Journal of Investment, Banking and Finance(JIBF)

WorldQuant University, United States

Publications

• Research Article   
  Deep Learning-Based Trading Strategy for Index ETFs Using LSTM and GARCH Under Market Uncertainty
  Author(s): Tumelo Ranoto*, Faezeh Ranjbar and Joe Wayne Byers
  
  This study deduces a trading system that combines econometric volatility modeling and deep learning for the purpose of enhancing index Exchange-Traded Fund (ETF) forecasts. It employs a series of Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-family models: GARCH(1,1), Exponential GARCH (EGARCH), Glosten– Jagannathan– Runkle GARCH (GJR-GARCH) , Asymmetric Power ARCH (APARCH), and Integrated GARCH (IGARCH) to estimate volatility processes in SPDR S&P 500 ETF Trust (SPY) returns and model them. These estimates are transformed into normalized and volatility-adjusted returns and used to provide insights towards sharpening predictive signals. Hidden Markov Models (HMMs) analyze raw and adjusted returns with the aim of detecting changes in market behavior and discriminating between various volatility regimes. Also, technical analysis indicators such as the.. Read More»
  

  Abstract HTML PDF