Portfolio Analysis & Optimization Module

Product Description & User Guide

Overview

Building an optimized portfolio of investment managers has traditionally relied on mean-variance optimization — a framework that uses historical returns and standard deviation to map the tradeoff between risk and reward. While this approach is mathematically rigorous, it has a well-documented limitation: it uses backward-looking performance data as a proxy for forward-looking expectations, often producing portfolios that overfit to historical patterns that may not persist.

Aapryl’s Portfolio Analysis module takes a fundamentally different approach. Rather than maximizing historical alpha, it maximizes Projected Manager Skill — Aapryl’s proprietary forward-looking measure of each manager’s expected excess return, derived from the Skill Analysis model. Research has found these skill projections to be more accurate and persistent than raw historical alpha. The result is a portfolio optimizer that is designed to find the best allocation of skilled managers within a user-defined risk and constraint framework.

The module also supports traditional Total Return maximization for users who prefer that objective, and allows a full range of risk minimization targets. Users can constrain allocations with minimum and maximum weight limits, and the output — an efficient frontier with detailed composition, contribution, and cycle coverage analytics — provides everything needed to make and defend an allocation decision.

Learning Goals

• Understand the business problem Aapryl’s Portfolio Analysis module solves
• Understand the difference between traditional Mean-Variance Optimization and Aapryl’s skill-based approach
• Understand the module’s three-step workflow: start a portfolio, set targets and constraints, view results
• Understand how to read and interpret the Efficient Frontier, Allocation Table, and Cycle Coverage outputs
• Understand the key terms and risk metrics used throughout the module

Three-Step Workflow

The Portfolio Analysis module follows a structured three-step process from setup to output:

Aapryl vs. Traditional Mean-Variance Optimization

Aapryl’s optimizer uses industry-standard optimization methodology as its mathematical foundation, but extends it in a critical way: the return estimate used in the objective function is replaced with Aapryl’s proprietary skill-based forward projection rather than historical return.

Optimization Inputs

Before the solver runs, users define four categories of input:

1. Manager Universe

The set of manager products eligible for inclusion in the optimized portfolio. This is typically sourced directly from the Skill Screening module — managers who have passed probability thresholds and other quality filters. Users can also add managers manually or adjust the screening-derived list.

2. Maximize Target (Objective Function)

3. Minimize Target (Risk Constraint)

4. Portfolio Constraints

The optimizer enforces that all weights sum to 100% and that no weight falls outside the min/max bounds set for each asset. Users can also apply custom attributes as additional constraint dimensions for advanced use cases.

Optimization Outputs

Once the solver runs, the module generates four integrated views of the optimized portfolio:

1. Efficient Frontier Chart

• Moving right along the frontier = accepting more risk for higher expected return/skill
• Moving left along the frontier = sacrificing return/skill for lower risk and more stable allocations
• If the frontier is short or flat, the constraint set may be too tight — consider relaxing min/max weight limits

2. Portfolio Stats Table

• Compare Expected Alpha vs. Historical Return: a positive spread (Expected Alpha > Historical Return) suggests the portfolio contains managers whose forward skill profile is improving relative to past performance
• Monitor Turnover when rebalancing an existing portfolio — high turnover may erode the Expected Alpha advantage after transaction costs

3. Portfolio Allocation Table

(Illustrative example — actual values reflect user-selected managers and optimization results)

• Managers where Marginal Alpha Contribution > Marginal Risk Contribution are net positive contributors to portfolio efficiency
• Managers where Marginal Risk Contribution > Marginal Alpha Contribution may be candidates for weight reduction — they add more risk than skill
• Use Min/Max Weight constraints to force inclusion/exclusion of managers regardless of optimizer preference

4. Cycle Coverage Table & Chart

(Illustrative cycle coverage grid — actual coverage determined by manager style analysis outputs)

• The cycle coverage view shows dominant manager styles per phase — and highlights gaps in coverage
• A gap in Recession coverage is a common finding: if no managers in the optimized portfolio have Defensive/Low-Vol style positioning, the portfolio may be exposed during downturns
• Use cycle gaps as a prompt to add a diversifying manager to the universe and re-run the optimizer
• Cycle Coverage complements the quantitative optimization — ensuring the efficient frontier portfolio also holds up qualitatively across regimes

Glossary of Key Terms

Actionable Use Cases

For more information, visit www.aapryl.com

Crowded trades occur when many large market participants pursue the same investment strategies causing overlapping portfolio positions. Portfolio Crowding can be especially risky when negative shocks result in forced liquidations. These “fire sales” may then cause losses for other investors following the same strategy and result in further liquidations, driving stock prices into a downward spiral.

Based on our research, we have found that portfolio crowding can also occur among factor exposures. The financial crisis’ “quant meltdown” is a classic example of this where “factor crashing” led to significant portfolio losses. Portfolio Crowding risk also affects so-called “unanchored” strategies, such as Momentum or Growth, that do not rely on a consistent or independent estimate of fundamental value.

With market activity increasingly leveraged to certain factors, determining the degree of factor crowding in a portfolio is essential to informing factor allocations and managing risk.

Aapryl’s Portfolio Crowding Module analyzes user portfolio data to identify and manage crowding. To measure and predict factor crowding we use three different methodologies:

• Pairwise correlation
• Valuation dispersion
• Fractal dimension

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Purpose: Aapryl’s Crowding Module is designed to identify the extent to which a portfolio is subject to risk from crowded trades which can force security sales at prices substantially below the current market price.

Glossary of Terms:

• Crowded Trades – A market condition created when many market participants trade the same security or securities while employing the same or similar strategy.
• Liquidity –In the context of the Crowding Module, liquidity refers to the extent to which markets allow a security or group of securities to be bought and sold at stable prices.
• Fractal Dimension Analysis – An analysis statistically compares the short-term trading activity against the longer-term trading activity of a given stock to identify potentially crowded trades.
• Pairwise Correlation – a technique for identifying crowding risk that examines the correlation between stocks in a portfolio that have high exposure to a given factor.
• Valuation Dispersion – A technique used to identify potential crowding risk by examining the average dispersion of price to book ratios for the portions of a portfolio with the most and least exposure to a given factor.

Description of Methodology:   The Crowding Module uses 3 techniques to measure crowding risk.  The first, pairwise correlation, looks at the correlation of the stocks in a portfolio that are most exposed to a particular factor.  A high correlation is an indication of potential crowding risk.  The second, valuation dispersion, looks at the price to book ratio of the group of stocks both most and least exposed to a particular factor.  A high rate of dispersion is an indication of potential crowding risk.  This is particularly useful for measuring the crowding risk associated with non-valuation based factors such as momentum or growth.  The final technique, fractal dimension analysis, is based on the premise that all things being equal, a stock with more short term trading has more crowding risk than a stock with less short term trading.  In that context, the technique statistically compares the ratio of short-term trading against long-term trading for stocks with exposure to a particular factor.  All of the techniques are calculated independently.

Information Provided:  Aapryl is able to use the information generated in the crowding module to provide users with charts and graphs that contain information that they can use to assess the extent to which a portfolio is subject to crowding risk.  The charts include the following:

• Pairwise Correlation Chart – Shows the median pairwise correlation across time of the stocks in a portfolio with the most exposure to the selected factor.  A relatively higher correlation indicates that there is a higher risk of crowded trades.
• Valuation Dispersion Chart – Shows the dispersion of the price to book value ratio across time between the stocks that are most exposed to the selected faction and the stocks that are least exposed to the same factor.  A relatively higher dispersion indicates that there is a higher risk of crowded trades.
• Fractal Dimension Chart – Shows the ratio of short-term to long-term trading across time, for a portion of the portfolio against a threshold.  The ratio going above the threshold is an indication that there is relatively more crowding risk in the portfolio.

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Style Analysis Module

Product Description & Analytical Reference Guide

Overview

Style Analysis is the analytical foundation of Aapryl. It is the engine that powers the platform’s core capabilities — from skill measurement and return simulation to economic cycle positioning and factor screening. Understanding how Aapryl uses Style Analysis is essential to interpreting everything else the platform produces.

At its core, Style Analysis is the process of using regression techniques to identify the market factors that best explain a manager’s return behavior. Rather than examining individual securities held in a portfolio, it asks a more fundamental question: how does this portfolio’s return pattern correlate with known market factors? The answer reveals the manager’s true style — often more accurately than self-reported classifications.

Learning Goals

• Understand what Style Analysis is and what can be learned from it
• Understand the difference between Returns Based and Holdings Based Analysis
• Understand the style analysis techniques and concepts used in Aapryl

Two Methods of Style Analysis

Style Analysis can be performed in two distinct ways. Aapryl uses the Returns Based approach exclusively, which offers significant practical advantages at scale.

Returns Based Style Analysis (RBSA) — Academic Foundation

Returns Based Style Analysis was introduced by Nobel Prize winner William Sharpe. It classifies investment strategies using only portfolio returns, employing a multifactor regression model against common market indices that represent distinct investment styles — a technique known as “partitioning the market.”

The CAPM Foundation

RBSA builds on the Capital Asset Pricing Model (CAPM). The fundamental equation is:

Aapryl’s Proprietary Approach

Aapryl’s implementation of RBSA goes beyond standard methodology in several important ways. Style Analysis is the underpinning of most analysis in Aapryl, including Expected Alpha calculation, Skill Analysis, and Economic Cycle positioning.

Clone Portfolios

The key output of Aapryl’s Style Analysis is a Clone Portfolio — a hypothetical portfolio comprised of the mix of style factors that explains the portion of a manager’s return driven by market exposure. It represents the passive “beta” baseline for that manager.

Aapryl calculates two types of Clone Portfolios for every manager:

Factor Universe

Rather than using broad market indices as regression inputs, Aapryl uses a precise set of proprietary style factors as the independent variables. These vary by geography:

• International strategies: MSCI/Barra factor framework
• Domestic strategies: Russell/Axioma factor framework

Aapryl’s Three Proprietary Methodology Principles

The Nine Equity Style Factors

Aapryl’s equity style analysis uses nine proprietary factors as the building blocks of every clone portfolio. Each factor represents a distinct dimension of market exposure, and together they span the full range of investment style characteristics found in institutional equity strategies.

Style Classification: The Aapryl Style Box

Traditional style boxes classify managers along two dimensions: Size (Small/Mid/Large Cap) and Valuation (Value/Blend/Growth). Aapryl’s style box takes a more analytically sophisticated approach by using factor exposures to classify managers on two proprietary axes.

The full box produces nine classification zones. Key properties of the Aapryl Style Box visualization:

• Circles represent the manager’s position at a given point in time
• Circles grow larger as the time period covered becomes more recent — allowing users to see how a manager’s style has evolved from inception to the present
• The manager’s benchmark and Aapryl peer group are overlaid for context
• Consistent clustering in one zone signals style discipline; drift across zones signals style rotation or process change

Style Analysis Charts & Visualizations

The Style Analysis module presents a suite of charts that together provide a complete view of a manager’s factor exposures — as a snapshot, over time, relative to peers, and in the context of the macroeconomic cycle.

1. Factor Composition Charts (Manager & Benchmark)

• Compare the manager pie to the benchmark pie side-by-side to identify deliberate overweights (e.g., manager at 49% Defensive vs. benchmark at 8% Defensive)
• Switch from Static to Dynamic to see if recent style has drifted from long-term positioning
• Factor availability varies by fund and benchmark universe — not all nine factors will appear in every chart

2. Factor Exposure (Style Beta) Over Time Chart

• Use the Dynamic toggle to focus on the trailing 36-month window and see what’s driving the current clone composition
• Compare the time series against the static pie chart — divergences show how much recent style differs from the long-run average
• Value-Growth crossovers are particularly meaningful in identifying regime-driven style shifts

3. Aapryl Style Box (Style Over Time)

• Tight clustering in one zone = strong style consistency
• Rightward drift over time = increasing growth tilt
• Upward drift = increasing defensive / quality orientation
• Manager dots sitting outside the yellow peer group region = style differentiation from peers

4. Factor Exposures vs. Peer Group Average

• Diamond in the top band = manager significantly overweights that factor vs. peers
• Diamond in the bottom band = meaningful underweight relative to the peer universe
• Consistent top/bottom positioning across multiple factors reveals the manager’s signature style tilt
• Benchmark dot divergence from the manager diamond highlights active style bets vs. the index

5. Macroeconomic Cycle Positioning Chart

The chart uses Aapryl’s Style Analysis to derive the manager’s style classification and then applies economic cycle research to show at which phase of the cycle the manager is expected to perform best. A green dot marks the manager’s optimal phase; the orange curve traces the expected path of the economic cycle.

• A manager with strong Defensive/Quality exposure will have a green dot in the Late/Recession zone
• A Cyclical/Value manager will shine in Recovery/Mid phases but may lag in Late and Recession
• Use current macroeconomic environment context to assess whether a manager is in or approaching their optimal performance phase

6. Stress Test (Clone-Based)

Aapryl performs stress tests directly on the clone portfolios constructed in the Style Analysis module. This is distinct from the Skill Analysis stress test, which uses actual and simulated manager returns. The Style Analysis stress test isolates the style component — answering: “How would this factor mix have performed in this crisis?”

• Known stress periods are preloaded — users may also define custom periods
• Compare the manager’s clone bar vs. the benchmark bar to assess whether the manager’s style provides protection or amplifies drawdown during each event
• A clone that held up during the GFC due to high Defensive/Quality exposure validates the style’s historical resilience — even if the manager didn’t exist at the time

7. Fixed Income Style Analysis

For fixed income strategies, Aapryl applies a parallel set of style analysis charts adapted to the unique risk dimensions of bond portfolios. Rather than equity style factors, the key risk dimensions are credit risk and duration (interest rate sensitivity).

Fixed Income Key Risk Measures Over Time

Fixed Income Cyclical Manager Positioning Chart

• The green product dot shows the manager’s optimal performance phase relative to the Agg center
• Greater distance from the red benchmark diamond indicates more active deviation from core exposure
• Position in the Stress quadrant signals credit protection characteristics; position in Ease quadrant indicates rate risk appetite

Style Analysis as Aapryl’s Analytical Foundation

Style Analysis does not stand alone — it is the calculation engine that powers virtually every other analytical output in Aapryl. Understanding this dependency helps users interpret all downstream results correctly.

For more information, visit www.aapryl.com

Predictive Investment Manager Ranking System

With over ten thousand mutual funds and separately managed accounts to choose from, investment allocators have their work cut out for them. They need to choose a few managers to meet their investment needs from an almost…Read More

Using Aapryl Ratings, investors will have the ability to report their overall evaluation of a manager through the Investor Network Rating option, which provides investors a collective assessment of a manager’s skill or lack of.

Combining quantitative analysis from Aapryl with qualitative investor feedback, this module provides a fully informed assessment of investment manager stability and style consistency, leading to more prudent selection decisions.

Aapryl Ratings is a systematic approach to manager evaluation that intends to rate both a manager firm, as well as the products they manage.

How it works

START AN AAPRYL RATINGS ANALYSIS

Aapryl Ratings will help you to evaluate the quantitative and qualitative stability of a single or multiple Manager Products.

VIEW THE AAPRYL RATINGS

View the Aapryl Ratings for each Manager Product(s), broken down by business, product and personnel stability.

CREATE A USER RATING

Evaluate a Manager, based on multiple attributes in order to create your own rating for a manager, as well as provide your experiences with them.

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A challenge that many investors face is the ability to fairly and accurately compare manager products. The existing peer universes available through 3rd party databases tend to lump too many unlike managers in to one large peer group. As a result, decisions are often un-informed. Assembling manager peer groups along traditional dimensions of style and capitalization often leads to inappropriate comparisons.

Aapryl can create peer universes that can be categorized across 12 factor groupings or clusters that incorporate the following dimensions:

• High vs Low Quality
• Large vs Small Cap
• Style, identified as Value, Blend and Growth.

To group managers, Aapryl modifies the K-Means Clustering statistical technique whereby each manager’s factor exposures measured against each cluster’s factor exposures and then assigned to the cluster which minimizes the distance between the manager and cluster’s factor loadings.

How it works

START A CLUSTER

Clustering allows you to create more precise, accurate peer groups given their factor exposures.

SELECT A UNIVERSE

Select an entire universe, single or multiple Manager Products.

ANALYZE CLUSTERS

View and further drill down to create more relevant, precise peer groups for a group of Manager Products.

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Purpose: The Clustering Module’s purpose is to properly classify funds or managers into categories. This is important to users because:

• Third party databases do not necessarily do a good job of classifying managers.
• Proper classification creates better peer groups useful for manager searches and comparisons.
• AAPRYL’s classifications are an essential building block to many of the calculations in the system including Skill Analysis and Skill Screening.

Glossary of Terms:

• K Means Clustering- Statistical method of grouping managers together based on multiple factors in which the average exposure to factors is calculated for all constituents of a universe. Groups or clusters are then formed that minimize the cumulative distance of each constituent from a group’s average.
• Quality- One of the primary classifications of managers in AAPRYL. Managers are separated into High Quality and Low Quality groupings based on their exposure to commonly used factors such as ROE, Earnings Stability, leverage, dividend yield and momentum.
• Hypothetical Beta Portfolio- Established sample portfolios used by AAPRYL as measuring stick to group managers. The sample portfolios used are preloaded into the system and are created by the AAPRYL team using proprietary methodology.
• Style- One of the primary classifications of managers in AAPRYL. Managers are separated into Value, Growth and Blend groupings based on their exposure to commonly used factors.

Description of Methodology: AAPRYL use K-means clustering to classify managers into one of the following six categories: Low Quality Value, Low Quality Blend, Low Quality Growth, High Quality Value, High Quality Blend, and High Quality Growth. The categories are used to create peer groups using the following methodology:

• A regression is run for each manager that calculates exposure to commonly used factors.
• Each manager’s exposures are compared to the exposures of hypothetical portfolios built by the AAPRYL team.
• K-Means clustering is used to group the managers into the categories described so that difference between a manager’s average factor exposure and a group’s average factor exposure is minimized.

Information Provided: Once categorized, AAPRYL is able to provide users with charts and graphs that contain an abundance of useful information which includes the following:

• Peer Groups Composition- Users can see all of the managers included in each of the 6 peer groups defined.
• Long-Term Classification- Users can see which group a particular manager has been categorized in based on the manager’s exposure over time.
• Short-Term Classification- Users can see which group a particular manager has been categorized in based on current exposures.
• Historical Classification- Users can see how a particular manager would have been classified at various points in a manager’s history.

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Skill Analysis Module

Product Description & Analytical Reference Guide

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 to style-matched “clone portfolios” — passive replicas designed to capture the factor exposures embedded in a manager’s strategy.

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.

Learning Goals

• Understand Aapryl’s basic approach to analyzing manager skill
• Understand the components of skill within Aapryl
• Interpret the various charts and tables provided in the Skill Analysis module

Key Concepts & Terminology

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.

Defining & Decomposing Skill

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.

Aapryl then applies two proprietary measurements of skill to each of these components:

Skill Decomposition Tree

The full decomposition flows from True Excess Return into four measurable outputs:

Skill Analysis Charts & Visualizations

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.

1. Excess Return Table

The table is structured in three sections:

2. Skill Attribution Bar Chart

• Green bars consistently exceeding red bars is the signal of a skilled active manager
• A large orange bar alongside a flat green bar means style drove results, not skill

3. Aapryl Skill Components Chart (Over Time)

• Y-axis shows Z-scores relative to the peer group; 0 = peer average (50th percentile)
• AUM is overlaid as a dashed line to help identify whether assets under management correlate with skill changes

4. Growth of $100 Chart

• Manager line above clone line = genuine skill beyond style replication
• Clone line above manager line = the style did the work, not the manager
• Use the Net Difference toggle to quantify how much alpha accumulated over specific periods

5. Manager Composite Performance Table

• Consistent Quartile Rank 1–2 across all horizons is the gold standard signal
• Strong short-term rank with weak ITD rank warrants investigation of process changes or capacity

6. Manager Skill Comparison Scatter Plot

• Blue dots represent all peer funds; hover to reveal names and exact skill values
• Double-click any dot to open that manager’s full Aapryl dashboard for direct comparison
• Toggle the period dropdown (QTD, 1YR, 3YR, 5YR) to test whether positioning is persistent or transient

7. Manager Skill vs. Peer Group Bar Chart

• The orange horizontal line shows where the manager ranks within each stacked period
• Consistent orange line placement in blue/green zones across all horizons signals persistent skill
• Parenthetical peer counts per period (e.g., QTD: 246 funds) confirm universe robustness

8. Manager Skill vs. AUM Correlation Chart

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.

9. Standard Statistical Measures Chart

• Use the dropdown to compare Information Ratio vs. Tracking Error simultaneously
• Rising tracking error with flat IR = more active risk for no incremental reward
• Zoom into crisis periods — does IR hold or collapse? Regime resilience in IR is a strong signal

10. Stress Test Chart

• Black bars = manager cumulative return over the stress period
• Blue bars = benchmark cumulative return for the same window
• Custom periods can be defined via the manager selection wizard using any start and end date
• A black bar consistently less negative than the blue bar across multiple crises is strong evidence of repeatable downside management

11. Market Trend Analysis

Interactive Controls

• 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
• Benchmark selection: Compare against any index, custom benchmark, or peer average
• Trend methodology varies by asset class: equity strategies use market momentum and volatility; fixed income uses credit spreads and yield curve indicators

Actionable Workflows

The following workflows describe how to use the Skill Analysis module in common due diligence and monitoring scenarios.

For more information, visit www.aapryl.com

Skill Screening Module

Product Description & User Guide

Overview

Aapryl’s Skill Screening module is designed to streamline the process of identifying top investment managers. By combining proprietary skill-based analytics with flexible multi-dimensional filtering, the module enables investment professionals to efficiently narrow large manager universes down to high-probability outperformers.

The module supports managers across multiple product types — Mutual Funds, Separate Accounts (SMAs), and ETFs — and covers equity and fixed income strategies globally. Data powering the screens comes from both third-party providers and Aapryl’s own proprietary calculations.

Learning Goals

• Understand the basics of Aapryl’s Screening module
• Use the Screening module to increase the probability of choosing managers who will outperform
• Understand the data points available to both view and screen managers

Step 1: Primary Filters

Users begin by defining the investment universe using the primary filter bar at the top of the Screening module. These filters determine which managers appear in the results table.

Step 2: Results Table

Once primary filters are applied, the results table auto-populates with all matching managers. The table is fully customizable — users can add or remove columns to surface the data points most relevant to their mandate.

• Click any column header in the black heading bar to sort ascending or descending
• Select managers using checkboxes to queue them for deeper analysis
• Aapryl Probability is displayed by default and is the primary outperformance signal

Available Data Fields (50+ Columns)

The following categories of data are available to add to the results table:

Step 3: Secondary Filters

After reviewing the results table, users can apply secondary filters to narrow results further. Any field that has been added to the results table becomes available as a secondary filter criterion.

• Secondary filters support the following operators:
• Greater than (>)
• Greater than or equal to (>=)
• Less than or equal to (<=)

Example Filter Combinations

Step 4: Run Analysis

After using filters to build a shortlist, users select managers and proceed to deeper analytical tools using the action buttons on the right side of the interface.

Fixed Income Skill Screening

Aapryl offers a parallel Skill Screening module specifically designed for fixed income managers. The workflow mirrors the equity module but is optimized for bond strategies, with an expanded set of FI-specific data fields.

Fixed Income-Specific Primary Filters

• Custom Universe, Product Type, Portfolio Strategy, Aapryl Categories (FI-specific)
• Target universes include: Core Investment Grade, Credit Intermediate, EM Hard Currency, and more

Fixed Income Data Fields

In addition to standard performance and ownership fields, the FI module includes 29 sector exposure columns:

Fixed Income Screening Use Cases

• Core mandate: Filter Core Investment Grade + Probability >60% + Duration 4-6
• Tax-exempt: Muni Intermediate + Consistency Score >0.8
• Satellite allocation: Credit Long + Expected Alpha >1.5%
• Save frequently-used FI universes as Custom Universe templates for recurring screens

Key Insights & Best Practices

Equity Screening

• Aapryl Probability above 70% identifies managers with high odds of top-quartile performance over 3 years
• Edge Score dominance in Stock Selection vs. Factor Timing reveals whether alpha comes from security picks or style rotations
• Long inception dates with low fees balance experience against cost drag
• Market Cycle Placement shows which economic phases favor each manager

Fixed Income Screening

• Agency MBS + Treasuries Intermediate dominance signals a liquidity focus
• EM HY + Bank Loans tilts indicate yield-seeking strategies
• Edge Score superiority in Security Selection over Factor Timing identifies strong credit pickers vs. duration timers
• High Aapryl Probability (>70%) combined with ownership diversity metrics supports dual mandates

For more information, visit www.aapryl.com

Return Simulator Module

Product Description & User Guide

Overview

A common challenge in manager evaluation is that newer managers lack the performance history required to be assessed on equal footing with established peers. A manager with only two or three years of actual returns cannot be meaningfully compared to one with a ten-year track record — even if the newer manager’s strategy is identical in style and process.

Aapryl’s Return Simulator module addresses this directly. By applying style analysis to a manager’s existing returns, the module constructs a clone portfolio that captures the manager’s factor exposures — then uses that clone to backfill simulated monthly returns for periods prior to the manager’s inception date. The result is a blended track record that allows newer managers to be evaluated alongside long-tenured peers across all standard analytical horizons.

The methodology underlying the Return Simulator is also applied across other Aapryl modules, making it foundational to how the platform evaluates shorter-history managers throughout the system.

Learning Goals

• Understand the business problem the Return Simulator addresses
• Understand the basic methodology used to backfill returns
• Understand the information provided in the module and how it is applied throughout Aapryl

Key Terms & Concepts

The Return Simulator is built on the same foundational concepts as the Skill Analysis module. These terms are used throughout every chart and table in the module.

Backfill Methodology

The Return Simulator generates historical returns in three steps:

The formula for each backfilled month is:

Module Charts & Visualizations

The Return Simulator module presents four key views that together form a complete picture of a manager’s blended track record. In each chart, simulated and actual returns are visually distinguished so users always know which portion of the history is modeled versus real.

1. Return Series Table

• Actual and simulated columns are mutually exclusive — each date will appear in only one column
• The column headers indicate the total length of each return series, giving users immediate context on how much history is real vs. modeled
• The combined length of both columns represents the full blended track record available for analysis

2. Growth of $100 Chart

• The transition point from dashed to solid marks the manager’s actual inception date
• A manager’s simulated line significantly above the benchmark during historical downturns (e.g., the GFC) validates the defensive characteristics of the manager’s style
• The Y-axis shows the cumulative value of a $100 initial investment — a value of 500 means $100 grew to $500

3. Annualized Performance Chart

• Periods that require simulated data to complete (e.g., a manager with 4 years of actual history showing a 10YR bar) rely on the backfilled return series for the earlier months
• This allows direct apples-to-apples comparison between newer and more established managers on a level playing field
• The chart reflects the same blended series used throughout the rest of the module

4. Stress Test Chart

• Results for pre-inception events use simulated returns — they represent modeled, not actual, performance during those crises
• Despite using modeled data, these results provide meaningful signal about how the manager’s style historically behaved under stress
• A manager showing -6% vs. a benchmark -27% in the Tech Bubble validates a style with low growth exposure; the pattern should be cross-referenced against the clone analysis
• Custom stress periods can be defined using the manager selection wizard for any user-specified date range

How the Return Simulator Connects to Other Modules

The Return Simulator is not a standalone feature — the blended return methodology it generates feeds directly into other areas of Aapryl, ensuring that newer managers are evaluated consistently across the entire platform.

Important Disclosures & Interpretation Guidelines

• Simulated returns are always clearly distinguished from actual returns in every chart and table within the module
• A minimum of 3 years of actual return history is required before the Return Simulator can generate backfilled data
• The quality of the simulation depends on the reliability of the style analysis — a high R-squared from the regression indicates the clone is a good fit; a low R-squared may indicate the manager’s returns are not well-explained by standard style factors
• Expected alpha is derived from the manager’s historical skill record and is applied consistently to the backfilled period — it does not assume the manager performed better or worse than observed during the actual period
• Users should cross-reference simulated stress test results with the Skill Analysis module to understand whether performance during a modeled crisis reflects style characteristics or active decisions

Actionable Use Cases

For more information, visit www.aapryl.com