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A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices
16 references·0 citing·53 second hop
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References

  • 1995Comparing Predictive Accuracy5,214 cited
  • 2020Empirical Asset Pricing via Machine Learning2,528 cited
  • 2012On the use of cross-validation for time series predictor evaluation1,324 cited
  • 2000Out-of-sample tests of forecasting accuracy: an analysis and review1,014 cited
  • 2013International Stock Return Predictability: What Is the Role of the United States?702 cited
  • 2005Relationships Between Implied Volatility Indexes and Stock Index Returns432 cited
  • 2002Market timing and return prediction under model instability307 cited
  • 2014The Impact of Oil Price Shocks on the Stock Market Return and Volatility Relationship287 cited
  • 2011Evaluating the Classification of Economic Activity into Recessions and Expansions261 cited
  • 1997Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models258 cited
  • 2001S&P futures returns and contrary sentiment indicators148 cited
  • 2015The Interactions among Gold, Oil, and Stock Market: Evidence from S&P500122 cited
  • 2010Forecasting the direction of the US stock market with dynamic binary probit models97 cited
  • 2008Return forecasts and optimal portfolio construction: a quantile regression approach73 cited

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2013
Model-Free Volatility Indexes in the Financial Literature: A Review
67 cited
  • 2020Modelling the volatility of crude oil returns: Jumps and volatility forecasts63 cited
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