Stock market reactions to monetary policy surprises under uncertainty

This article investigates how uncertainty impacts the effect of monetary policy surprises on stock returns. Using high-frequency US data, we demonstrate that stock markets respond more aggressively to monetary policy surprises during periods of high uncertainty. We also show that uncertainty asymmetrically influences the transmission of positive and negative monetary policy surprises to stock market prices. The amplifying effect of uncertainty is found to be stronger for expansionary shocks than for contractionary shocks. Our robustness analysis confirms that financial uncertainty has a significant role in shaping the influence of monetary policy on the stock market.     

The Importance of Tax Adjustments When Evaluating Wage Expectations*

Using elicited expectations of future gross salaries, we evaluate characteristics causing German students to make larger or smaller estimation errors. While students seem to underestimate actual salaries by 18 percent, we show that these errors are highly attributable to misconceptions of the progressive income tax. Developing a suitable adjustment procedure, we correct students’ estimates and find that errors decline by 12 percentage points. Conducting regression analyses, we reveal strong connections with students’ age, gender, work experience, secondary school track, and knowledge about student loans. These results change notably if not controlling for students’ misconceptions of the tax system.     

Information in stock prices: the case of the 2016 U.S. presidential election

On the day before the 2016 U.S. presidential election, the odds of Hillary Clinton winning the presidency, according to political prediction markets, were above 90%. Surprisingly, Donald Trump won the Electoral College handily. In this study, we examine how movements in specific stock prices foreshadowed the eventual outcome. Specifically, we conduct a series of standard event-study tests focused on pharmaceutical companies, which became a focal point during the presidential campaign. Results show that while stocks of pharmaceutical companies significantly underperformed the market prior to the election, prices substantially increased beginning three days before the election outcome. This increase is both statistically significant and economically meaningful and robust to various event-study methodologies. These results suggest that some sectors of the stock market seemed to anticipate the election outcome.     

HOW DO RATING AGENCIES’ DECISIONS IMPACT STOCK MARKETS? A META‐ANALYSIS

The purpose of this study is to examine how credit rating agencies’ decisions impact the stock market using a systematic and quantitative review of existing empirical studies. Specifically, we employ a meta‐regression analysis (MRA) to investigate the extent and nature of the effect of rating agencies’ decisions on the stock market. We survey 62 studies published between 1978 and 2015. Our first finding is that the cumulative average abnormal returns calculated from this empirical literature are affected by publication bias. After controlling for publication bias, the main findings of our meta‐analysis indicate that negative rating decisions cause statistically significant negative abnormal returns. This evidence suggests an informational effect. Our results also indicate that positive rating decisions do not have a significant effect. Finally, the MRA results reveal the importance of several factors related to primary study design, as well as to the nature of the data.     

A new approach to measuring riskiness in the equity market: Implications for the risk premium

We introduce a new approach to measuring riskiness in the equity market. We propose option implied and physical measures of riskiness and investigate their performance in predicting future market returns. The predictive regressions indicate a positive and significant relation between time-varying riskiness and expected market returns. The significantly positive link between aggregate riskiness and market risk premium remains intact after controlling for the S&P 500 index option implied volatility (VIX), aggregate idiosyncratic volatility, and a large set of macroeconomic variables. We also provide alternative explanations for the positive relation by showing that aggregate riskiness is higher during economic downturns characterized by high aggregate risk aversion and high expected returns.     

Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets

Firm size is known to be an important factor affecting stock returns. This study proposes a panel threshold cointegration model to investigate the impact of the size effect on stock returns for the panel of G7 countries: Canada, France, Germany, Italy, Japan, the U.K., and the U.S. over the period 1991:1–2012:12. The empirical analysis is based upon the nonlinear cointegration framework using the asymmetric ARDL cointegration methodology (Shin et al., 2011). This methodological approach permits a much richer degree of flexibility in the dynamic adjustment process toward equilibrium, than in the classical linear model. Our findings indicate the presence of asymmetric adjustment around a unique long-run equilibrium. In particular, the empirical analysis provides evidence of asymmetric effects between stock returns and the size effect, while controlling for the book-to-market ratio and the price-to-earnings ratio.     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.     

The effect of oil price shocks on asset markets: Evidence from oil inventory news

We quantify the reaction of U.S. equity, bond futures, and exchange rate returns to oil price shocks driven by oil inventory news. Across most sectors, equity prices decrease in response to higher oil prices before the 2007/2008 crisis but increase after it. Positive oil price shocks cause a depreciation of the U.S. dollar against a broad range of currencies but have only a modest effect on bond futures returns. The evidence suggests that changes in risk premia help to explain the time-varying effect of oil price shocks on U.S. equity returns.     

Firm profitability and expected stock returns: Evidence from Latin America

Despite their higher valuation ratios, larger size, and higher investment needs, profitable firms outperform, in both raw and risk-adjusted returns, unprofitable firms in Latin America. The positive effect of firm profitability on stock returns is pervasive in univariate and bivariate sorts, panel regressions, across sub-regional markets, and among small and large stocks. A five-factor model that includes market, size, distress, profitability, and investment factors prices profitability portfolios better than other popular factor models. Five-factor alphas of profitability portfolios tend to be lower and less statistically significant, both individually and collectively, than alphas from other three widely-used pricing models.     

Does Investor Sentiment Affect the Value Relevance of Accounting Information?

We investigate whether investor sentiment affects the relationships between accounting variables and contemporaneous stock returns. Using price-relevant accounting variables identified by Chen and Zhang (2007) and the investor sentiment index constructed by Baker and Wurgler (2006), we find that the value relevance of accounting variables is collectively lower in high sentiment periods than in low sentiment periods. More importantly, earnings yield appears to be more related to contemporaneous stock returns in high sentiment periods, while other accounting variables are more related to stock returns in low sentiment periods. The effect of investor sentiment on the value relevance of accounting information is stronger for firms that are more difficult to value and to arbitrage.