Tenyakov Anton, Mamon Rogemar
1Treasury Department, TD Bank Group, Toronto, ON Canada.
2Department of Statistical and Actuarial Sciences, University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7 Canada.
J Big Data. 2017;4(1):46. doi: 10.1186/s40537-017-0106-3. Epub 2017 Dec 11.
This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential between the returns from a pair of stocks, exhibiting a heavy-tailed mean-reverting process is also dealt with. Likewise, the optimal recovery of time-varying parameters in a return-spread model is tackled. It is important to solve such issues in an integrated manner to carry out the execution of trading strategies in a dynamic market environment. The Kalman and hidden Markov model (HMM) multi-regime dynamic filtering approaches are fused together to provide a powerful method for pairs-trading actualisation. Practitioners' considerations are taken into account in the way the new filtering method is automated. The synthesis of the HMM's expectation-maximisation algorithm and Kalman filtering procedure gives rise to a set of self-updating optimal parameter estimates. The method put forward in this paper is a hybridisation of signal-processing algorithms. It highlights the critical role and beneficial utility of data fusion methods. Its appropriateness and novelty support the advancements of accurate predictive analytics involving big financial data sets. The algorithm's performance is tested on historical return spread between Coca-Cola and Pepsi Inc.'s equities. Through a back-testing trade, a hypothetical trader might earn a non-zero profit under the assumption of no transaction costs and bid-ask spreads. The method's success is illustrated by a trading simulation. The findings from this work show that there is high potential to gain when the transaction fees are low, and an investor is able to benefit from the proposed interplay of the two filtering methods.
本文探讨了设计一个高效平台以实时实施配对交易的问题。还处理了捕捉价差过程的典型特征，即一对股票收益之间差异的演变，呈现出重尾均值回归过程。同样，也解决了收益价差模型中时变参数的最优恢复问题。在动态市场环境中执行交易策略时，以综合方式解决此类问题很重要。卡尔曼模型和隐马尔可夫模型（HMM）多状态动态滤波方法融合在一起，为配对交易的实现提供了一种强大的方法。新的滤波方法在自动化过程中考虑了从业者的考量。HMM的期望最大化算法与卡尔曼滤波过程的综合产生了一组自我更新的最优参数估计。本文提出的方法是信号处理算法的一种混合。它突出了数据融合方法的关键作用和有益效用。其适用性和新颖性支持了涉及大型金融数据集的精确预测分析的进步。该算法的性能在可口可乐和百事公司股票的历史收益价差上进行了测试。通过回测交易，假设在无交易成本和买卖价差的情况下，一个假设的交易者可能会获得非零利润。交易模拟说明了该方法的成功。这项工作的结果表明，当交易费用较低时，有很大的获利潜力，并且投资者能够从所提出的两种滤波方法的相互作用中受益。
