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Macro-Conditioned Trend Signals: A Smarter Way to Trade Short-Term Market Moves

Financial markets don’t operate in a vacuum—short-term price trends often carry deeper macroeconomic implications. While past returns sometimes forecast future price movements, their predictive power tends to be unstable. What often determines whether a trend continues or reverses is the prevailing economic environment.

In this post, we examine how the relationship between recent futures market trends and upcoming equity returns, particularly for the S&P 500, shifts depending on U.S. inflation dynamics. Our findings suggest that incorporating macro conditions into short-term trend signals improves predictive accuracy and adds meaningful value to trading strategies.


Why Macro Context Matters for Short-Term Signals

Short-term return patterns can signal future market behavior—particularly if price changes are only partially absorbed due to sluggish information processing. These patterns arise for two reasons:

  1. Cross-market linkages: Moves in one asset class (e.g., commodities) can influence expectations for another (e.g., equities), especially when inflation or interest rate outlooks are affected.
  2. Policy response dynamics: A rise in asset prices can alter expectations for central bank actions. For example, surging equities in a high-inflation environment may provoke monetary tightening, while similar gains in a deflationary setting might be ignored or even welcomed.

Because these relationships vary with macro conditions, it’s logical to condition trend-following strategies on relevant economic indicators. Ignoring this context can result in misleading signals.


Testing the Hypotheses: How Inflation Alters Trend Impacts

We explore two specific hypotheses:


Constructing Short-Term Trend Signals

We calculate short-term return trends using a straightforward moving average method—specifically, the difference between a 3-day and a 10-day moving average. For robustness, we also examine a faster alternative using a 1-day versus 5-day comparison.

For commodity markets, we define three groups:

Each group’s trend signal is based on equal-weighted returns of its constituent contracts, and we also create a composite commodity trend as well as an aggregate trend that includes both commodity and equity price movements.

Importantly, all trend signals are constructed without any look-ahead bias. Contracts are used from their respective inception dates, and signals are recalculated consistently across time.


Defining the Inflation Environment

To condition signals, we develop four inflation scores using macroeconomic data from the U.S., adjusted for the Federal Reserve’s inflation target:

  1. Excess CPI inflation – Measures the deviation of consumer prices from target, using both headline and core data.
  2. Excess PPI inflation – Captures inflation from the producer side, reflecting cost pressures within the economy.
  3. Inflation from credit, earnings, and housing – Uses broader indicators like retail sales, credit expansion, wage growth, and house prices.
  4. Composite inflation pressure – A normalized blend of the above three scores.

These scores are slow-moving and shift signs roughly once every few years. They are normalized using rolling standard deviations and are winsorized to reduce the impact of outliers.


Building Conditional Trend Signals

Conditional trend signals are created by multiplying each short-term return trend with the inflation score. The sign of the inflation score determines how the original trend is interpreted:

This macro-conditioning introduces both asymmetry and context to trend signals, distinguishing them from traditional momentum approaches.


Evaluating Predictive Power

To assess performance, we test how well both conditional and unconditional trend signals forecast weekly S&P 500 futures returns. We ask:

  1. Do short-term futures trends predict next-week equity returns?
  2. Does the signal improve when conditioned on inflation?

The results are clear: all versions of the short-term trend signal show a statistically significant predictive relationship with future S&P returns. But macro-conditioned signals consistently outperform their unconditional counterparts—both in terms of correlation and statistical confidence.

Moreover, conditional signals improve directional accuracy. While unconditional signals hover around 50% correct predictions, macro-informed versions reach closer to 52%, with balanced accuracy also improving.


Translating Signals into PnL

To determine whether these signals generate real value, we simulate naïve profit and loss series. Each week, a position is taken based on the previous week’s signal, scaled to a target of 10% annualized volatility. Positions are capped at four standard deviations to limit tail risk.

Key findings include:

The seasonal nature of returns—stronger in periods of extreme inflation dynamics—further supports the case for conditioning trend signals on macro context.


Leveraging the Strategy Further

While the standalone Sharpe ratios may not seem exceptional, especially for a single instrument strategy, the real value lies in scalability and diversification.

One practical enhancement is applying these signals as a volatility-scaled overlay to a long-only S&P 500 position. Doing so increases exposure during favorable conditions and reduces it during high-risk periods. In a simulation, this simple overlay lifted the Sharpe ratio from 0.6 to nearly 0.8 and the Sortino from above 0.8 to over 1—without introducing complex leverage or risk management rules.


Conclusion

Short-term trend-following strategies are notoriously fragile. But by embedding them within a macroeconomic framework—especially inflation dynamics—we can enhance both their accuracy and robustness. The evidence suggests that conditional trend signals are a compelling tool for managing equity exposure, especially when combined with simple risk-adjusted overlays.

For investors seeking an edge in volatile markets, blending high-frequency market data with slower-moving macro insights may be the key to more adaptive, reliable returns.

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