摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx编辑在中国上海的新闻报道，研究表明：“了解价格信息如何决定未来的价格走势，对于那些经常在买卖双方下订单的做市商，以及交易者分割元订单以减少价格影响非常重要。”本研究的资助者包括国家自然科学基金。新闻编辑们从上海大学的研究中获得了一句话：“即使问题具有复杂的非线性性质，我们考虑了期权订单中价格走势的预测，”本文利用机器学习工具,对中国股票交易所股票期权的日内交易数据进行研究,应用了多种机器学习方法,包括决策树、随机森林、Logistic回归和长短期Me Mory神经网络等,随着机器学习模型的复杂性,对股票期权的日内交易数据进行分析。我们发现，价格波动是可预测的，具有时滞特征的深度神经网络比其他简单的模型表现得更好，这种能力具有通用性和跨资产共享性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Understanding how pricevolume i nformation determines future price movement is important for market makers who f requently place orders on both buy and sell sides, and for traders to split meta -orders to reduce price impact." Funders for this research include National Natural Science Foundation of China. The news editors obtained a quote from the research from Shanghai University: "G iven the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is current ly missing. On an intraday tick-level dataset of options on an exchange traded f und from the Chinese market, we apply a variety of machine learning methods, inc luding decision tree, random forest, logistic regression, and long short-term me mory neural network. As machine learning models become more complex, they can ex tract deeper hidden relationship from input features, which classic market micro structure models struggle to deal with. We discover that the price movement is p redictable, deep neural networks with time-lagged features perform better than a ll other simpler models, and this ability is universal and shared across assets. "
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