论文标题
深入投资凯尔的单个时期模型
Deep Investing in Kyle's Single Period Model
论文作者
论文摘要
凯尔（Kyle）模型描述了订单规模和安全价格的平衡是如何在带有内部信息的交易者之间自然出现的，而价格在通过一系列拍卖互动的情况下提供了市场制造商的价格。自从1985年由Albert S. Kyle引入以来，该模型在使用不对称信息的市场微观结构模型的研究中变得很重要。众所周知，它是研究如何使用现代深度学习技术来复制和更好地理解某些市场学习问题中发生的平衡的绝佳机会。 我们使用深层神经网络在凯尔的单个时期设置中对代理进行建模。通过按照Kyle定义的规则和目标进行交互来训练网络。我们展示了正确的网络体系结构和训练方法如何导致代理行为融合到凯尔（Kyle）模型预测的理论平衡中。
The Kyle model describes how an equilibrium of order sizes and security prices naturally arises between a trader with insider information and the price providing market maker as they interact through a series of auctions. Ever since being introduced by Albert S. Kyle in 1985, the model has become important in the study of market microstructure models with asymmetric information. As it is well understood, it serves as an excellent opportunity to study how modern deep learning technology can be used to replicate and better understand equilibria that occur in certain market learning problems. We model the agents in Kyle's single period setting using deep neural networks. The networks are trained by interacting following the rules and objectives as defined by Kyle. We show how the right network architectures and training methods lead to the agents' behaviour converging to the theoretical equilibrium that is predicted by Kyle's model.
