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