论文标题
深度学习在股票市场预测中的应用：最新进展
Applications of deep learning in stock market prediction: recent progress
论文作者
论文摘要
股票市场预测一直是一个经典而又具有挑战性的问题，受到经济学家和计算机科学家的关注。为了构建有效的预测模型，在过去的几十年中，已经探索了线性和机器学习工具。最近，已经引入了深度学习模型，作为该主题的新领域，快速发展太快了，无法追赶。因此，我们进行调查的动机是对股票市场预测深度学习模型的最新作品进行最新审查。我们不仅将不同的数据源，各种神经网络结构和常用评估指标分类，还分类了实现和可重复性。我们的目标是帮助感兴趣的研究人员与最新进展同步，并帮助他们轻松地将先前的研究重现为基础。基于摘要，我们还强调了该主题中的一些未来研究方向。
Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Base on the summary, we also highlight some future research directions in this topic.
