State‐Dependent Forward Guidance and the Problem of Inconsistent Announcements

Forward guidance can be provided as an unconditional promise, i.e. commitment to a specific low policy rate. Alternatively, the promise may include an escape clause, i.e. a condition defining the state of the economy under which the central bank would not keep such a low rate and, instead, it would revert to setting policy under discretion. The escape clause can be expressed as a threshold in terms of a specific variable. The present paper shows that, when such a threshold is expressed in terms of an endogenous variable (e.g. output, inflation), there are cases where it becomes impossible for the central bank to act in a way that is consistent with its promise. Consistency imposes limits on the policy rate that can be set since reverting immediately to the optimal discretionary rate can be incompatible with exceeding the threshold.     

Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies

Decisions in Economics and Finance - This paper sets out to explore the nexus between cryptocurrencies connected to cannabis production and the three highest capitalization digital currencies....     

An Ethical Inquiry of the Effect of Cockpit Automation on the Responsibilities of Airline Pilots: Dissonance or Meaningful Control?

Journal of Business Ethics - Airline pilots are attributed ultimate responsibility and final authority over their aircraft to ensure the safety and well-being of all its occupants. Yet, with the...     

A new take on voice: the influence of BlackRock’s ‘Dear CEO’ letters

Review of Accounting Studies - Abstract We examine whether broad-based public engagement by institutional investors influences the behavior of portfolio firms. We investigate this question in the...     

Shareholder Activism and Voluntary Disclosure Initiation: The Case of Political Spending

Demand for disclosures on environmental, social, and governance (ESG) issues has increased dramatically. Using corporate political spending disclosures as our setting, we conduct a detailed inquiry of 541 political spending‐related shareholder proposals from 2004 to 2012 to highlight the role of shareholder activism as a mechanism to motivate ESG disclosure. Unlike earlier studies, we examine both proposals that went to a vote and proposals that were withdrawn by the activist, allowing us to assess more comprehensively the success of shareholder activism. We find that 20 percent of firms targeted by disclosure proposals begin disclosing in the subsequent year, although implementation rates vary by proposal type—8 percent for proposals subject to a vote versus 56 percent for proposals withdrawn. The sponsor is also important: unions and public pension funds are less likely than other activists to target firms with agency problems and are less successful in having proposals withdrawn, and the implementations they obtain are viewed more negatively by the broader investor base. Our findings highlight shareholder proposals as one mechanism through which investors can successfully express their preferences for corporate disclosure policies. Given activists' long‐standing interest in environmental and social disclosure policies, we believe our findings generalize to a broader set of ESG disclosures.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Flood Risk and Salience: New Evidence from the Sunshine State

A growing literature finds evidence that flood risk salience varies over time, spiking directly following a flood and then falling off individuals' cognitive radar in the following years. In this article, we provide new evidence of salience exploiting a hurricane cluster impacting Florida that was preceded and followed by periods of unusual calm. Utilizing residential property sales across the state from 2002 through 2012, our main estimate finds a salience impact of ?8%, on average. The salience effect persists when we base estimation only on spatial variation in prices to limit confounding from other simultaneous changes due to shifting hedonic equilibria over time. These effects range from housing prices decreases of 5.4–12.3% depending on the year of sale. Understanding flood risk salience has important implications for flood insurance and disaster policy, the benefits transfer literature, and, more broadly, our understanding of natural disaster resilience. JEL Classification: Q51, Q54, R21     

Media-Based Sentiment Indices as an Alternative Measure of Consumer Confidence

In this paper, we consider the feasibility of constructing online sentiment indices, using large amounts of media data, as an alternative to the conventional survey method used to create the consumer confidence index in South Africa. A clustering framework is adopted to provide an indication of possible candidate sentiment indices constructed from a combination of different text sources and dictionaries that best mimic the traditional survey-based consumer confidence index from the South African Bureau for Economic Research (BER). The results conclude that it is possible to create an index using sentiment analysis using online editorial data that does resemble the BER’s consumer confidence index. The different media-based sentiment indices (MSI) show a significant level of correlation and co-movement with the BER’s CCI. Impulse responses and cross-correlation functions indicate that the MSI could potentially lead the survey-based method up to two quarters. Furthermore, Granger-causality tests show that the media-based indices are good predictors of future consumer confidence index values. The results provide motivation for further study on the use of sentiment-based techniques and online media data sources to track consumer confidence within an emerging market such as South Africa.