This study seeks to examine the determinants of revenue manipulation by focusing on factors that have unique implications for revenue. Based on a sample of financial restatements arising from aggressive accounting, I control for general earnings management incentives by comparing revenue restatement firms with non-revenue restatement firms. I predict and find that firms with (i) higher growth p...

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonabl...

In this paper, the problem of forecasting a time series with only a small amount of data is addressed within the Bayesian framework. The quantity to be predicted is the accumulated value of a positive and continuous variable for which some partially accumulated data has been observed. These conditions appear in a natural way in the prediction of sales of style goods and coupon redemption among ...

– We study the effect of decomposing a series into multiple components and performing forecasts on each component separately. The focus here is on sales data-most of the series considered display both seasonality and trend. Hence the original series is decomposed into trend, seasonality and an irregular component. Multiple forecasting 'experts' are used to forecast each component series. These ...

In this paper we develop a novel methodology to incorporate managerial judgment or intuition into a logit model. The major contribution and focus of this research is on the way prior information is incorporated in our model. By extending the model introduced by Koop and Poirier (1993) we are able incorporate information about exogenous variables as priors in our model. The major advantage of th...

Data analysts are increasingly important for companies to extract critical information from their vast amount of data in order to be competitive. Data analytics specialists or data scientists develop statistical models and make use of dedicated software components for example to categorize products and forecast future sales. Their unique skill set is among the most sought after in the current j...

This paper proposes the use of artificial neural networks (feed forward multi-layer perceptron and Elman recurrent networks) in forecasting sales trends at retail by analyzing industry and manufacturer specific metrics along with national economic indicators. Relevant data drivers were gathered based on consultations with the manufacturer as well as experts in the fields of economics and financ...

We propose a new and simple methodology to estimate the loss function associated with experts’ forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin-lin and linex loss function can easily be estimated using a linear regression. This regression also provides an estimate for potential systematic bias in the forecasts...

Traditionally, the quality of a forecasting model is judged by how it compares, in terms of accuracy, to alternative models. However, by providing a relative measure, no indication is given as to how much scope there might be for improvements beyond the benchmark model. When judgemental methods are used alongside simple forecasting models, the scope for such improvements is considerable and dif...