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Forecasting stock market crisis events using deep and statistical machine learning techniques
10 references·16 citing·49 second hop
Forecasting stock market crisis events using deep and statistical machine learning techniques (2018)Forecasting stock market crisis events us…Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)Forecasting the volatility of…Forecasting daily stock market return using dimensionality reduction (2016)Forecasting daily stock marke…Credit scoring using the clustered support vector machine (2014)Credit scoring using the clus…The use of data mining and neural networks for forecasting stock market returns (2005)The use of data mining and ne…Combination forecasts of output growth in a seven‐country data set (2004)Combination forecasts of outp…A New Approach to Measuring Financial Contagion (2003)A New Approach to Measuring F…No Contagion, Only Interdependence: Measuring Stock Market Comovements (2002)No Contagion, Only Interdepen…Extreme Correlation of International Equity Markets (2001)Extreme Correlation of Intern…A New Approach to Measuring Financial Contagion (2000)A New Approach to Measuring F…Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Financial Ratios and the Prob…Financial applications of machine learning: A literature review (2023)Financial applications of mac…How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda (2022)How are reinforcement learnin…Deep Learning-Based Model for Financial Distress Prediction (2022)Deep Learning-Based Model for…Stock Prediction Based on Technical Indicators Using Deep Learning Model (2021)Stock Prediction Based on Tec…Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market (2021)Novel Stock Crisis Prediction…Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators (2021)Feature selection and deep ne…Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Markets (2021)Explainable AI (XAI) Models A…Effective multinational trade forecasting using LSTM recurrent neural network (2021)Effective multinational trade…Crowds, Lending, Machine, and Bias (2021)Crowds, Lending, Machine, and…Bankruptcy prediction using fuzzy convolutional neural networks (2021)Bankruptcy prediction using f…Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average (2020)Forecasting the monthly iron …Forecasting significant stock price changes using neural networks (2020)Forecasting significant stock…A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM (2020)A novel deep learning framewo…Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting (2019)Can deep learning predict ris…A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices (2019)A CEEMDAN and XGBOOST‐Based A…Lagged correlation-based deep learning for directional trend change prediction in financial time series (2018)Lagged correlation-based deep…Applied Statistical Decision Theory (1962)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks (2014)A comparative study on base classifiers in ensemble methods for credit scoring (2016)Machine learning techniques for credit risk evaluation: a systematic literature review (2020)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Business conditions and expected returns on stocks and bonds (1989)Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010)Systematic analysis and review of stock market prediction techniques (2019)Financial Ratios As Predictors of Failure (1966)Conditional logit analysis of qualitative choice behavior (1972)Methodological Issues Related to the Estimation of Financial Distress Prediction Models (1984)The Effect of Audit Quality on Earnings Management* (1998)Forecasting Bankruptcy More Accurately: A Simple Hazard Model (2001)The Combination of Forecasts (1969)Forecasting Using Principal Components From a Large Number of Predictors (2002)Macroeconomic Forecasting Using Diffusion Indexes (2002)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Forecasting Stock Returns (2013)Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2016)Extreme Values, Regular Variation and Point Processes (1987)Modelling Extremal Events (1997)International Asset Allocation With Regime Shifts (2002)Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns (2006)MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE* (2006)Fuzzy sets (1965)Fuzzy sets (1996)Time series forecasting using a hybrid ARIMA and neural network model (2003)A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction (2017)ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module (2018)Stock market prediction using machine learning classifiers and social media, news (2020)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)Forecasting stock prices with long-short term memory neural network based on attention mechanism (2020)A Survey of Forex and Stock Price Prediction Using Deep Learning (2021)Limited-dependent and qualitative variables in econometrics (1983)On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation (2004)The subprime credit crisis and contagion in financial markets (2010)Capital flows, push versus pull factors and the global financial crisis (2012)Financial Contagion (2000)Emerging markets finance (2003)Dynamic correlation analysis of financial contagion: Evidence from Asian markets (2007)Measuring financial contagion: A Copula approach (2007)Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets (2008)Better to give than to receive: Predictive directional measurement of volatility spillovers (2011)
this work references cited by second hop

References

  • 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy6,005 cited
  • 2002No Contagion, Only Interdependence: Measuring Stock Market Comovements4,647 cited
  • 2001Extreme Correlation of International Equity Markets2,608 cited
  • 2004Combination forecasts of output growth in a seven‐country data set1,138 cited
  • 2003A New Approach to Measuring Financial Contagion1,015 cited
  • 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models786 cited
  • 2000A New Approach to Measuring Financial Contagion723 cited
  • 2005The use of data mining and neural networks for forecasting stock market returns465 cited
  • 2016Forecasting daily stock market return using dimensionality reduction395 cited
  • 2014Credit scoring using the clustered support vector machine269 cited

Cited by

  • 2020A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM237 cited
  • 2023Financial applications of machine learning: A literature review230 cited
  • 2022How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda203 cited
  • 2019A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

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131 cited
  • 2021Crowds, Lending, Machine, and Bias126 cited
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  • 2018Lagged correlation-based deep learning for directional trend change prediction in financial time series123 cited
  • 2019Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting120 cited
  • 2021Stock Prediction Based on Technical Indicators Using Deep Learning Model118 cited
  • 2020Forecasting significant stock price changes using neural networks117 cited
  • 2021Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators80 cited
  • 2020Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average75 cited
  • 2021Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market65 cited
  • 2021Bankruptcy prediction using fuzzy convolutional neural networks60 cited
  • 2021Effective multinational trade forecasting using LSTM recurrent neural network57 cited
  • 2021Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Markets55 cited