On Predictability of Revisioning in Corporate Cash Flow Forecasting

Financial services within corporations usually are part of an information system on which many business functions depend. As of the importance of forecast quality for financial services, means of forecast accuracy improvement, such as data-driven statistical prediction techniques and/or forecast support systems, have been subject to IS research since decades. In this paper we consider means of forecast improvement due to regular patterns in forecast revisioning. We analyze how business forecasts are adjusted to exploit possible improvements for the accuracy of forecasts with lower lead time. The empirical part bases on an unique dataset of experts\u27 cash flow forecasts and accountants\u27 actuals realizations of companies in a global corporation. We find that direction and magnitude of the final revision in aggregated forecasts can be related to suggested targets in earnings management, providing the means of improving the accuracy of longer-term cash flow forecasts

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available