Predicting Earnings and Cash Flows: The Information Content of Losses and Tax Loss Carryforwards
Sandra Dreher, Sebastian Eichfelder, Felix Noth
Abstract
We analyse the relevance of losses, accounting information on tax loss carryforwards, and deferred taxes for the prediction of earnings and cash flows up to four years ahead. We use a unique hand-collected panel of German listed firms encompassing detailed information on tax loss carryforwards and deferred taxes from the tax footnote. Our out-of-sample predictions show that considering accounting information on tax loss carryforwards and deferred taxes does not enhance the accuracy of performance forecasts and can even worsen performance predictions. We find that common forecasting approaches that treat positive and negative performances equally or that use a dummy variable for negative performance can lead to biased performance forecasts, and we provide a simple empirical specification to account for that issue.
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Tail-risk Protection Trading Strategies
Natalie Packham, Jochen Papenbrock, Peter Schwendner, Fabian Wöbbeking
Quantitative Finance,
Nr. 5,
2017
Abstract
Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Evidence for the Existence of Downward Real-Activity Earnings Management
Bill Francis, Iftekhar Hasan, Lingxiang Li
Journal of Accounting, Auditing and Finance,
Nr. 2,
2016
Abstract
Prior studies of real-activity earnings management (REM) focus on earnings-inflating abnormal activities. We seek to establish the existence of downward REM by investigating several corporate events in which managers have incentives to temporarily deflate market valuations. Specifically, we focus on, and find downward REM before, share repurchases, management buyouts (MBOs), and CEO option awards. Large-sample evidence of downward REM is also found in our general analysis of earnings smoothing. Downward REM becomes much smaller or nonexistent when there is a lack of managerial incentives in those events, such as non-carry-through repurchases, incomplete MBOs, and unexpected option awards. Following the research design of Zang, we find that various REM and accrual-based earnings management (AEM) cost factors consistently influence the magnitude of downward REM and AEM around the three corporate events.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Predicting Financial Crises: The (Statistical) Significance of the Signals Approach
Makram El-Shagi, Tobias Knedlik, Gregor von Schweinitz
Journal of International Money and Finance,
Nr. 35,
2013
Abstract
The signals approach as an early-warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it cannot distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful results; (2) that composite indicators aggregating information contained in individual indicators add value to the signals approach; and (3) that indicators which are found to be significant in-sample usually perform similarly well out-of-sample.
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Delineation of City Regions Based on Commuting Interrelations: The Example of Large Cities in Germany
Albrecht Kauffmann
IWH Discussion Papers,
Nr. 4,
2012
Abstract
The comparison of cities with regard to their economic or demographic development may yield misleading results, if solely the cities in their administrative borders are the object of consideration. Frequently, historical borders of cities neither conform to the contemporary settlement structures, nor do they consider the mutual dependencies between cities and parts of their hinterland. Therefore, it is often claimed to use city regions as objects of comparison or for the sake of urban planning. Commonly, the delineation of functional regions is based on commuting flows from the municipalities in the hinterland of the core cities directed to the cores. A municipality is regarded as belonging to a certain city region if the share of out-commuters from this municipality to the respective core in the total mass of those employees who reside in that municipality is the largest one, and if this share exceeds a certain threshold value. However, commuting flows in the opposite direction are not considered. The method presented here delineates city regions on the base of bidirectional commuting flows. Hereby, various modifications regarding the characteristics of the employment base, the possibility of overlaps of regions, the formation of polycentric city regions, and of the minimum threshold value of mutual connectivity are applied to the sample of 81 German cities with more than 100 000 inhabitants. Finally, the effects of different kinds of regionalisation on the coefficients of regional specialisation of these cities and city regions are demonstrated.
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Bank-specific Shocks and the Real Economy
Claudia M. Buch, Katja Neugebauer
Journal of Banking and Finance,
Nr. 8,
2011
Abstract
Governments often justify interventions into the financial system in the form of bail outs or liquidity assistance with the systemic importance of large banks for the real economy. In this paper, we analyze whether idiosyncratic shocks to loan growth at large banks have effects on real GDP growth. We employ a measure of idiosyncratic shocks which follows Gabaix (forthcoming). He shows that idiosyncratic shocks to large firms have an impact on US GDP growth. In an application to the banking sector, we find evidence that changes in lending by large banks have a significant short-run impact on GDP growth. Episodes of negative loan growth rates and the Eastern European countries in our sample drive these results.
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