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.     

The Effect of Media Coverage on the Pricing of Seasoned Equity Offerings

This paper examines the role of media coverage and investor attention on the outcomes of seasoned equity offerings (SEOs). I use an archive of Thomson Reuters news articles to proxy for firm visibility and investor attention. I find that the volumes of news articles prior to the offerings are positively associated with the offer price discounts of SEOs. Furthermore, the volumes of news articles are negatively associated with the cumulative abnormal returns three days around the SEOs. I conclude that the costs of equity increase with media coverage prior to SEOs. Overall, the evidence is consistent with the hypothesis that media coverage affects investors' information processing in SEOs. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

A conceptual model and research agenda for bidding,planning and delivering Major sport events that lever human rights

In this conceptual article, the authors propose a framework for how progressive human rights outcomes may be obtained in the context of bidding, planning and implementing major sport events (MSEs) through the implementation of four pathways, including good governance, the democratic participation of stakeholders, the formalisation of human rights agendas and the deployment of sensitive urban development. The authors argue that there is a need for adherence to internationally recognised standards, such as the Universal Declaration of Human Rights and the United Nations Guiding Principles on Business and Human Rights if rhetorical statements from MSE awarding bodies and host organisers are to be accountable to a wider set of actors. If researchers and practitioners want to address some of the critical issues related to human rights and MSEs, it is imperative that key actors working in the rights sphere are involved in shaping the research agenda and monitoring its implementation. Academics need to take a proactive approach aimed at achieving both theoretically grounded and practically relevant solutions, with engagement occurring over an extended period of time. This approach, avowedly political and concerned with genuine action, is a key way in which MSE stakeholders can be held to account for their actions in regard to human rights.     

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.     

Auditors,Specialists, and Professional Jurisdiction in Audits of Fair Values

Auditors frequently use valuation specialists to help them evaluate fair values, but researchers and regulators know little about how auditors use these specialists. Based on interviews with 28 auditors and 14 valuation specialists, I develop a theoretical framework informed by expert systems and professional competition theories. The interviews suggest that institutional pressures in the fair value environment unevenly impact auditors and specialists, causing tension between auditors' needs for ontological security and jurisdictional claims. This tension leads to one-sided competition between auditors and specialists and incomplete acceptance of specialists' work. Auditors' competitive behaviors coupled with this incomplete acceptance result in a tendency to make specialists' work conform to auditors' views. Collectively, these findings suggest that auditors use specialists as an institutional mechanism to create comfort, but not insight. This study links expert systems and professional competition theories, and it provides critical insight into some assumptions underlying tenets of each theory. It also informs researchers, regulators, and practitioners interested in understanding and addressing problems related to the use of specialists.     

How much are electric vehicles driven?

The prospect for electric vehicles as a climate change solution hinges on their ability to reduce gasoline consumption. But this depends on how many miles electric vehicles are driven and on how many miles would have otherwise been driven in gasoline-powered vehicles. Using newly-available U.S. nationally representative data, this paper finds that electric vehicles are driven considerably fewer miles per year on average than gasoline-powered vehicles. The difference is highly statistically significant and holds for both all-electric and plug-in hybrid vehicles, for both single- and multiple-vehicle households, and both inside and outside California. The paper discusses potential explanations and policy implications. Overall, the evidence suggests that today’s electric vehicles imply smaller environmental benefits than previously believed.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

INSURANCE‐INDUCED MORAL HAZARD: A DYNAMIC MODEL OF WITHIN‐YEAR MEDICAL CARE DECISION MAKING UNDER UNCERTAINTY

This study quantifies the moral hazard effect of health insurance on medical expenditure by estimating a dynamic model of within‐year medical care consumption that allows for insurance selection, endogenous health transitions, and individual uncertainty about medical care prices in an environment where insurance has nonlinear cost‐sharing features. The results suggest that moral hazard accounts for 53.1%, on average, of total annual medical expenditure when insured. This estimate is significantly different, and generally larger, than that produced by an alternative model that is representative of the annual medical care decision‐making models commonly found in the literature.