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Midterm stock price prediction is crucial for value investments in the stockmarket. However, most deep learning models are essentially short-term andapplying them to midterm predictions encounters large cumulative errors becausethey cannot avoid anomalies. In this paper, we propose a novel deep neuralnetwork Mid-LSTM for midterm stock prediction, which incorporates the markettrend as hidden states. First, based on the autoregressive moving average model(ARMA), a midterm ARMA is formulated by taking into consideration both hiddenstates and the capital asset pricing model. Then, a midterm LSTM-based deepneural network is designed, which consists of three components: LSTM, hiddenMarkov model and linear regression networks. The proposed Mid-LSTM can avoidanomalies to reduce large prediction errors, and has good explanatory effectson the factors affecting stock prices. Extensive experiments on S&P 500 stocksshow that (i) the proposed Mid-LSTM achieves 2-4% improvement in predictionaccuracy, and (ii) in portfolio allocation investment, we achieve up to 120.16%annual return and 2.99 average Sharpe ratio.

Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction