Analysts’ annual earnings forecasts and changes to the I/B/E/S database

Review of Accounting Studies - I/B/E/S is a common source of analyst earnings forecast data, and the reliability of these data is important for practice and academic research. Examining a common...     

Disaggregating operating and financial activities: implications for forecasts of profitability

Researchers, practitioners, and standard setters emphasize the importance of disaggregating financial statements into operating and financial activities. However, there is a lack of research demonstrating that this disaggregation improves forecasts of profitability. In this study, we consider whether and when the operating/financial disaggregation improves forecasts of profitability. Contrary to the use of an aggregate forecasting approach by most related prior research, we first show that the operating/financial disaggregation only provides forecast improvement over a benchmark model incorporating aggregate information when the components forecasting approach is used. We also compare the operating/financial disaggregation to the unusual/infrequent disaggregation required by US GAAP. We find that the operating/financial disaggregation yields less accurate forecasts than the unusual/infrequent disaggregation. However, when using the components forecasting approach, we find that the combination of both disaggregations improves forecasts of profitability. Finally, we document that the incremental usefulness of the operating/financial disaggregation relative to a benchmark model incorporating aggregate information is a function of growth and accounting conservatism. Overall, our study provides timely evidence concerning how analysts and investors might best use the operating/financial disaggregation for forecasting profitability.     

The Effect of Measurement Subjectivity Classifications on Analysts' Use of Persistence Classifications When Forecasting Earnings Items

Earnings items are typically classified in financial reports based on their persistence and measurement subjectivity. Archival research examines investors' use of persistence and measurement subjectivity classifications for forecasting and valuation. However, this research typically examines only one of these classifications at a time and ignores the potential interactive implications of an earnings item's persistence and measurement subjectivity classifications. We recruited experienced financial analysts to participate in two experiments that examined the effect of measurement subjectivity classifications on analysts' use of persistence classifications when forecasting earnings items. We find that analysts rely less on an earnings item's persistence classification when measurement subjectivity is high relative to when measurement subjectivity is low. We also find that presentation format affects analysts' use of these two classifications. Specifically, we find that the matrix format (i.e., rows display persistence classifications and columns display measurement subjectivity classifications) facilitates analysts' combined use of persistence and measurement subjectivity classifications relative to the sequential format (i.e., the classifications are displayed separately). These findings suggest that archival research could improve its examination of market participants' use of earnings classifications for forecasting and valuation by recognizing that the implications of an earnings item's persistence classification can vary according to the item's measurement subjectivity classification. By also demonstrating how presentation format affects analysts' use of earnings classifications, our study provides further insights into this fundamental issue in accounting research and standard setting.     

Locating drivers in a trucking terminal network

We consider the problem of determining the home locations, or domiciles, of truck drivers for a less-than-truckload carrier. Domiciling decisions are complex, in part due to regulations and union rules restricting driver schedules, but have a significant impact on the operating costs of less-than-truckload carriers. We present an iterative scheme, using driver dispatch technology in each iteration, to allocate drivers to terminals and to determine drivers’ bids so as to satisfy union requirements. Computational experiments demonstrate the value of the iterative scheme and quantify the impact of union rules on the number of drivers required (and the resulting operating costs).     

Cost-effectiveness of droxidopa in patients with neurogenic orthostatic hypotension: post-hoc economic analysis of Phase 3 clinical trial data

Objective:

Falls are associated with neurogenic orthostatic hypotension (nOH) and are an economic burden on the US healthcare system. Droxidopa is approved by the US FDA to treat symptomatic nOH. This study estimates the cost-effectiveness of droxidopa vs standard of care from a US payer perspective.

Methods:

A Markov model was used to predict numbers of falls and treatment responses using data from a randomized, double-blind trial of patients with Parkinson’s disease and nOH who received optimized droxidopa therapy or placebo for 8 weeks. The severity of falls, utility values, and injury-related costs were derived from published studies. Model outcomes included number of falls, number of quality-adjusted life-years (QALYs), and direct costs. Incremental cost-effectiveness ratios (ICERs) were calculated. Outcomes were extrapolated over 12 months.

Results:

Patients receiving droxidopa had fewer falls compared with those receiving standard of care and gained 0.33 QALYs/patient. Estimated droxidopa costs were $30,112, with estimated cost savings resulting from fall avoidance of $14,574 over 12 months. Droxidopa was cost-effective vs standard of care, with ICERs of $47,001/QALY gained, $24,866 per avoided fall with moderate/major injury, and $1559 per avoided fall with no/minor injury. The main drivers were fall probabilities and fear of fall-related inputs.

Limitations:

A limitation of the current study is the reliance on falls data from a randomized controlled trial where the placebo group served as the proxy for standard of care. Data from a larger patient population, reflecting ‘real-life’ patient use and/or comparison with other agents used to treat nOH, would have been a useful complement, but these data were not available.

Conclusion:

Using Markov modeling, droxidopa appears to be a cost-effective option compared with standard of care in US clinical practice for the treatment of nOH.     

Stabex: An evalution of the economic impact over the first five years

The shortfall in funding to meet current claims on the EEC's export earnings stabilization scheme (Stabex) has diverted attention from the operations of the scheme and its merits and shortcomings in providing developing countries with compensatory finance. This paper takes a sample of 10 ACP recipients of Stabex, which together drew over half ($200m) of the Stabex transfers under Lomé I and evaluates the stabilization effects of the transfers at macroeconomic and sectoral level. It concludes that Stabex has proved to be a useful balance-of-payments support mechanism, albeit inequality distributed due to quirks in its rules of operation, although it was designed as a scheme to support smallholder export crop producers. In the light of this evidence, the future form of Stabex under a re-negotiated Lomé Convention is considered.     

Using structural restrictions to achieve theoretical consistency in benefit transfers

Analysts often extrapolate estimates of the value of environmental improvements reported in prior studies to evaluate new policy proposals, a practice sometimes referred to as “benefit transfer.” Benefit transfer functions are frequently specified based on statistical considerations alone. However, such a purely statistical approach can lead to willingness-to-pay functions that fail to satisfy some aspects of theoretical consistency that may be especially important for policy evaluations. In this paper, we examine several previous meta-analyses of nonmarket valuation studies in light of the adding-up condition, which is one important aspect of theoretical validity. We then use meta-regression to estimate a new willingness-to-pay function for surface water quality improvements intended to be used for benefit transfers. We estimate the meta-regression model using summary results from 51 previously published stated preference studies. An important feature of our approach is that we develop the meta-regression estimating equation to ensure that the resulting benefit transfer function will necessarily comply with the adding-up condition. This is achieved by first specifying a marginal willingness-to-pay function and then deriving an expression for total willingness-to-pay. This leads to a non-linear estimating equation, so we estimate the parameters of the model using non-linear least squares. We discuss the advantages and disadvantages of our approach relative to other structural approaches, and we compare our empirical results to a more traditional nonstructural meta-regression model. Finally, we examine the quantitative importance of imposing the adding-up condition in our case study by performing some illustrative calculations of willingness-to-pay for hypothetical water quality improvements using both structural and non-structural models.