SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data
16 references·0 citing·44 second hop
Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data (2024)Bankruptcy prediction using optimal ensem…Impacts of crisis on SME bankruptcy prediction models’ performance (2022)Impacts of crisis on SME bank…Bankruptcy prediction using synthetic sampling (2022)Bankruptcy prediction using s…CatBoost model and artificial intelligence techniques for corporate failure prediction (2021)CatBoost model and artificial…Bankruptcy prediction using fuzzy convolutional neural networks (2021)Bankruptcy prediction using f…A horse race of models and estimation methods for predicting bankruptcy (2021)A horse race of models and es…Performance assessment of ensemble learning systems in financial data classification (2020)Performance assessment of ens…CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection (2020)CUS-heterogeneous ensemble-ba…Targeting customers for profit: An ensemble learning framework to support marketing decision-making (2019)Targeting customers for profi…Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design (2019)Can machine learning approach…Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets (2019)Bankruptcy prediction for sma…An investigation of bankruptcy prediction in imbalanced datasets (2018)An investigation of bankruptc…A machine learning framework for customer purchase prediction in the non-contractual setting (2018)A machine learning framework …Features selection, data mining and finacial risk classification: a comparative study (2016)Features selection, data mini…Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016)Ensemble boosted trees with s…Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices (2015)Comparing Variational and Emp…An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility (2014)An Ensemble System Based on H…Financial Ratios As Predictors of Failure (1966)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)A comparative study on base classifiers in ensemble methods for credit scoring (2016)Predicting failure in the U.S. banking sector: An extreme gradient boosting approach (2018)Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling (2011)Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices (2013)Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis (2018)A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms (2021)Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network (2021)Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers (2000)The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration (2003)Relationship quality as a predictor of B2B customer loyalty (2006)A systematic review of the research trends of machine learning in supply chain management (2019)Machine learning in energy economics and finance: A review (2019)On the platform but will they buy? Predicting customers' purchase behavior using deep learning (2021)Predictive Analytics in Information Systems Research (2010)Predictive Analytics in Information Systems Research1 (2011)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Response transformation and profit decomposition for revenue uplift modeling (2019)Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art (2023)Profit-driven weighted classifier with interpretable ability for customer churn prediction (2024)Neural network credit scoring models (2000)On the network topology of variance decompositions: Measuring the connectedness of financial firms (2014)Prediction of financial distress in the Spanish banking system (2019)Machine learning methods in finance: Recent applications and prospects (2022)The Cost of Capital, Corporation Finance and the Theory of Investment (1958)The cost of capital, corporation finance and the theory of investment (2008)Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security? (2020)Financial distress prediction by combining sentiment tone features (2021)A Discriminant Analysis of Predictors of Business Failure (1972)Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach (2022)Systematic Review of Financial Distress Identification using Artificial Intelligence Methods (2022)Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction (2024)Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions (2023)Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers (1986)Bankruptcy prediction using machine learning and Shapley additive explanations (2023)Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models (2024)Bankruptcy prediction: Integration of convolutional neural networks and explainable artificial intelligence techniques (2025)Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE (2020)Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods (2021)Financial applications of machine learning: A literature review (2023)
this work references cited by second hop

References

  • 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction419 cited
  • 2021CatBoost model and artificial intelligence techniques for corporate failure prediction397 cited
  • 2018A machine learning framework for customer purchase prediction in the non-contractual setting267 cited
  • 2018An investigation of bankruptcy prediction in imbalanced datasets185 cited
  • 2015Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices160 cited
  • 2019Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets109 cited
  • 2020CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection91 cited
  • 2019Targeting customers for profit: An ensemble learning framework to support marketing decision-making80 cited
  • 2019Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design63 cited
  • 2021Bankruptcy prediction using fuzzy convolutional neural networks60 cited
  • 2022Impacts of crisis on SME bankruptcy prediction models’ performance56 cited
  • 2020Performance assessment of ensemble learning systems in financial data classification49 cited
  • 2016Features selection, data mining and finacial risk classification: a comparative study36 cited
  • 2014

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility
36 cited
  • 2022Bankruptcy prediction using synthetic sampling33 cited
  • 2021A horse race of models and estimation methods for predicting bankruptcy26 cited
  • Cited by

    No citing works inside the corpus for this window.