Corpus ID: 222310279

Asset Price Forecasting using Recurrent Neural Networks

  title={Asset Price Forecasting using Recurrent Neural Networks},
  author={Hamed Vaheb},
  • Hamed Vaheb
  • Published 2020
  • Computer Science, Economics, Mathematics
  • ArXiv
  • This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring… CONTINUE READING


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