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Financial Markets and Portfolio Management

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29.01.2020

The stock market’s reaction to macroeconomic news under ambiguity

verfasst von: Ariel M. Viale, Antoine Giannetti, Luis Garcia-Feijoó

Erschienen in: Financial Markets and Portfolio Management | Ausgabe 1/2020

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Abstract

We investigate the quality of the information that macroeconomic news conveys to the stock market about future business conditions. Our econometric approach is consistent with the decision problem of an investor concerned with ambiguity, which allows us to recover a theoretically motivated and empirically tractable proxy of time-varying ambiguity in the stock market. We find the stock market reacts more strongly to negative rather than positive real and monetary macroeconomic news, which is consistent with the predictions of the ambiguity literature. Further, the indirect effect of ambiguous news on investors’ loss of confidence in the signal can contribute up to 80% of the stock market’s reaction. Our findings offer a potential explanation for the weak results of the prior early literature using low-frequency data; they also offer an alternative explanation for the apparent counterintuitive results of the more recent literature using high-frequency data.

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Schwert (1981), Chen et al. (1986), Ederington and Lee (1993), and Adams et al. (2004) study the impact of news from monetary variables. Pearce and Roley (1985) and Orphanides (1992) focus on news from real activity variables, specifically the unemployment rate.

McQueen and Roley (1993), Flannery and Protopapadakis (2002), Boyd et al. (2005), Boyd et al. (2006), Bernanke and Kuttner (2005), Andersen et al. (2007), Bestelmeyer and Hess (2010), and Cenesizoglu (2011).

It is well known that expected returns are subject to more imprecision than volatilities.

Jurado et al. (2015) discuss the drawbacks of using disagreement in survey forecasts as a proxy for uncertainty. First, subjective expectations are only available for a limited number of macroeconomic variables. Second, responses from surveys of professional forecasters are known to show systematic biases and omissions. Third, rather than uncertainty, disagreement in survey forecasts may be reflecting just differences of opinion (e.g., see Diether et al. 2002; Mankiw et al. 2004). Furthermore, Lahiri and Sheng (2010) provide empirical evidence that when using surveys of professional forecasts, the variance of aggregate accumulated shocks can drive a large wedge between uncertainty and disagreement in times of important economic changes (see also, Bachman et al. 2013; Scotti 2016).

From the perspective of a macroeconomist, the steady state corresponds to the economy growing close to its long-run potential under adequate monetary conditions. Under ambiguity, a recession or large deviation from the steady state can arise from a sudden loss of confidence independently of fundamentals.

Signals are assumed to be serially independent and independent of dividends before \( \tau \).

For dynamic consistency (i.e., consumption plans made for decisions at subsequent dates remain optimal in the future), it is critical that one-step-ahead conditional probabilities be considered. Epstein and Schneider (2003, pp. 16–17) discuss the technical requirements needed to ensure dynamic consistency.

The KL divergence arises in statistics as the expected logarithm of the likelihood ratio between two distributions (see Cover and Thomas (1991)). It is not a true distance as it is not symmetric and does not satisfy the triangular inequality. The reason for the constraint is to avoid infinite pessimism (Hansen and Sargent 2008).

The investor chooses without regret the optimal allocation, and asset returns follow a memoryless mechanism defined on a compact state space (e.g., a Markov chain with some transition probability function across states). The Dynkin space that captures the notion of risk is the one where the set of priors collapses to a singleton (the reference model). The Dynkin space that captures the notion of ambiguity is the one with the largest possible set of priors or distorted models.

If investors perceive dividends also as ambiguous, the model is observationally equivalent to the regime-switching model of Veronesi (1999), but premia in stock returns will be related to idiosyncratic effects in fundamentals, which are independent of the state of the economy (Epstein and Schneider 2008).

Grünwald and Dawid’s (2004) article motivates Cerreia-Vioglio et al. (2013)’s dual result.

GME is a special case of GCE when the prior is the uniform distribution.

The GME/GCE approach can be extended to the case where the estimation functions are based on weakly dependent observations. In other words, ME can be applied to regression problems where the data have a convergent autocorrelation structure (Kitamura and Stutzer 1997).

Higher moment restrictions can be added to the analysis if the econometrician knows these moments.

GME-IV converges asymptotically to the MLE of a logistic regression with transition probabilities that follow a stationary Markov process (Mittelhammer et al. 2000).

The stock market data is available at: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. The FRED^® macroeconomic data is available at: https://fred.stlouisfed.org. The ALFRED® database is available at: https://alfred.stlouisfed.org.

The PPIFGS series was discontinued in December 2015. We subsequently chain it with the producer price index for all commodities (PPIACO).

The choice of the lag order is motivated by Campbell and Shiller (1988), who show that any high order VAR can be collapsed to its first-order (companion) VAR.

We obtain the news from Kenneth Kuttner’s website: https://econ.williams.edu/faculty-pages/research/.

We choose a subset of variables from a total of 26 macroeconomic variables for parsimony. We exclude macroeconomic variables with less frequent announcements (e.g., quarterly), overlapping announcements, and/or limited interest from financial reporters.

We use Pagan and Sossounov’s (2003) algorithm to identify turning points in the stock market. The algorithm relies on the martingale model for stock market returns and classifies bull and bear markets from observed local peaks and troughs using an event window of 8 months. The authors argue that their results are similar to those from more elaborated econometric estimation methods, such as Hamilton’s regime switching (RSM), GARCH, and/or structural models.

As explained by Cenesizoglu (2011), this specification takes into account the possibility of several announcements on the same day and controls for other possible effects of announcement days not captured by the news.

Results are similar for the different sets of priors shown in Fig. 2. Thus, to save space, we report results based on Gaussian priors.

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Titel: The stock market’s reaction to macroeconomic news under ambiguity
verfasst von: Ariel M. Viale
Antoine Giannetti
Luis Garcia-Feijoó
Publikationsdatum: 29.01.2020
Verlag: Springer US
Erschienen in: Financial Markets and Portfolio Management / Ausgabe 1/2020
Print ISSN: 1934-4554
Elektronische ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-019-00342-3

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