Ambiguity and market returns: A composite index perspective

This research investigates ambiguity, uncertainty arising from doubts about predictive models, and its impact on market returns. Traditional asset-pricing frameworks treat uncertainty as quantifiable risk with known probabilities, whereas ambiguity represents a deeper challenge because investors cannot fully trust the models that generate forecasts. A composite ambiguity measure is constructed using principal component analysis of three established proxies: the CBOE Volatility Index (VIX), the Economic Policy Uncertainty (EPU) Index, and the RavenPack News Sentiment (SENT) Index. The resulting measure captures belief dispersion across financial, policy, and informational domains within a single interpretable factor. Weekly U.S. data from September 2005 to January 2025 are analyzed to assess whether ambiguity helps explain variation in aggregate market returns. The study findings indicate that higher ambiguity is consistently associated with lower market returns, reflecting investor ambiguity aversion and reduced confidence in predictive models. The revealed pattern suggests that ambiguity amplifies return fluctuations in a state-dependent manner, with effects strengthening during periods of financial stress, such as the global financial crisis, the COVID-19 shock, and the 2022 monetary-tightening cycle. Overall, ambiguity emerges as persistent and state-dependent component of the U.S. equity market, with important implications for theory, policy, and portfolio design. Robustness across selected controls, PCA designs, and sample periods shows that the composite ambiguity factor (CAF) models uncertainty rather than transitory sentiment.