Lost in Translation? Analysts' Forecasts of Cross-Listed Firms

ABSTRACT The level of difficulty for U.S. analysts in the native language of a cross-listed firm increases their forecast errors. The association is decreased by analyst experience and analyst fluency in the language of the cross-listed firms. The association is also decreased for countries using IFRS and those with higher financial reporting quality. Investors react more strongly to forecasts for firms that present greater language difficulty to analysts. Overall, our findings suggest that the language difficulty of cross-listed firms is associated with a poor information environment and capital-market-related costs.

A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification

The concept of equifinality is currently one of the largest issues in taphonomy, frequently leading analysts to erroneously interpret the formation and functionality of archaeological and paleontological sites. An example of this equifinality can be found in the differentiation between anthropic cut marks and other traces on bone produced by natural agents, such as that of sedimentary abrasion and trampling. These issues are a key component in the understanding of early human evolution, yet frequently rely on qualitative features for their identification. Unfortunately, qualitative data is commonly susceptible to subjectivity, producing insecurity in research through analyst experience. The present study intends to confront these issues through a hybrid methodological approach. Here, we combine Geometric Morphometric data, 3D digital microscopy, and Deep Learning Neural Networks to provide a means of empirically classifying taphonomic traces on bone. Results obtained are able to reach over 95% classification, providing a possible means of overcoming taphonomic equifinality in the archaeological and paleontological register.

The Boss Knows Best: Directors of Research and Subordinate Analysts

Research departments are managed by directors of research (DORs). Subordinate analysts working for higher-quality DORs provide superior earnings forecasts that elicit stronger market reactions, provide better investment recommendations, and have better career outcomes. For the broker, higher-quality DORs drive more trading commissions. Economically, analysts benefit the most from DOR–analyst industry alignment resulting from DORs’ former analyst experience. We provide several tests to mitigate endogeneity concerns and explore various mechanisms to explain these results. Overall, our article identifies a unique channel whereby the industry-specific and general human capital of top management filters through to individual subordinates and consequently improves organizational performance.

Are Experienced Analysts More Accurate?

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">We examine the relationship between analyst experience and the accuracy of annual earnings forecasts using a 20-year sample (1983-2002) from the Thomson Financial First Call I/B/E/S database.<span style="mso-spacerun: yes;">&nbsp; </span>We test for this relationship using three different measures of forecast accuracy employed by prior researchers, which are regressed against measures of general experience and specific experience, along with five other controls, for four independent 5-year subperiods, as well as for the full 20-year period.<span style="mso-spacerun: yes;">&nbsp; </span>We find that general experience levels are positively associated with forecast accuracy (negatively associated with forecast error) in most subperiods for two of the three measures of forecast accuracy.<span style="mso-spacerun: yes;">&nbsp; </span>We also find, in contrast with the extant literature, that for two of the three measures of forecast accuracy and for most subperiods, specific experience does not have an association with forecast accuracy beyond that provided by the general experience measure. <span style="mso-spacerun: yes;">&nbsp;</span>Our results suggest that the relationship between forecast accuracy and analyst experience (as well as some other commonly examined analyst characteristics) is dependent on the measure of accuracy employed and the time period studied. </span></span></p>