英文标题：
《Extracting Predictive Information from Heterogeneous Data Streams using
Gaussian Processes》
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作者：
Sid Ghoshal, Stephen Roberts
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最新提交年份：
2018
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英文摘要：
Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain\'s heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction.
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中文摘要：
金融市场是出了名的复杂环境，呈现出大量嘈杂但潜在信息丰富的数据。我们考虑使用带有自动相关确定（ARD）核的在线高斯过程从广泛的信息源预测金融时间序列的问题。我们通过融合四个独立的数据域（时间序列技术、情绪分析、期权市场数据和经纪人建议）中的每一个来衡量绩效收益，并以标准化均方根误差（NRMSE）、中值绝对偏差（MAD）和皮尔逊相关性进行量化。我们证明，ARD内核产生有意义的特征排名，有助于保留显著的输入并降低输入维度，为筛选财务复杂性提供了一个框架。我们测量将每个域的异构数据流融合到单个概率模型中的性能增益。特别是，我们的研究结果强调了期权数据在绘制价格空间曲率方面的关键价值，并为金融预测的研究提供了直观、新颖的方向。
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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：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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