The growth companies puzzle: can growth opportunities measures predict firm growth?

While numerous empirical studies include proxies for growth opportunities in their analyses, there is limited evidence as to the validity of the various growth proxies used. Based on a sample of 1942 firm-years for listed UK companies over the 1990-2004 period, we assess the performance of eight growth opportunities measures. Our results show that while all the growth measures show some ability to predict growth in company sales, total assets, or equity, there are substantial differences between the various models. In particular, Tobin's Q performs poorly while dividend-based measures generally perform best. However, none of the measures has any success in predicting earnings per share growth, even when controlling for mean reversion and other time-series patterns in earnings. We term this the 'growth companies puzzle'. Growth companies do grow, but they do not grow in the key dimension (earnings) theory predicts. Whether the failure of 'growth companies' to deliver superior earnings growth is attributable to increased competition, poor investments, or behavioural biases, it is still a puzzle why growth companies on average fail to deliver superior earnings growth

Earnings Management: The Effect of Ex Ante Earnings Expectations

Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Banks' risk assessment of Swedish SMEs

Building on the literatures on asymmetric information and risk taking, this paper applies conjoint experiments to investigate lending officers' probabilities of supporting credit to established or existing SMEs. Using a sample of 114 Swedish lending officers, we test hypotheses concerning how information on the borrower's ability to repay the loan; alignment of risk preferences; and risk sharing affect their willingness to grant credit. Results suggest that features that reduce the risk to the bank and shift the risk to the borrower have the largest impact. The paper highlights the interaction between factors that influence the credit decision. Implications for SMEs, banks and research are discussed

Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information

Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, to microstructure, and to ground surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize ground surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than 20 % and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally.</p

Modeling Denitrification : Can We Report What We Don't Know?

Funding Information: This study is the products of a workshop funded by the Deutsche Forschungsgemeinschaft through the research unit DFG‐FOR 2337: Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales (DASIM), and by the German Federal Ministry of Education and Research (BMBF) under the “Make our Planet Great Again—German Research Initiative”, Grant 306060, implemented by the German Academic Exchange Service (DAAD). This work was supported by the European Union's Horizon 2020 research and innovation programme project VERIFY (grant agreement no. 776810). We would like to thank the contribution of all workshop participants of the II. DASIM Modeler Workshop. Publisher Copyright: © 2023. The Authors.Peer reviewedPublisher PD

Religiosity and corporate financial reporting: evidence from a European country

Using a sample of Portuguese privately-held firms, I examine the association between religiosity and financial reporting quality. The results suggest that firms headquartered in Portuguese areas with strong religious adherence and in the core area of the Portuguese religious cult (the district where the Fátima Sanctuary is located) generally experience lower incidence of earnings management. I provide further evidence that the results are robust to alternative measures of religiosity, and that are not driven by firms headquartered in rural areas. I also conclude that religious social norms, together with other forms of external financial monitoring, represent a mechanism for reducing costly agency conflicts. While the religious practice declined in the last decades in Portugal, I provide evidence that, even in a such context, religiosity is associated with reduced acceptance of unethical business practices, in particular, with reduced acceptance of aggressive accounting practices.I thank participants of the Second Paris Financial Management Conference (PFMC, 2014) and the 3RD Workshop on Business Ethics (EIASM, 2015) for their helpful insights.info:eu-repo/semantics/publishedVersio