Field guide to macro and markets

A field guide to macroeconomics and market dynamics.

Introduction: The Top-Down Approach

Macro vs. Micro: Macroeconomic factors drive 70% of stock price movements, while company-specific idiosyncrasies account for only 30%.
Fundamentals Aren’t Enough: Companies with consistently strong fundamentals do not always outperform because individual stocks react differently to macroeconomic conditions.
Core Strategy: Investing success relies on predicting the economic cycle and selecting stocks that naturally fit the incoming macroeconomic backdrop.

The Three Types of Data: Economic data is categorized as leading, coincident, or lagging. Because stocks move in tandem with leading indicators, predicting leading indicators is the essence of investing.
The HOPE Framework: Predicting economic cycles is challenging but becomes easier when conditioned on monetary policy shifts (e.g., interest rate hikes). The economic impact follows a sequential chain:
1. Housing (turns first)
2. Orders (manufacturing)
3. Profits (sell-side research expectations fall)
4. Employment (turns last)

This entire cycle can take up to two years to play out.

The Ultimate Indicator: PMI—which is primarily survey-based—is Piper Sandler’s preferred Leading Economic Indicator (LEI) because it correlates highly with stock returns.
The Predictive Model: The economic cycle is a lagged function of the change (not the absolute level) in stimulus and tightening. Stimulus is defined by a decline in the cost of money (lower interest rates) and the cost of goods (falling wages).

We need to measure a stock’s cycle sensitivity and cycle appetite.

Measuring Cyclical Sensitivity: A common way to measure cyclical sensitivity of the stocks is by using a 9-box grid based on Size (Large, Mid, Small) and Style (Value, Core, Growth).
The Extremes: Large Growth stocks are generally counter-cyclical, while Small Value stocks are the most cyclical.
Key Underlying Drivers:
1. Cyclical sensitivity is actually driven by underlying financial metrics (factors) such as Beta, Return on Equity (ROE), and Debt-to-Equity ratios.
2. Small and Value categories tend to hold a higher percentage of non-profitable stocks with weaker fundamentals, explaining their high cyclicality.

What is a Factor? Similar to sabermetrics in baseball, a factor is a single-number summary measuring a specific, narrow characteristic common to a group of stocks.
Cycle Appetite: Riskier factors outperform during market ascents, while defensive factors outperform during declines.
The Flaws of Sector Investing: Sector investing is sub-optimal for three reasons:
1. Concentration: Some sectors are dominated by just a few massive stocks (e.g., Tech).
2. In-Group Heterogeneity: A single sector contains stocks with wildly different factor profiles.
3. Over-Time Heterogeneity: Sector compositions change over time.
Conclusion: Factors consistently outperform sectors in both magnitude and reliability. Factor leadership is not bound by size, style, or sector.

Cyclical, Not Stable: While dividend factors are widely thought to be defensive and stable, they are actually highly cyclical.
The Quality Pick: Piper Sandler recommends Dividend Growth as a superior, high-quality factor rather than just high dividend yield.
The Problem with Dividend ETFs: Many of the 200+ global dividend ETFs exhibit severe sector biases, meaning their performance is driven more by the sector than the dividend itself.
The Solution: Piper Sandler uses a sector-neutral dividend factor. By intentionally controlling for sector distribution, they significantly outperform traditional, sector-biased dividend ETFs.

The Flaw in Traditional Valuation: Traditional valuation models do not correlate with future returns. Stocks do not fall simply because they are expensive, nor do they rise just because they are cheap.
The Index Problem: Valuation is usually measured relative to an index, but index compositions and sector weights change drastically over time.
The Right Approach: Use valuation to identify stocks that look cheap relative to their current peers’ prices, rather than its own long term average or cycle average.

Diversification & Correlation: In high-correlation market environments, you need more stocks to achieve true diversification. In low-correlation environments, fewer stocks are required.
Barbelling: A “barbell” strategy (weighting only the extremes) is not an effective way to build a balanced, diversified portfolio.
Control for Sectors: When tilting a portfolio toward a specific factor, you must control for sector exposure. For example, strictly applying a valuation factor without sector constraints will force a portfolio to severely underweight technology.