Using Neural Net Technology to Analyze Corporate Restructuring Announcements

It is a rare day when the Wall Street Journal does not include an announcement that a company is taking a restructuring charge. Nowadays it is often assumed that this charge is being taken for the purpose of managing earnings. The problems associated with earnings management are not limited to Wall Street but can be found throughout the world\u27s financial markets. Ongoing developments in artificial intelligence technology hold considerable promise for helping monitor and detect financial fraud and abuse. The objective of this paper is twofold: first, to illustrate how neural nets, a branch of artificial intelligence, can be used to analyze the impact of corporate restructuring announcements on stock performance and second, to propose the need for a balanced approach using both tighter accounting standards and ex-post analysis for better control of excessive earnings management practices

BAYESIAN HERDERS: ASYMMETRIC UPDATING OF RAINFALL BELIEFS IN RESPONSE TO EXTERNAL FORECASTS

Temporal climate risk weighs heavily on many of the world's poor. Recent advances in model-based climate forecasting have expanded the range, timeliness and accuracy of forecasts available to decision-makers whose welfare depends on stochastic climate outcomes. There has consequently been considerable recent investment in improved climate forecasting for the developing world. Yet, in cultures that have long used indigenous climate forecasting methods, forecasts generated and disseminated by outsiders using unfamiliar methods may not readily gain the acceptance necessary to induce behavioral change. The value of model-based climate forecasts depends critically on the premise that forecast recipients actually use external forecast information to update their rainfall expectations. We test this premise using unique survey data from pastoralists and agropastoralists in southern Ethiopia and northern Kenya, specifying and estimating a model of herders updating seasonal rainfall beliefs. We find that those who receive and believe model-based seasonal climate forecasts indeed update their priors in the direction of the forecast received, assimilating optimistic forecasts more readily than pessimistic forecasts.Agribusiness, O1, D1, Q12,

Bayesian Herders: Optimistic Updating of Rainfall Beliefs In Response To External Forecasts

Temporal climate risk weighs heavily on many of the world\u27s poor. Model based climate forecasts could benefit such populations, provided recipients use forecast information to update climate expectations. We test whether pastoralists in southern Ethiopia and northern Kenya update their expectations in response to forecast information and find that they indeed do, albeit with a systematic bias towards optimism. In their systematic optimism, these pastoralists are remarkably like Wall Street\u27s financial analysts and stockbrokers. If climate forecasts have limited value to these pastoralists, it is due to the flexibility of their livelihood rather than an inability to process forecast information

Bayesian Herders: Asymmetric Updating of Rainfall Beliefs in Response to External Forecasts

Temporal climate risk weighs heavily on many of the world\u27s poor. Model-based climate forecasts could benefit such populations, provided recipients use forecast information to update climate expectations. We test whether pastoralists in southern Ethiopia and northern Kenya update their expectations in response to forecast information and find that they indeed do, albeit with a systematic bias towards optimism. In their systematic optimism, these pastoralists are remarkably like Wall Street\u27s financial analysts and stockbrokers. If climate forecasts have limited value to these pastoralists, it is due to the flexibility of their livelihood rather than an inability to process forecast information

Dynamic Field Experiments in Development Economics: Risk Valuation in Morocco, Kenya, and Peru

The effective design and implementation of interventions that reduce vulnerability and poverty require a solid understanding of underlying poverty dynamics and associated behavioral responses. Stochastic and dynamic benefit streams can make it difficult for the poor to learn the value of such interventions to them. We explore how dynamic field experiments can help (i) intended beneficiaries to learn and understand these complicated benefit streams, and (ii) researchers to better understand how the poor respond to risk when faced with nonlinear welfare dynamics. We discuss and analyze dynamic risk valuation experiments in Morocco, Peru, and Kenya.poverty, risk and uncertainty, dynamics, experiments, Kenya, Morocco, Peru, International Development, Research Methods/ Statistical Methods, Risk and Uncertainty,

The Vehicle, February 1960, Vol. 2 no. 1

Vol. 2, No. 1 Table of Contents Editorialpage 2 A White Man\u27s BurdenRobert Mills Frenchpage 4 Passing TrainWayne Bakerpage 8 AutumnMajor Dan Ragainpage 8 Chaos in CulturevilleJ.B. Youngpage 9 Cure-allJerry N. Whitepage 13 Love-Long DistanceMary Ellen Mockbeepage 13 Metropolitan CaravanThomas McPeakpage 14 Ode to the Lion HuntersRichard Blairpage 16 ImmortalityM.E.M.page 16 EntranceSam Martinpage 16https://thekeep.eiu.edu/vehicle/1006/thumbnail.jp

The Vehicle, February 1960, Vol. 2 no. 1

Vol. 2, No. 1 Table of Contents Editorialpage 2 A White Man\u27s BurdenRobert Mills Frenchpage 4 Passing TrainWayne Bakerpage 8 AutumnMajor Dan Ragainpage 8 Chaos in CulturevilleJ.B. Youngpage 9 Cure-allJerry N. Whitepage 13 Love-Long DistanceMary Ellen Mockbeepage 13 Metropolitan CaravanThomas McPeakpage 14 Ode to the Lion HuntersRichard Blairpage 16 ImmortalityM.E.M.page 16 EntranceSam Martinpage 16https://thekeep.eiu.edu/vehicle/1006/thumbnail.jp

The Impact of Business Intelligence Systems on Profitability and Risks of Firms

202105 bcvcAccepted ManuscriptRGC155009/15BEarly releas