Adaptive Market Making via Online Learning
Adaptive Market Making via Online Learning
复制标题
通过在线学习进行适应性做市
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Satyen Kale
中科院分区：
文献类型：
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作者：
Jacob D. Abernethy;Satyen Kale
我们考虑交易所做市策略的设计。做市商通常寻求从一项资产的买入和卖出价格之间的差价中获利，但做市商在价格大幅波动的情况下也要承担风险。做市策略的利润保证通常需要对相关资产的价格波动做出某些随机假设；例如，假设一个价格过程是均值回归的模型。我们提出了一类“基于价差”的做市策略，即使在最坏情况（对抗）的情况下，其表现也可以控制。我们证明了这些策略的结构性质，使我们能够设计一个主算法，相对于后见之明的最佳策略获得低后悔。我们运行了一组实验，在最近的真实世界的股票价格数据上显示出良好的性能。
We consider the design of strategies for market making in an exchange. A market maker generally seeks to profit from the difference between the buy and sell price of an asset, yet the market maker also takes exposure risk in the event of large price movements. Profit guarantees for market making strategies have typically required certain stochastic assumptions on the price fluctuations of the asset in question; for example, assuming a model in which the price process is mean reverting. We propose a class of "spread-based" market making strategies whose performance can be controlled even under worst-case (adversarial) settings. We prove structural properties of these strategies which allows us to design a master algorithm which obtains low regret relative to the best such strategy in hindsight. We run a set of experiments showing favorable performance on recent real-world stock price data.