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Censored posterior and predictive likelihood in Bayesian left-tail prediction for accurate value at risk estimation
Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013
Persistent link: https://www.econbiz.de/10009756308
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Beware the performance of an algorithm before relying on it : evidence from a stock price forecasting experiment
Tse, Tiffany Tsz Kwan; Hanaki, Nobuyuki; Mao, Bolin - 2022
the task, and the performance level of the algorithm. We found that when participants could freely decide on their final … forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average … in the task. Experienced participants relied less on the algorithm than inexperienced participants, regardless of its …
Persistent link: https://www.econbiz.de/10013419049
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Time Series Forecasting With a Learning Algorithm : An Approximate Dynamic Programming Approach
Collado, Ricardo - 2019
We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...
Persistent link: https://www.econbiz.de/10012894079
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Forecasting Financial Distress by the Semiparametric Logistic Scoring
Farhat, Abdejelil - 2011
The aim of this paper is to compare the forecasting financial distress models named the logistic mixed scoring and the semi parametric logistic scoring. Altman scoring variables are used to predict financial situation of Tunisian firms. First, the mixed logistic scoring is applied in order to...
Persistent link: https://www.econbiz.de/10013134046
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Intertemporal Defaulted Bond Recoveries Prediction Via Machine Learning
Nazemi, Abdolreza - 2018
The recovery rate on defaulted corporate bonds has a time-varying distribution. We propose machine learning approaches for intertemporal analysis of U.S. corporate bonds' recovery rates with a large number of predictors. The most informative macroeconomic variables are selected from a broad...
Persistent link: https://www.econbiz.de/10012908447
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Predicting Bankruptcy via Cross-Sectional Earnings Forecasts
Hess, Dieter - 2019
We develop a model to predict bankruptcies, exploiting that negative book equity is a strong indicator of financial distress. Accordingly, our key predictor of bankruptcy is the probability that future losses will deplete a firm's book equity. To calculate this probability, we use earnings...
Persistent link: https://www.econbiz.de/10012899828
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Predicting Default More Accurately : To Proxy or Not to Proxy for Default?
Galil, Koresh - 2018
Previous studies targeting accuracy improvement of default models mainly focused on the choice of the explanatory variables and the statistical approach. We alter the focus to the choice of the dependent variable. We particularly explore whether the common practice (in literature) of using...
Persistent link: https://www.econbiz.de/10012937214
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Unveiling the diverse efficacy of artificial neural networks and logistic regression : a comparative analysis in predicting financial distress
Sabek, Amine - In: Croatian review of economic, business and social … 9 (2023) 1, pp. 16-32
The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict...
Persistent link: https://www.econbiz.de/10014372938
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Kombinierte Liquiditäts- und Solvenzkennzahlen und ein darauf basierendes Insolvenzprognosemodell für deutsche GmbHs
Perederiy, Volodymyr (contributor) - 2007
Eine große Herausforderung der multivariablen Analyse mit bilanziellen Kennzahlen besteht in der Identifikation derjenigen Kennzahlen, die zur besten Modellperformance führen und dabei möglichst leicht interpretierbar und intuitiv bleiben. Die Menge der in Frage kommenden Kennzahlen ist in...
Persistent link: https://www.econbiz.de/10003635001
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Do Structural Constraints of the Industry Matter for Corporate Failure Prediction?
Chiu, Wan-Chien - 2014
We hypothesize and find empirical evidence that two structural constraints of the industry are informative in the corporate failure prediction, industry concentration and dependence on customers and suppliers. Using an extensive database on corporate failures and bankruptcies in U.S. market from...
Persistent link: https://www.econbiz.de/10013067701