Systematic review of bankruptcy prediction models: Towards a framework for tool selection
The bankruptcy prediction research domain continues to evolve with many new different
predictive models developed using various tools. Yet many of the tools are used with the …
predictive models developed using various tools. Yet many of the tools are used with the …
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches
As a hot topic, financial distress prediction (FDP), or called as corporate failure prediction,
bankruptcy prediction, acts as an important role in decision-making of various areas …
bankruptcy prediction, acts as an important role in decision-making of various areas …
Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods
Binary financial distress prediction (FDP), which categorizes corporate financial status into
the two classes of distress and nondistress, cannot provide enough support for effective …
the two classes of distress and nondistress, cannot provide enough support for effective …
Pre-and post-dam river water temperature alteration prediction using advanced machine learning models
Dams significantly impact river hydrology by changing the timing, size, and frequency of low
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
The effects of handling outliers on the performance of bankruptcy prediction models
Ratio type financial indicators are the most popular explanatory variables in bankruptcy
prediction models. These measures often exhibit heavily skewed distribution because of the …
prediction models. These measures often exhibit heavily skewed distribution because of the …
A two-stage classification technique for bankruptcy prediction
P du Jardin - European Journal of Operational Research, 2016 - Elsevier
Ensemble techniques such as bagging or boosting, which are based on combinations of
classifiers, make it possible to design models that are often more accurate than those that …
classifiers, make it possible to design models that are often more accurate than those that …
A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring
Credit and credit-based transactions underlie the financial system. After decades of
development, artificial intelligence and machine learning have brought new momentum to …
development, artificial intelligence and machine learning have brought new momentum to …
Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles
Q Wang, W Xu, H Zheng - Neurocomputing, 2018 - Elsevier
Many researchers and practitioners have attempted to predict financial market trends for
excess returns using multiple information sources including social media. Recent studies …
excess returns using multiple information sources including social media. Recent studies …
On random subspace optimization-based hybrid computing models predicting the california bearing ratio of soils
The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity
of pavement subgrade materials. In this research, random subspace optimization-based …
of pavement subgrade materials. In this research, random subspace optimization-based …
Enhancing corporate bankruptcy prediction via a hybrid genetic algorithm and domain adaptation learning architecture
In the contemporary business landscape, accurately evaluating a company's financial health
is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an …
is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an …