The Effect of Reinforcement Learning Agents in Double-Auction Markets

author: Neil Raynor, School of Computer Science and Electronic Engineering, University of Essex
author: Khoa Minh Nguyen, School of Computer Science and Electronic Engineering, University of Essex
author: Imon Palit, School of Computer Science and Electronic Engineering, University of Essex
published: Aug. 21, 2009,   recorded: July 2009,   views: 4358

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Several time series models such as ARCH and GARCH have been developed to forecast volatility using asset returns data. However, these methods ignore one key source of market volatility: financial news. Similarly, asset pricing models often describe the arrival of novel information by a jump process, but the characteristics of the underlying jump process are only coarsely, if at all, related to the underlying news source. Our objective in this paper is to show that recent advances in statistical learning allow a much more refined analysis of the impact of news on asset prices. In this paper, we demonstrate that information from press releases can be used to predict intraday abnormal returns with relatively high accuracy. We form a text classification problem where press releases are labeled positive if the absolute return jumps at some (fixed) time after the news is made public. First, abnormal returns are predicted using support vector machines in similar fashion to [1]. Given a press release, we predict whether or not an abnormal return will occur in the next 10, 20, ..., 250 minutes using either text or past absolute returns. Our experiments analyze predictability at many horizons and demonstrate significant initial intraday predictability that decreases throughout the trading day. Second, we optimally combine text information with asset price time series to significantly enhance classification performance using multiple kernel learning (MKL).We use an analytic center cutting planemethod (ACCPM) to solve the resultingMKL problem. ACCPM is particularly efficient on problems where the objective function and gradient are hard to evaluate but whose feasible set is simple enough so that analytic centers can be computed efficiently. Furthermore, because it does not suffer from conditioning issues, ACCPM can achieve higher precision targets than other first-order methods.

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