Using a sample from 22 countries, I investigate the relations between the accuracy of analysts' earnings forecasts and the level of annual report disclosure; and between forecast accuracy and the degree of enforcement of accounting standards. I document that firm-level disclosures are positively...

Using a large sample of business groups from more than one hundred countries around the world, we show that group information matters for parent and subsidiary default prediction. Group firms may support each other when in financial distress. Potential group support represents an off-balance...

Over the past 12 years, financial analysts across the world have been optimistically wrong with their 12-month earnings forecasts by 25.3%. This study may be the first of its kind to assess analyst earnings forecast accuracy at all listed companies across the globe, covering 70 countries. A...

This paper revisits two valuation models based on accounting figures: the Residual Income Valuation (RIV) and Abnormal Earnings Growth (AEG). Our research design has two approaches: i) we demonstrate theoretical integration of both models; and ii) we show in a practical manner that models...

Using smooth transition regression model analysis, we examine the non-linear predictability of Japanese and US stock market returns by a set of macroeconomic variables between 1981 and 2012. The theoretical basis for investigating non-linear behavior in stock returns can be based on the...

Speakers of weak future-time reference (FTR) languages perceive the future as closer and more imminent. In this study, we examine the important question of whether the FTR properties of languages spoken by investors affect their demand for forward-looking information, thereby influencing...

Using global cross-firm ownership data, we find that both stock returns and cash-flow news of ownership-linked firms predict focal firm’s returns for all four types of ownership structures: subsidiary−parent, parent−subsidiary, subsidiary−subsidiary, and parent−parent. These results...

We demonstrate the value of machine learning in accounting through a detailed examination of litigation risk, an important and frequently used estimate in the literature. We evaluate a comprehensive set of twelve machine learning techniques and benchmark their performance against the logistic...

This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The mapped data set permits us to...

In this study, we investigate how product market competition affects the extent of analyst following and the properties of analyst forecasts. Using a broad sample of firms from 37 countries over the 1990 to 2008 period, we find that firms that operate in more concentrated industries and with...