Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

16 references·1 citing·39 second hop

FromToDepth

this work references cited by second hop

References

2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
2020Financial markets under the global pandemic of COVID-192,349 cited
1971Time Series Analysis Forecasting and Control1,792 cited
2020Stock markets’ reaction to COVID-19: Cases or fatalities?1,253 cited
1995Random Walks in Stock Market Prices1,227 cited
2014Stock Price Prediction Using the ARIMA Model1,153 cited
2020COVID-19 and the march 2020 stock market crash. Evidence from S&P1500726 cited
1989Invited review combining forecasts—twenty years later321 cited
2019Predicting the daily return direction of the stock market using hybrid machine learning algorithms276 cited
2020Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost242 cited
2020COVID-19: stock market reactions to the shock and the stimulus212 cited
2020Trust and stock market volatility during the COVID-19 crisis208 cited
2021The impact of COVID-19 on the stock market crash risk in China186 cited
2021Technical analysis strategy optimization using a machine learning approach in stock market indices171 cited
2013An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50

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

144 cited

2010Stock market value prediction using neural networks142 cited

Cited by

2024A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting48 cited