Evaluation of a Numerical, Real-Time Ultrasound Imaging Model for the Prediction of Litter Size in Pregnant Sows, with Machine Learning

The present study aimed to evaluate the accuracy of a numerical model, quantifying real-time ultrasonographic (RTU) images of pregnant sows, to predict litter size. The time of the test with the least error was also considered. A number of 4165 pregnancies in Farm 1 and 438 in Farm 2 were diagnosed twice, with the quality of the RTU images translated into rated-scale values (RSV1 and RSV2). When a deep neural network (DNN) was trained, the evaluation of the method showed that the prediction of litter size can be performed with little error. Root square mean error (RMSE) for training, validation with data from Farm 1, and testing on the data from Farm 2 were 0.91, 0.97, and 1.05, respectively. Corresponding mean absolute errors (MAE) were 2.27, 2.41, and 2.58. Time appeared to be a critical factor for the accuracy of the model. The smallest MAE was achieved when the RTU was performed at days 20–22. It is concluded that a numerical, RTU imaging model is a prominent predictor of litter size, when a DNN is used. Therefore, early routinely evaluated RTU images of pregnant sows can predict litter size, with machine learning, in an automated manner and provide a useful tool for the efficient management of pregnant sows. © 2022 by the authors

EARNINGS RESPONSE COEFFICIENTS IN THE GREEK MARKET

The paper explores the relationship between accounting information and stock returns of the companies listed on the Athens Stock Exchange (ASE) in the period 1998–2008. Publicly available financial data on the companies included in the ASE during 1998–2008 have been collected and processed. The data sample consists of 245 companies and varies from 2,166 to 1,441 firm-year observations. The research methodology has been based on the extension of the model introduced by Kothari and Sloan (1992) and investigates whether the level of earnings divided by price at the beginning of the stock return period is associated with returns in the context of ‘prices lead earnings’ using annual and quarterly data. Cross-sectional regression analysis points to a significant relationship between earnings and returns on measurement windows of one year and longer. Similar results have been found in the case of a cumulative model where earnings are aggregated up to four years; however, relationship in the short measurement window up to three quarters has resulted in low earnings response coefficients

Earnings management: Origins

This chapter seeks to describe the field of inquiry by defining the concepts of earnings quality, earnings management, fraud, and earnings manipulation. It presents the earnings management phenomenon, specifically, from whence it comes. It reviews the mainstream studies, and focuses on two types of earnings management: accruals earnings management and real activities earnings management. In addition, studies related to fraudulent financial reporting (or non-generally accepted accounting principles, i.e. non-GAAP earnings management) will be presented and discussed as well. Furthermore, this chapter presents studies on managerial incentives for earnings management. The most important incentives (or causes) for managing earnings are discussed and the contradictory results provided by some of them highlighted. Finally, a few offsetting causes that may interfere with these main incentives for managing earnings are presented