限价订单簿中的排队不平衡预测
Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book
摘要：
我们研究是否出价/要价队列不平衡的限价订单簿（LOB）提供了显着的预测能力的方向，下一个中间价的运动。我们在一个简单的二元分类器和一个概率分类器的上下文中考虑这个问题，二元分类器试图预测下一个中间价走势的方向，概率分类器试图预测下一个中间价走势向上的概率。为了实现这些分类器，我们拟合了纳斯达克10只流动股票的队列不平衡和随后中间价运动方向之间的逻辑回归。在每一种情况下，我们发现这些变量之间存在强烈的统计学显着关系。与一个简单的空模型相比，它假设中间价变化的方向与队列不平衡无关，我们发现我们的逻辑回归拟合为大报价股票的二进制和概率分类提供了相当大的改进，并为小报价股票的二进制和概率分类提供了适度的改进。我们还对相同的数据进行了局部逻辑回归拟合，发现这种半参数方法的性能略优于我们的逻辑回归拟合，但代价是实现时的计算量更大。
英文摘要：
We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a probabilistic classifier, which seeks to predict the probability that the next mid-price movement will be upwards. To implement these classifiers, we fit logistic regressions between the queue imbalance and the direction of the subsequent mid-price movement for each of 10 liquid stocks on Nasdaq. In each case, we find a strongly statistically significant relationship between these variables. Compared to a simple null model, which assumes that the direction of mid-price changes is uncorrelated with the queue imbalance, we find that our logistic regression fits provide a considerable improvement in binary and probabilistic classification for large-tick stocks, and provide a moderate improvement in binary and probabilistic classification for small-tick stocks. We also perform local logistic regression fits on the same data, and find that this semi-parametric approach slightly outperform our logistic regression fits, at the expense of being more computationally intensive to implement.
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