manager adds value across both skill dimensions.</td>
</tr>
<tr>
<td><strong>Top-Left (Selection+, Timing−)</strong></td>
<td>Excellent stock picker but poor factor timing. Common among disciplined bottom-up managers.</td>
</tr>
<tr>
<td><strong>Bottom-Right (Selection−, Timing+)</strong></td>
<td>Skill from style rotation, not individual picks. Evaluate whether timing is repeatable.</td>
</tr>
<tr>
<td><strong>Bottom-Left (Negative Both)</strong></td>
<td>Underperforming on both dimensions versus peers. Warrants close scrutiny.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Blue dots represent all peer funds; hover to reveal names and exact skill values</li>
<li>Double-click any dot to open that manager’s full Aapryl dashboard for direct comparison</li>
<li>Toggle the period dropdown (QTD, 1YR, 3YR, 5YR) to test whether positioning is persistent or transient</li>
</ul>
<p>&nbsp;</p>
<h2 id="7-manager-skill-vs-peer-group-bar-chart" >7. Manager Skill vs. Peer Group Bar Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Skill Return vs. Peer Group (All Horizons)</strong></td>
</tr>
<tr>
<td>A stacked horizontal bar chart that shows the manager’s annualized skill return (peer-adjusted alpha) across QTD, CYTD, 1YR, 3YR, 5YR, and ITD, with the full peer distribution displayed as color-coded percentile bands.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Color Band Legend</strong></td>
</tr>
<tr>
<td><strong>Blue</strong></td>
<td>Top 10% of peers — elite performance</td>
</tr>
<tr>
<td><strong>Green</strong></td>
<td>10th–25th percentile — strong performers</td>
</tr>
<tr>
<td><strong>Yellow</strong></td>
<td>25th–50th percentile — above average</td>
</tr>
<tr>
<td><strong>Light Brown</strong></td>
<td>50th–75th percentile — below average</td>
</tr>
<tr>
<td><strong>Dark Brown</strong></td>
<td>75th–90th percentile — weakest performers</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>The orange horizontal line shows where the manager ranks within each stacked period</li>
<li>Consistent orange line placement in blue/green zones across all horizons signals persistent skill</li>
<li>Parenthetical peer counts per period (e.g., QTD: 246 funds) confirm universe robustness</li>
</ul>
<p>&nbsp;</p>
<h2 id="8-manager-skill-vs-aum-correlation-chart" >8. Manager Skill vs. AUM Correlation Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Manager Skill vs. AUM Correlation</strong></td>
</tr>
<tr>
<td>A scatter plot examining the relationship between the manager’s quarterly skill score and the corresponding level of assets under management. Each dot represents one quarter in the track record.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p>This chart tests the “capacity hypothesis” — whether skill erodes as AUM grows. It is particularly useful during due diligence when evaluating whether a manager can scale without performance drag.</p>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Reading the Scatter</strong></td>
</tr>
<tr>
<td><strong>Positive Slope (Dots trend up-right)</strong></td>
<td>Skill persists or strengthens as assets grow. Suggests a scalable process and favors continued or increased allocation.</td>
</tr>
<tr>
<td><strong>Negative Slope (Dots fall as AUM rises)</strong></td>
<td>Skill decays with growth. May indicate capacity constraints, liquidity pressure, or market impact issues.</td>
</tr>
<tr>
<td><strong>Tight Vertical Clustering</strong></td>
<td>Consistent skill regardless of AUM level. Indicates process stability across the size spectrum.</td>
</tr>
<tr>
<td><strong>Inflection Point</strong></td>
<td>The AUM level where dots shift from high-skill to low-skill quadrants. Use to set allocation caps.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<h2 id="9-standard-statistical-measures-chart" >9. Standard Statistical Measures Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Rolling Statistical Metrics (36-Month Rolling, Dual-Panel)</strong></td>
</tr>
<tr>
<td>A dual-panel line chart tracking key risk-adjusted metrics over rolling periods. Users can toggle between metrics via dropdowns to analyze trade-offs between return consistency and active risk.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Available Metrics</strong></td>
</tr>
<tr>
<td><strong>Information Ratio (IR)</strong></td>
<td>Excess return divided by Tracking Error. &gt;0.5 is good; &gt;1.0 is excellent. Negative IR = value destruction.</td>
</tr>
<tr>
<td><strong>Tracking Error %</strong></td>
<td>Annualized standard deviation of monthly excess returns vs. benchmark. Represents active risk. 4–8% is typical for equity strategies.</td>
</tr>
<tr>
<td><strong>Ann. Volatility %</strong></td>
<td>Standard deviation of total returns. Measures absolute risk independent of the benchmark.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Use the dropdown to compare Information Ratio vs. Tracking Error simultaneously</li>
<li>Rising tracking error with flat IR = more active risk for no incremental reward</li>
<li>Zoom into crisis periods — does IR hold or collapse? Regime resilience in IR is a strong signal</li>
</ul>
<p>&nbsp;</p>
<h2 id="10-stress-test-chart" >10. Stress Test Chart</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Stress Test Chart (Based on Clone Returns)</strong></td>
</tr>
<tr>
<td>A grouped bar chart evaluating manager and benchmark performance during predefined crisis periods. Clone attribution underlies each calculation to separate style effects from genuine skill under stress.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Preset Stress Periods</strong></td>
</tr>
<tr>
<td><strong>European Debt Crisis</strong></td>
<td>April 2010 – July 2011</td>
</tr>
<tr>
<td><strong>Flash Crash</strong></td>
<td>June 2010</td>
</tr>
<tr>
<td><strong>March 2020</strong></td>
<td>Initial pandemic market decline</td>
</tr>
<tr>
<td><strong>COVID-19</strong></td>
<td>January 2020 – March 2020 (full early pandemic drop)</td>
</tr>
<tr>
<td><strong>Great Financial Crisis</strong></td>
<td>October 2007 – February 2009</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<ul>
<li>Black bars = manager cumulative return over the stress period</li>
<li>Blue bars = benchmark cumulative return for the same window</li>
<li>Custom periods can be defined via the manager selection wizard using any start and end date</li>
<li>A black bar consistently less negative than the blue bar across multiple crises is strong evidence of repeatable downside management</li>
</ul>
<p>&nbsp;</p>
<h2 id="11-market-trend-analysis" >11. Market Trend Analysis</h2>
<table width="624">
<tbody>
<tr>
<td><strong>Market Trend Analysis</strong></td>
</tr>
<tr>
<td>Evaluates manager performance segmented by market regimes — Rising Trend, Falling Trend, and No Trend — determined by Aapryl’s objective trend detection models. Includes both a visual trend line chart and a regime-segmented performance table.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Market Trend Chart Elements</strong></td>
</tr>
<tr>
<td><strong>Black Line</strong></td>
<td>The selected market indicator used to determine trend direction (e.g., credit spreads, volatility, equity momentum).</td>
</tr>
<tr>
<td><strong>Green Background</strong></td>
<td>Rising Trend — favorable or improving market conditions.</td>
</tr>
<tr>
<td><strong>Red Background</strong></td>
<td>Falling Trend — deteriorating or challenging conditions.</td>
</tr>
<tr>
<td><strong>White/Neutral</strong></td>
<td>No Trend — no clear directional signal.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td colspan="2"><strong>Performance Table Metrics by Regime</strong></td>
</tr>
<tr>
<td><strong>Annualized Return</strong></td>
<td>Average annual return generated within each trend environment.</td>
</tr>
<tr>
<td><strong>Standard Deviation</strong></td>
<td>Return volatility within that specific regime.</td>
</tr>
<tr>
<td><strong>Sharpe Ratio</strong></td>
<td>Risk-adjusted return showing efficiency of converting risk into reward.</td>
</tr>
<tr>
<td><strong>Upside Capture</strong></td>
<td>Percentage of benchmark gains captured during Rising Trend periods.</td>
</tr>
<tr>
<td><strong>Downside Capture</strong></td>
<td>Percentage of benchmark losses experienced during Falling Trend periods.</td>
</tr>
<tr>
<td><strong>Information Ratio</strong></td>
<td>Risk-adjusted excess return vs. the selected benchmark within each regime.</td>
</tr>
<tr>
<td><strong>Tracking Error</strong></td>
<td>Deviation between strategy and benchmark returns, computed within each regime.</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p><strong>Interactive Controls</strong></p>
<ul>
<li>Strategy toggle: Switch between the actual manager and the manager’s clone to isolate whether results are driven by active decisions or passive style exposure</li>
<li>Benchmark selection: Compare against any index, custom benchmark, or peer average</li>
<li>Trend methodology varies by asset class: equity strategies use market momentum and volatility; fixed income uses credit spreads and yield curve indicators</li>
</ul>
<p>&nbsp;</p>
<h1 id="actionable-workflows" >Actionable Workflows</h1>
<p>The following workflows describe how to use the Skill Analysis module in common due diligence and monitoring scenarios.</p>
<p>&nbsp;</p>
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<tbody>
<tr>
<td colspan="4"><strong>Common Use Cases</strong></td>
</tr>
<tr>
<td><strong>Workflow</strong></td>
<td><strong>Charts Used</strong></td>
<td><strong>What to Look For</strong></td>
<td><strong>Decision Signal</strong></td>
</tr>
<tr>
<td>Initial Manager Screening</td>
<td>Excess Return Table, Performance Table</td>
<td>Peer Adjusted Alpha &gt;1%, Rank 1–2 ITD</td>
<td>Proceed to deeper analysis</td>
</tr>
<tr>
<td>Narrative Validation</td>
<td>Skill Attribution, Skill Components</td>
<td>Stock Selection Edge dominates if “pick-based” claim</td>
<td>Green bars &gt; red; high selection Z-score</td>
</tr>
<tr>
<td>Skill Persistence Check</td>
<td>Skill Components, Peer Bar Chart</td>
<td>Z-scores stable &gt;50th pct. across time</td>
<td>Consistent high lines signal process</td>
</tr>
<tr>
<td>Scalability Assessment</td>
<td>Skill vs. AUM Scatter</td>
<td>Skill holds or grows as AUM rises</td>
<td>Positive slope = scalable strategy</td>
</tr>
<tr>
<td>Crisis Resilience Review</td>
<td>Stress Test, Market Trend</td>
<td>Black bars less negative across 4/5+ events</td>
<td>Repeatable downside protection</td>
</tr>
<tr>
<td>Ongoing Monitoring</td>
<td>Skill Components, Statistical Measures</td>
<td>Watch for score deterioration or IR drop</td>
<td>Decline in 2+ consecutive periods = flag</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<table width="624">
<tbody>
<tr>
<td><strong>Aapryl Skill Analysis — The Complete Picture</strong></p>
<p><em>Clone-adjusted excess returns → Skill decomposition → Peer context → Regime analysis → Forward probability</em></td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p><em>For more information, visit www.aapryl.com</em></p>
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