J.P. Morgan Macrosynergy: Transforming asset management with macroquantamental signals

November 12, 2025

In today’s data-driven markets, systematic investing dominates much of global capital allocation. Nevertheless, one of the most enduring sources of returns – macroeconomic change – remains underutilized. The missing link has been reliable, timestamped economic data suited for investment strategy development. Macro-quantamental signals now offer that solution.

By Robert Enserro and Ralph Sueppel

Macro-quantamental signals are a new class of point-intime, economically grounded indicators, enabling the integration of real-time macroeconomic data into the development of systematic trading strategies and discretionary trading tools, producing higher quality backtests, and realizing the opportunity to create macroeconomically oriented machine learning workflows. By linking economic fundamentals directly to investment strategies, they are quietly reshaping how institutions extract value from macroeconomic information more efficiently and more effectively.

Macro matters

For decades, investors have relied on price action, technical patterns, and flow data to guide trading decisions. But behind every sustained market trend lies a real-world driver: growth, inflation, policy change, or credit stress. Macro matters, not just because it influences risk appetite, but because it shapes the expected payoff of assets in tangible, directional ways.

Yet, while macroeconomic data has always been influential, most investment platforms developed without the tools to handle it properly. Conventional economic databases:

• overwrite data retroactively,
• lack publication and revision timestamps, and
• use evolving definitions that obscure historical reality.

As a result, discretionary investors have been using inferior economic data for years or spent considerable time cleaning economic data and restoring it to its original unrevised state. Systematic strategies have simply ignored one of the richest and most intuitive sources of information, just because it wasn’t in the right format. Bringing it into the right format requires time, skills and material investment.

Systematic insight

The J.P. Morgan Macrosynergy Quantamental System (JPMaQS), jointly developed by J.P. Morgan and Macrosynergy, generates more than 25,000 revision-aware daily macroquantamental indicators, many extending back to the 1990s. Behind the scenes, it requires a vintage data ware- house, which is hundreds of times larger than traditional economic databases and is sourced from global statistical agencies and research providers. It harmonizes inputs and reconstructs model-driven indicators such as nowcasts. The indi- cators capture the true state of knowledge at each historical moment, thereby enabling the integration of daily, point-in-time macroeconomic data into investment frameworks and academic research without compromising scale, precision, or backtest integrity.

Why macroquantamental signals matter for investors

The integration of macroquantamental indicators into institutional portfolios is more than a technical enhancement. It delivers real strategic benefits by providing a broader information set beyond price and flow data to include core economic drivers. It also helps to improve market efficiency by ‘pricing-in’ macro events faster, identifying implausible premia, and correcting distortions. Ultimately, macro-quantamental indicators allow investors to scale beyond human judgment, enabling the processing of daily macro releases from dozens of developed and emerging countries, which would be impossible for manual research teams.

Macro-quantamental signals create value by:

1. Tracking macro trends
   Macroeconomic trends influence both investor sentiment and asset fundamentals. Signals based on inflation surprises or employment momentum can pre-empt changes in risk appetite or policy outlook, offering predictive power across asset classes.
2. Detecting implicit subsidies
   Market inefficiencies often stem from nonprofit motives: central bank interventions, regulatory mandates, or behavioral biases. Macroquantamental tools help identify excess returns tied to these distortions, such as currency carry premiums shaped by deflation fears or policy divergence.
3. Identifying price distortions
   Misalignments between asset prices and fundamentals arise from rigid mandates, liquidity crunches, or institutional frictions. Tools like purchasing power parity (PPP) or financial conditions indices can flag overvaluations in FX or sovereign spreads.
4. Managing endogenous risk
   Crowded trades carry ‘setback risk’: large losses despite fundamentally sound positions. Macroquantamental indicators track capital flows, current accounts, and public borrowing needs, offering early warnings of instability.

Macro-quantamental trading signals strengthen price-only approaches across markets, frequencies, and signal-generation principles. The signals can be used to identify market direction and timing. Simple, non-optimized macroquantamental signals have displayed significant predictive power of directional returns across and between asset classes. These signals merely follow standard economic theory and common sense. For example, a balanced composite cyclical indicator built from pointin- time measures of excess GDP growth, labor-market tightness, and excess inflation can tilt exposure in equities, rates, FX, and commodities, and improve trade timing.

They also foster trend enhancement. Classic trends follow prices, while macro trends follow fundamentals. Price trends are timelier, while macro trends provide more specific information. Combined, for scaling or pausing momentum, they typically add more value than either alone.

Carry strategy precision can also be improved through macro-quantamental signals. Adjusting carry for inflation and policy differentials, terms-of-trade shifts, or growth gaps improves FX carry robustness. Similar quantamental filters help commodity carry avoid late-cycle traps.

For equity investors, macroquantamental signals will improve equity allocation across sectors and countries, such as signals tied to policy cycles, liquidity, and credit guide sector and country tilts. Cross-country success often comes from differentiating indicators linked to monetary stance and earnings growth in local currency.

Point-in-time updates of recorded economic developments provide daily or weekly signals and thrive when escalatory shifts are underestimated, adding a layer of tail-risk protection when leveraging higherfrequency macro signals.

Macro-quantamental signals can also be applied to machine learning integration. Theoretical priors generate a broad universe of candidate macro features, and revision-aware, time-stamped construction enables realistic backtests. The core task is to select and combine features consistently through time, an area where statistical learning and macro-quantamental signals are natural complements.

Overall, macro-quantamental signals add another invaluable ‘lens’: they indicate when price signals deserve trust, how much risk to allocate, and where to deploy capital for the best risk-return profile.

Overcoming adoption barriers

Despite their promise, macro-quantamental systems face hurdles, which include:

• Data overload: compete with less actionable ‘alternative data’ in crowded research pipelines.
• Macro skepticism: noneconomist quants may distrust macro theory due to its jargon and perceived subjectivity.
• Cost perception: quantamental platforms appear costly, yet they reduce internal development time and deliver pre-modeled, tradable signals.
• Access models: JPMaQS offers research access freely but requires a subscription for full integration, balancing openness and exclusivity.

The future of macrodriven investing

Notwithstanding some of the traditional adoption barriers, macro-quantamental signals do more than enhance backtests. They represent a structural evolution in how capital responds to economic reality. As macro shocks – from inflation to geopolitics – reassert their influence, the ability to systematically absorb and act on point-in-time economic data becomes a competitive edge.

For institutional investors seeking robust, explainable, and economically grounded returns, macro-quantamental signals are no longer a research niche. They are becoming a tested standard and a strategic capability for a more responsive, resilient investment process.