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sp500

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Abstract: The S&P500 is difficult to predict. Multi-factor models provide a useful framework for making returns predictions and for controlling portfolio risk. This paper explores a three-step process in predicting PCA and Autoencoders factors to generate multi-factor models from the S&P500 component securities.

  • Updated Jan 18, 2020
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Developed a predictive model using LSTM networks to forecast strong daily uptrends in the SPY ETF. This project includes feature engineering, model building and hyper parameter tuning and a backtested trading strategy that outperforms the market.

  • Updated Aug 29, 2024
  • Jupyter Notebook

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