论文信息 - Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks

Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks

We use an autoencoder composed of stacked restricted Boltzmann machines to extract features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period versus 10.53% for basic momentum.

Lawrence Takeuchi | Lawrence Takeuchi

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