A Theory of Income Smoothing When Insiders Know More Than Outsiders

We consider a setting in which insiders have information about income that outside shareholders do not, but property rights ensure that outside shareholders can enforce a fair payout. To avoid intervention, insiders report income consistent with outsiders' expectations based on publicly available information rather than true income, resulting in an observed income and payout process that adjust partially and over time towards a target. Insiders under-invest in production and effort so as not to unduly raise outsiders' expectations about future income, a problem that is more severe the smaller is the inside ownership and results in an "outside equity Laffer curve". A disclosure environment with adequate quality of independent auditing mitigates the problem, implying that accounting quality can enhance investments, size of public stock markets and economic growth.

Stability of constant retrial rate systems with NBU input*

We study the stability of a single-server retrial queueing system with constant retrial rate, general input and service processes. First, we present a review of some relevant recent results related to the stability criteria of similar systems. Sufficient stability conditions were obtained by Avrachenkov and Morozov (2014), which hold for a rather general retrial system. However, only in the case of Poisson input is an explicit expression provided; otherwise one has to rely on simulation. On the other hand, the stability criteria derived by Lillo (1996) can be easily computed but only hold for the case of exponential service times. We present new sufficient stability conditions, which are less tight than the ones obtained by Avrachenkov and Morozov (2010), but have an analytical expression under rather general assumptions. A key assumption is that interarrival times belongs to the class of new better than used (NBU) distributions. We illustrate the accuracy of the condition based on this assumption (in comparison with known conditions when possible) for a number of non-exponential distributions

Band gap prediction for large organic crystal structures with machine learning

Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline materials. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state-of-the-art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.Comment: 10 pages, 6 figure

THE IMPACTS OF THE CONSERVATION RESERVE PROGRAM ON RURAL COMMUNITIES: THE CASE OF THREE OREGON COUNTIES

Using an economic input/output model, the community personal income impacts of participating in the Conservation Reserve Program were analyzed for three rural Oregon counties. While individual farmers may benefit from participation, there may be net adverse impact on the community if the retired land is relatively productive or if the inputs that are no longer purchased would have been purchased locally. These negative effects may be exacerbated if participating farmers quit farming and leave the local area or if the Conservation Reserve Program benefits go to absentee landowners. The Conservation Reserve Program may then represent a conflict between community and national policy objectives.Agricultural and Food Policy, Community/Rural/Urban Development,

Online Search Tool for Graphical Patterns in Electronic Band Structures

We present an online graphical pattern search tool for electronic band structure data contained within the Organic Materials Database (OMDB) available at https://omdb.diracmaterials.org/search/pattern. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput ab initio calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The tool can be used to find realizations of functional materials characterized by a specific pattern in their electronic structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a "Mexican hat" pattern or an effectively free electron gas, characterized by a parabolic dispersion. The source code of the developed tool is freely available at https://github.com/OrganicMaterialsDatabase/EBS-search and can be transferred to any other electronic band structure database. The approach allows for an automatic online analysis of a large collection of band structures where the amount of data makes its manual inspection impracticable.Comment: 8 pages, 8 figure

Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;

Mass fluctuations and absorption rates in Dirac materials sensors

We study the mass fluctuations in gapped Dirac materials by treating the mass-term as both a continuous and discrete random variable. Gapped Dirac materials were proposed to be used as materials for Dark matter sensors. One thus would need to estimate the role of disorder and fluctuations on the interband absorption of dark matter. We find that both continuous and discrete fluctuations across the sample introduce tails (e.g. Lifshitz tails) in the density of states and the interband absorption rate. We estimate the strength of the gap filling and discuss implications of these fluctuations on the performance as sensors for Dark matter detection. The approach used in this work provides a basic framework to model the disorder by any arbitrary mechanism on the interband absorption of Dirac material sensors.Comment: 7 pages, 5 figure