Sector rotation · Factor analysis · Aggregate signalsTraditional macro analysis is top-down: GDP forecasts, central bank policy, yield curve dynamics. Our approach is bottom-up: we aggregate signals across thousands of companies to detect macroeconomic shifts before they appear in official statistics. When a significant fraction of industrial companies simultaneously extend their accounts receivable language, or when energy sector risk profiles cluster around a new theme, our systems detect the pattern and translate it into a macro positioning signal.
Sector rotation is typically driven by economic cycle models or relative valuation metrics. We augment these with data-derived signals: capex momentum, hiring versus restructuring sentiment, and guidance revision breadth across sectors. These signals lead traditional indicators because corporate language updates before the economic data confirms the trend. The information is public — it is simply too voluminous for manual analysis to process systematically.
Factor premia — value, momentum, quality, size — are time-varying. Our aggregate data provides signals for factor timing: when quality scores diverge sharply across the universe, the quality premium tends to expand. When momentum names show deteriorating fundamentals, momentum reversals become more probable. We use these data-driven regime indicators to understand which analytical frameworks are most relevant to the current environment.