英文标题：
《Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for
Midterm Stock Prediction》
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作者：
Xinyi Li, Yinchuan Li, Xiao-Yang Liu and Christina Dan Wang
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最新提交年份：
2019
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英文摘要：
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.
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中文摘要：
中期股价预测对于股票市场的价值投资至关重要。然而，大多数深度学习模型本质上是短期的，将其应用于中期预测会遇到巨大的累积误差，因为它们无法避免异常。在本文中，我们提出了一种新的用于中期股票预测的深度神经网络Mid-LSTM，它将市场趋势作为隐藏状态。首先，在自回归滑动平均模型（ARMA）的基础上，通过同时考虑隐藏状态和资本资产定价模型，建立了一个中期ARMA。然后，设计了一个基于LSTM的中期深度神经网络，该网络由LSTM、隐马尔可夫模型和线性回归网络三部分组成。所提出的中期LSTM可以避免异常，减少较大的预测误差，对影响股价的因素具有良好的解释效果。对标准普尔500指数股票的大量实验表明：（i）所提出的中期LSTM在预测精度上提高了2-4%，并且（ii）在组合配置投资中，我们实现了120.16%的年回报率和2.99的平均夏普比率。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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