Trading in credit markets often involves managing asymmetrical risks—most notably, the tendency for slow and steady gains punctuated by rare but severe downturns. This risk profile is particularly evident in macro credit strategies implemented through credit default swap (CDS) indices. While the long-run trend of these instruments tends to be upward, crisis episodes can lead to sharp, sudden losses. To improve timing and mitigate drawdowns, credit markets can be classified into favorable or unfavorable regimes using real-time macroeconomic data and machine learning techniques.
This post explores how macro indicators—such as credit growth, bank lending standards, real estate prices, and interest rates—can be used in classification models to predict credit market environments. We assess four popular machine learning classifiers: naïve Bayes, logistic regression, k-nearest neighbors (KNN), and random forest, evaluating their predictive power and trading performance across four key CDS indices.
Understanding Macro Credit Exposure
Macro credit strategies focus on broad credit risk rather than idiosyncratic exposures. In this context, a “long credit” position typically means selling protection via a CDS index, collecting premiums in exchange for assuming default risk across a basket of issuers. The associated return distribution tends to be positively skewed in the long term, but with pronounced downside risks during credit crises. This unique return pattern stems from the asymmetric nature of credit losses and limited upside gains, akin to selling insurance or writing options.
At a systemic level, escalating credit risk often triggers feedback loops—tightening financial conditions lead to more defaults, further deteriorating the lending environment. This contagion effect can spread rapidly across sectors and geographies. As a result, macro credit returns are shaped not just by fundamentals but also by liquidity conditions and broader sentiment.
CDS Index Market Overview
CDS indices offer a liquid and standardized way to gain exposure to credit risk. The market is dominated by two families: CDX, which covers North America and some emerging markets, and iTraxx, which focuses on Europe and Asia. Our analysis centers on four key contracts: CDX investment grade (UIG), CDX high yield (UHY), iTraxx investment grade (EIG), and iTraxx crossover (EHY).
Over two decades, these indices have shown positive long-term returns, interrupted by major drawdowns during the global financial crisis, eurozone debt crisis, and COVID-19 panic. Even with volatility targeting to smooth performance, sharp downturns remain difficult to avoid entirely.
Enhancing Strategy with Macro Classification
While trend-following and volatility targeting offer some protection, they often fall short during regime shifts. Integrating macroeconomic signals into classification models offers a more informed approach. Here, we assign each month to a “good” or “bad” regime based on whether credit returns are expected to be positive or negative, using real-time macro data to train classifiers.
The goal isn’t to pinpoint crisis triggers, which are often unpredictable. Instead, macro-based classification can:
- Identify rising risks based on deteriorating fundamentals.
- Justify short positions even in non-crisis corrections.
- Offer more timely insights into potential regime changes.
We rely on six macro factor groups, sourced from JPMorgan’s Macrosynergy Quantamental System (JPMaQS), including:
- Credit supply conditions from bank surveys.
- Private credit growth relative to GDP.
- Real estate price dynamics, a proxy for collateral health.
- Business confidence, reflecting corporate sentiment.
- Real interest rates, adjusted for inflation.
- Credit spread movements, signaling tightening liquidity.
Machine Learning Models and Process
Each classifier was trained on expanding time windows of historical macro data and CDS returns, producing monthly regime predictions. We applied four classification techniques:
- Naïve Bayes: Uses conditional probabilities, assuming independence among features. Simple but potentially limited by macro variables’ high correlation.
- Logistic Regression: Estimates probabilities via a logistic function, assuming linearity between inputs and output log-odds. This assumption may not hold well for credit returns.
- K-Nearest Neighbors (KNN): Assigns regime based on proximity to historical observations. Intuitive but sensitive to outliers and sample size.
- Random Forest: Builds multiple decision trees using bootstrapped samples and aggregates predictions. This ensemble method offers robustness and adaptability to complex patterns.
Each method was applied across all four CDS indices, with a unified monthly signal created via majority voting. This prevents conflicting signals across markets and enables global positioning decisions.
Performance Results and Strategy Evaluation
We tested each model’s predictive power and economic value through both accuracy metrics and naive trading simulations. Key findings include:
- Prediction Accuracy: Random forest consistently outperformed, with monthly accuracy of 65% and strong balanced accuracy. Logistic regression lagged.
- Naive Long-Short PnLs: Random forest strategies delivered the highest Sharpe and Sortino ratios, significantly outperforming other models.
- Long-Biased PnLs: Adjusting positions with a long bias (more reflective of market structure) showed that only random forest outpaced a simple long-only strategy.
- Volatility-Targeted PnLs: Risk-adjusted performance improved across the board, but again, random forest led. Other methods underperformed or matched the baseline.
Notably, classification models generally fared well during the financial crisis but struggled to anticipate COVID-19’s market impact—likely due to the non-economic nature of that shock.
Final Thoughts
Machine learning offers powerful tools for classifying macro credit environments, but model choice matters. While all four methods yielded some predictive utility, random forest stood out in both accuracy and portfolio returns. By integrating macro data into systematic frameworks, investors can better navigate the asymmetries of credit markets—maximizing returns during stable periods and mitigating losses during crises.
The results suggest that even simple binary classification based on macro factors can substantially enhance credit trading strategies. As data availability and model sophistication continue to improve, macro-informed classification will likely play an increasingly central role in institutional credit risk management.
