英文标题：
《Stock Market Prediction from WSJ: Text Mining via Sparse Matrix
Factorization》
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作者：
Felix Ming Fai Wong, Zhenming Liu, Mung Chiang
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最新提交年份：
2014
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英文摘要：
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the \"co-movements\" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive backtesting on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.
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中文摘要：
我们再次讨论了基于新闻文章预测股票价格方向变动的问题：在这里，我们的算法使用《华尔街日报》的每日文章来预测当天的收盘价格。我们提出了一个统一的潜在空间模型来描述股票价格和新闻文章之间的“共同运动”。与许多现有方法不同，我们的新模型能够同时利用相关性：（a）股票价格之间的相关性，（b）新闻文章之间的相关性，以及（c）股票价格与新闻文章之间的相关性。因此，我们的模型能够对500多只股票进行每日预测（其中大多数股票甚至没有在任何新闻文章中提及），同时具有较低的复杂性。基于我们的算法，我们对交易策略进行了广泛的回溯测试。结果表明，与许多广泛使用的算法相比，我们的模型具有更好的准确率（55.7%）。基于我们模型的交易策略产生的回报率（56%）和夏普比率也远高于基准指数。
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分类信息：

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：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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