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INTEGRATION OF RANDOM SAMPLE SELECTION, SUPPORT VECTOR MACHINES AND ENSEMBLES FOR FINANCIAL RISK FORECASTING WITH AN EMPIRICAL ANALYSIS ON THE NECESSITY OF FEATURE SELECTION
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INTEGRATION OF RANDOM SAMPLE SELECTION, SUPPORT VECTOR MACHINES AND ENSEMBLES FOR FINANCIAL RISK FORECASTING WITH AN EMPIRICAL ANALYSIS ON THE NECESSITY OF FEATURE SELECTION (2012)INTEGRATION OF RANDOM SAMPLE SELECTION, S…Detection of financial statement fraud and feature selection using data mining techniques (2010)Detection of financial statem…Using neural networks and data mining techniques for the financial distress prediction model (2008)Using neural networks and dat…Forecasting financial condition of Chinese listed companies based on support vector machine (2007)Forecasting financial conditi…Predicting corporate financial distress based on integration of support vector machine and logistic regression (2006)Predicting corporate financia…A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy (2006)A real-valued genetic algorit…Hybrid genetic algorithms and support vector machines for bankruptcy prediction (2005)Hybrid genetic algorithms and…Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005)Bankruptcy prediction using s…An application of support vector machines in bankruptcy prediction model (2004)An application of support vec…Application of support vector machines in financial time series forecasting (2001)Application of support vector…A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)A survey of credit and behavi…Business failure prediction using rough sets (1999)Business failure prediction u…A neural network model for bankruptcy prediction (1990)A neural network model for ba…Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress (1985)Introducing Recursive Partiti…Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Financial Ratios and the Prob…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)FINANCIAL RATIOS, DISCRIMINAN…Financial Ratios As Predictors of Failure (1966)Financial Ratios As Predictor…The Pricing of Options and Corporate Liabilities (1973)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Credit scoring with a data mining approach based on support vector machines (2006)Recent developments in consumer credit risk assessment (2007)A comparative assessment of ensemble learning for credit scoring (2010)ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)An application of the cox proportional hazards model to bank failure (1986)A survey of business failures with an emphasis on prediction methods and industrial applications (1996)Predicting corporate financial distress: Reflections on choice-based sample bias (2002)Stock market prediction system with modular neural networks (1990)Introduction to financial forecasting (1996)Neural network forecasting of the British Pound/US Dollar exchange rate (1998)Financial time series forecasting using support vector machines (2003)Support vector machine with adaptive parameters in financial time series forecasting (2003)Forecasting stock market movement direction with support vector machine (2004)Conditional logit analysis of qualitative choice behavior (1972)The Effect of Audit Quality on Earnings Management* (1998)Forecasting Bankruptcy More Accurately: A Simple Hazard Model (2001)Positive Accounting Theory (1985)Designing a neural network for forecasting financial and economic time series (1996)Forecasting with artificial neural networks: (1998)Methodological Issues Related to the Estimation of Financial Distress Prediction Models (1984)In Search of Distress Risk (2008)Using neural network ensembles for bankruptcy prediction and credit scoring (2007)Consumer credit-risk models via machine-learning algorithms (2010)Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016)A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)Managerial incentives and risk-taking☆ (2005)How does financial reporting quality relate to investment efficiency? (2009)Big data techniques in auditing research and practice: Current trends and future opportunities (2018)Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach (2019)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)
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References

  • 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY14,869 cited
  • 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy6,005 cited
  • 1966Financial Ratios As Predictors of Failure4,753 cited
  • 2001Application of support vector machines in financial time series forecasting1,271 cited
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  • 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress748 cited
  • 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers744 cited
  • 2010Detection of financial statement fraud and feature selection using data mining techniques586 cited
  • 1999Business failure prediction using rough sets493 cited
  • 2006A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy381 cited
  • 2005Hybrid genetic algorithms and support vector machines for bankruptcy prediction377 cited
  • 2008Using neural networks and data mining techniques for the financial distress prediction model

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283 cited
  • 2007Forecasting financial condition of Chinese listed companies based on support vector machine246 cited
  • 2006Predicting corporate financial distress based on integration of support vector machine and logistic regression229 cited
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