F9 RMAG MACRO BOND FUND

IMPROVING RISK RETURN PROFILES THROUGH ADVANCED AI ANALYTICS

Euroclear ISIN: CH1108682167
Bloomberg: BBG01TYZ33L7
ISIN: AE000A4195M9

Quantitative AI Strategies boost performance by improving portfolio’s risk return profiles

Quantitative AI Strategies boost performance by improving portfolio’s risk return profiles

As early innovators in an unsaturated AI application space, our solutions are built with a distinctive first-mover advantage. Our AI and machine learning powered proprietary statistical models provide superior precision and reliability, supported by a comprehensive database covering most of the fixed-income securities across developed and emerging markets.

The Risk Management Analytics Group, RMAG, is backed by a team of experts, including multiple PhD holders, our research is driven by advanced statistical methodologies and proprietary AI/ML components integrated across multiple layers. Our Quantitative Strategies team specializes in global fixed income.

F9 RMAG Identifies corporates with the strongest cashflow and debt metrics relative to the pricing.

Systematic Edge in Credit Marketing ​

Structrural Inefficiencies by Passive Flows

A structural edge exists in U.S. corporate credit due to the dominance of passive investment flows. A large share of trading is driven by bond ETFs executing block transactions based on index composition rather than fundamentals. These flows introduce systematic inefficiencies and create recurring pricing dislocations.

Systematic Identification of Misprices Opportunities

Our quantitative framework is designed to identify and exploit these dislocations at scale.
F9 RMAG continuously screens the entire corporate bond universe to identify issuers with the strongest cash-flow quality and debt metrics relative to their market pricing.
The process is repeated daily, producing a dynamic investable universe of statistically mispriced opportunities.

Predictable Pricing Dynamics in Corporate Bonds

Unlike equities — where valuations rely heavily on sentiment, narratives, or long-term optionality — investment-grade and high-quality corporate bonds exhibit pricing behavior that is more predictable, mean-reverting, and mathematically modelable. Outside of distressed or idiosyncratic situations, credit markets offer a robust foundation for probability-based pricing.

A Repeatable and Defensible Advantage

This combination of structural inefficiencies and our systematic fundamental-quant engine creates a repeatable edge that is difficult for discretionary or passive strategies to replicate.

Quantitative AI Macro Bond Portfolio

Investment Universe

  • Investable universe is US IG and HY corporate bonds drawing from the benchmarks with composite credit rating maximum of A+ and minimum of B-.
  • Bonds are filtered for both credit quality and liquidity.
  • We use the latest LLM’s to classify the risk of issuers.
  • 70% Investment Grade
  • 30% High Yield

Portfolio Constraints

  • Constraints such as concentration risk, or allocation quotas are written into code to optimise the portfolio against.
  • The approach ensures unbiased investment decisions prioritising total returns and risk mitigation.
  • Risk Control
  • Liquidity

Portfolio Selection

  • We identify dislocations in the universe and identify risk adjusted opportunities.
  • The portfolio is constructed for best potential returns based of both yield to maturity and potential short-term misprised instruments for trading profits.
  • Total Returns
  • Diversification

1

Transparent Benchmarking

The Bloomberg U.S. Corporate Investment Grade Benchmark is used to measure portfolio performance. The portfolio will deviate substantially from the benchmark as we do not aim to achieve index replication but can only select bonds from the index.

2

Portfolio Rebalancing

Total and Investment universes are screened daily. Portfolio is balanced regularly when thresholds are met considering both liquidity and market spreads. Estimated portfolio turnover ca 1.2-1.5x per year depending on market volatility

3

Multi Asset Inputs

In addition to screening actual investment universe, the correlated asset prices are systematically analysed Finding hidden cross correlations and delayed price transmission across the asset classes to have early warning signals

4

Portfolio Constraints

Portfolios are optimised to match the target credit profile. Flexible duration, concentration and industry limits allows the portfolio to remain liquid in most market scenarios. The optimization process allocates across sectors and durations to enhance returns, avoiding index replication.

5

Liquidity Considerations

We aim to invest in bonds that are in the top 50th percentile most liquid bonds for their respective asset class. The quantitative analysis dynamically allocates more to US Treasuries in market downturn or stressed environments.

6

Diversification

Maximum allocation to single issuer is 5% of the portfolio indicating minimum portfolio size of 20 issuers. Target portfolio size 35-40 issuers

Portfolio Selection Process

Driving Alpha through Intelligent Portfolio Construction

Initial Filtering

High level portfolio investment constraints defined in code. Proprietary risk scores given to each issuer. Ability to add issuers to “blocked list” to have oversight of risk allocation.

Sample Inputs

Trend Cycle & market momentum. Resistance levels and recovery values. Asset correlations and volatilities. Sensitivity to cross-asset prices. Projected yields and trading profits.

Portfolio Construction

Effort made to enhance best potential return both through yield and trading profits. Continuous monitoring to take advantage of asset movements and market risk changes.

Portfolio Selection Process

Enhancing Returns through Quantitative Strategies

Consistency & Repeatability

Strategies consistently apply defined rules, ensuring repeatability over long periods. Eliminates dependence on individual decision-making and reduces variability in outcomes.

Speed & Efficiency

Can process vast amounts of data rapidly, far exceeding human analytical capabilities. Quickly adapt to new information, capturing opportunities faster than traditional analysis.

Consistency & Repeatability

Quantitative strategies can be thoroughly backtested against historical data to validate effectiveness. Provides clear metrics for strategy refinement before committing capital.

Risk Management

Enhanced ability to quantify and systematically control risk through defined limits and constraints. Continuously monitors risk, adjusting exposures proactively rather than reactively.

Objectivity in Decision Making

Removes subjective opinions, biases, or emotional responses to market volatility. Ensures decision-making remains disciplined, focused purely on data-driven signals.

Scalability

Easily expanded to new markets, asset classes, or investment universes without significantly increasing resource costs. Allows rapid diversification and adaptation to changing market opportunities.

Advantages of Quantitative Strategies

1

Global Screening

Global screening of all corporate financial data available.

2

Rebalancing

Portfolio rebalancing with desired parameters to avoid over trading and loss of performance.

3

Global Screening

Total and Investment universes are screened daily. Portfolio is balanced regularly when thresholds are met considering both liquidity and market spreads. Estimated portfolio turnover ca 1.2-1.5x per year depending on market volatility

4

Global Market Overlay

Systematic market stress indicators together with traditional macro data and fast moving proxies provide layer of safety in credit and duration exposures.

5

Real-time On-going Monitoring

Non-stop real time screening of the portfolio and investable universe. Screening parameters designed to minimize risk of defaults. Forensic Accounting methods applied for constant portfolio monitoring.

F9 Capital Management Ltd, a private company limited by shares, duly registered with the Registration Authority of Abu Dhabi Global Market and licensed under commercial licence number 8 730, having its registered office at Office 202, level 14, Al Sarab Tower, Adgm Square, Al Maryah Island, Abu Dhabi, United Arab Emirates (hereinafter referred to as the “Company” or “F9”). F9 is licensed as a collective investment fund manager by the Financial Services Regulatory Authority (FSRA) as a Category 3C firm and is permitted to control Client Assets. F9 is not licensed to serve Retail Clients and this document is addressed strictly to Professional Clients or Market Counterparties, as defined in FSRA COBS Rulebook Chapter 2.

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