Exploiting the heteroskedasticity of the changes in short-term and long-term interest rates and exchange rates around the FOMC announcement, we identify three structural monetary policy shocks. We eliminate the predictable part of the shocks and study their effects on financial variables and macro variables. The first shock resembles a conventional monetary policy shock, and the second resembles an unconventional monetary shock. The third shock leads to an increase in interest rates, stock prices, industrial production, consumer prices, and commodity prices. At the same time, the excess bond premium and uncertainty decrease, and the U.S. dollar depreciates. Therefore, this third shock combines all the characteristics of a central bank information shock.

We examine whether combining factual information on inflation levels and forecasts with a narrative can persistently shape consumers’ inflation expectations. In a preregistered randomized controlled trial with a representative sample of 3,000 German consumers, participants received either numerical or textual information about inflation rates, with or without an accompanying narrative. All treatments immediately lower inflation expectations, with numerical information eliciting stronger adjustments. Adding a narrative produces no additional immediate effect, confirming that it conveys no new information. However, only the combination of numerical information with a narrative yields a lasting reduction in inflation expectations and forecast uncertainty still observable after four weeks. Our results suggest that combining precise information with a narrative enhances information retention and can lead to more persistent shifts in consumers’ beliefs. The effects are strongest when respondents perceive the narrative as relatable and emotionally engaging, and among those with low financial literacy and limited knowledge of inflation.

This study examines the institutional demand for mispriced stocks with incongruent expectations implied by the book-to-market (BM) ratio and financial strength. Institutional trading (or institutional demand) is calculated by both changes in institutional ownership (percentage of shares held) and the number of institutional investors from the previous to the current quarter. Market mispricing and expectation errors in value/glamour stocks can be identified by analysing firms’ recent financial strength (measured by FSCORE). Firms are sorted into value stocks (top 30%), middle stocks (between 30% and 70%) and glamour stocks (bottom 30%) by distribution of BM ratios at the end of the previous fiscal year. Firms in the sample are then double sorted by FSCORE and BM: in each BM portfolio, firms are further classified into high-, mid- and low-FSCORE groups. Consistent with the argument of expectation errors in value/glamour stocks (Piotroski and So, 2012), institutional investors buy value stocks with strong fundamentals (underpriced) and sell glamour stocks with weak fundamentals (overpriced). Independent institutions are more likely to take advantage of the mispricing in value/glamour firms than passive institutions. Institutional trading on expectation errors could reduce the abnormal returns to mispriced stocks. Institutional trading patterns on mispriced value/glamour stocks are also documented in global markets. Our research provides new evidence that the institutional investors do exploit the BM anomalies if the mispricing can be identified by both the BM and the recent financial strength. Our study differs from Caglayan, Celiker and Sonaer (2018) as we emphasise that financial institutions, in addition to relying on only the BM values, process information from financial statements to infer firms’ financial strength. This study is also the first to document that institutional demand on mispricing could attenuate the BM anomaly.

This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act‘s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.