As deviations of stock prices from intrinsic values grow, there is a concurrence of novel events that impart instability and, thus, greater uncertainty, onto investors’ understanding of the process driving market outcomes. This paper proxies for Knightian uncertainty based on the frequency of unscheduled, or novel, corporate events reported in the financial news as relevant for share prices and earnings prospects each day. Empirical findings suggest unscheduled events help explain variation in expected returns, especially at longer horizons. Results show a significant positive relationship between the absolute stock price-gap from intrinsic values (based on constant and time-varying returns) and the frequency of unscheduled events through time. When shocks derail the longer-run relationship, the stock price-gap adjusts. Valuation-based gaps generate similar results, but display nonlinear effects with unscheduled events by producing positive and significant coefficients only for the largest absolute gap values.

Samuelson’s Dictum argues that aggregate stock markets do not convincingly reflect information on fundamentals, such as dividends or earnings, and are, thus, inefficient in setting prices. By contrast, firm-level stock prices share a much closer connection with fundamentals and are, therefore, deemed relatively efficient. This paper presents an alternative explanation: Knightian uncertainty stemming from historically unique events produces differential impacts on investor forecasting bounds at the aggregate versus firm level. Several uncertainty proxies are employed from millions of unscheduled events identified in the universe of Dow Jones & Co. financial news reports over the last twenty years. Uncertainty based on count and proportion of unscheduled events shares a significant inverse relationship with the range of forecasting bounds on returns and earnings at both the aggregate and firm level suggesting informational effects may dominate. However, when uncertainty is measured based on the diversity of unscheduled event groups, the forecasting bounds for broad stock market returns widen, while those at the firm level narrow, implying model ambiguity dominates at the aggregate level. These findings are robust for the majority of Dow Jones Industrial Average 30 firms and for alternative categories of unscheduled events.

This study introduces a novel index based on expectations concordance for explaining stock-price volatility when novel events that are each somewhat unique cause unforeseeable change and Knightian uncertainty in the process driving outcomes. Expectations concordance measures the degree to which KU events are associated with directionally similar expectations of future returns. Narrative analytics of daily news reports allow for the assessment of bullish versus bearish views in the stock market. Increases in expectations concordance across all KU events results in reinforcing effects and an increase in stock market volatility. Lower expectations concordance produces a stabilizing effect wherein the offsetting views reduce market volatility. The empirical findings hold for ex post and ex ante measures of volatility and for OLS and GARCH estimates.

This paper provides an empirical investigation of leading models of stock price fluctuations, including those based on canonical present value and behavioral considerations. It uses the cointegrated VAR framework to test the models’ competing predictions concerning the roles of fundamentals, psychology, and structural change in driving fluctuations. We rely on a novel dataset from Bloomberg News to capture the influence of psychological factors and the broader information that market participants use contemplating stocks’ fundamental values. We find that stock prices, earnings, and interest rates are cointegrated, but only when measures of psychological factors, a broader information set, and mean shifts are included in the cointegration relation. The results provide support for the scapegoat and imperfect knowledge models of stock prices, with weak evidence in favor of bubble models.

We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns.

This paper provides empirical evidence that marketplace context matters for understanding stock price behavior. Investor sentiment, as measured by the informational tone of stock market reports from the Wall Street Journal and Bloomberg News outlets, is compared across two classification dictionaries: the Harvard General Inquirer IV-4 dictionary and the financial context-specific dictionary of Loughran and McDonald (2011). Empirical analyses find a negative relationship between measures of investor pessimism and real stock returns. However, this relationship is strongest and statistically significant only for the context-specific measures. The results suggest that investor sentiment based on contextualized information is able to explain medium- to longer-term swings in aggregate stock prices. This, in turn, implies that investor interpretation of stock market information may not unfold in mechanical ways.

This paper provides econometric evidence on the importance of psychological considerations for aggregate stock price fluctuations. To this end, a novel measure of stock market sentiment, dubbed the Net Psychology Index (NPI ), based on information contained in Bloomberg News’ end-of-the-day stock market reports, is confronted with a battery of multivariate empirical analyses. Results suggest that NPI is statistically different from popular sentiment proxies within the literature. NPI exhibits predictive power, increasing stock returns in the short-run with this impact dissipating in the medium-term. NPI does not exhibit asymmetric effects on returns for size- and momentum-related portfolios. A trading strategy based on NPI generates a statistically significant positive monthly return. Recursive out-of-sample fit analyses report a lower standard deviation of forecasting errors for NPI-based returns models versus competing accounts.

This paper provides empirical evidence suggesting that fundamentals matter for stock price fluctuations once temporal instability underpinning stock price-relations is accounted for. Specifically, this study extends the out-of sample forecasting methodology of Meese and Rogoff (1983) to the stock market after explicitly testing for parameter nonconstancy using recursive techniques. The predictive ability of a present value model based on Imperfect Knowledge Economics (IKE) is found to match that of the pure random walk benchmark at short forecasting horizons and to perform significantly better at medium to longer-run horizons based on conventional measures of predictability and direction of change statistics. In addition, the presence of a cointegrating relation is found only within regimes of statistical parameter constancy. Augmenting the MR methodology in a piecewise linear fashion yields empirical results in favor of a fundamentals-based account of stock price behavior overturning the recent results of Flood and Rose (2010).

This article argues that the field of asset pricing theory is undergoing a scientific revolution in Kuhnian terms. The orthodox view is one of determinate change in causal processes and inherent stability whereby financial markets, left unfettered, allocate nearly perfectly society’s scare capital. However, decades of mounting anomalous evidence against the implications of stable causal processes perpetuated by conventional models based on efficient markets and the rational expectations hypothesis have paved the way for alternative avenues of research. Although various approaches are being developed, the imperfect knowledge economics (IKE) class of models has emerged as a potential new paradigm in the field of macro-finance. By stopping short of fixing in advance the specification of individual forecasting behavior and the causal process, the IKE class of models has been able to reconcile many of the puzzles found within the literature on asset price behavior and risk.

Macroeconomic models that are based on either the rational expectations hypothesis (REH) or behavioral considerations share a core premise: all future market outcomes can be characterized ex ante with a single overarching probability distribution. This paper assesses the empirical relevance of this premise using a novel data set. We find that Knightian uncertainty, which cannot be reduced to a probability distribution, underpins outcomes in the stock market. This finding reveals the full implications of Robert Shiller's ground-breaking rejection of the class of REH present-value models that rely on the consumption-based specification of the risk premium. The relevance of Knightian uncertainty is inconsistent with all REH models, regardless of how they specify the market's risk premium. Our evidence is also inconsistent with bubble accounts of REH models' empirical difficulties. We consider a present-value model based on a New Rational Expectations Hypothesis, which recognizes the relevance of Knightian uncertainty in driving outcomes in real-world markets. Our novel data is supportive of the model's implications that rational forecasting relies on both fundamental and psychological factors.

This study provides evidence that stock market participants revise their forecasting strategies in response to macroeconomic news contingent on the state of the economy. This study utilizes Mangee (2011)'s novel dataset based on textual information contained in Bloomberg News's end-of-the-day stock market reports. A key finding is that macroeconomic news is reported to impact stock prices with a positive relation on some days and a negative one on others. The Bloomberg data show that, on average, economic considerations matter positively for stock prices during both expansions and contractions, but the degree to which macroeconomic news matters negatively rises dramatically during expansions. However, the Bloomberg data suggests that the connection between macroeconomic news and stock prices is much more unstable than what has been previously reported. In particular,the qualitative impact of economic considerations is not constant across expansionary periods. Furthermore, net market psychology associated with economic considerations is found to decline sharply leading up to and during sustained economic contractions. The results are consistent with theoretical accounts of asset price fluctuations which recognize that market participants imperfection of knowledge underpins the temporal indeterminacy of fundamentals-based relations.

Abstract: This paper presents a theoretical and empirical analysis of the effects of environmental regulation on bilateral trade volume. We use a gravity model of trade flows and find weak evidence that differences in regulation are a source of comparative advantage. We also find evidence against the race-to-the-bottom hypothesis in that increases in standards in both high and low standard countries increase bilateral trade volume. We use 1999 data on GDP, population, and environmental stringency for 39 countries.