A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
Price formation and optimal trading in intraday electricity markets with a major player
我们研究了间歇性可再生能源发电存在下的日内电力市场价格形成。我们认为，设置一个主要生产者可能会与大量的小生产者的战略互动。利用随机控制理论，我们确定了代理商的最佳策略与市场冲击和展示纳什均衡在封闭形式的渐近框架中的平均场游戏与一个主要的球员。这是[Féron，Tankov，and Tinsi，Price formation and optimal trading in the intraday electricity markets，arXiv：2009.04786，2020]的姊妹论文，其中在相同代理人的设置中开发了类似的模型。
Learning the dynamics of technical trading strategies
我们使用基于对抗专家的在线学习算法来学习最大化财富交易零成本投资组合策略所需的最佳参数。学习算法用于确定技术交易策略的相对种群动态，这些策略可以经受住历史回溯测试，并从每日和日内约翰内斯堡证券交易所数据上实施的基础交易策略的集合中形成总体聚合的投资组合交易策略。由此产生的人口时间序列进行调查，使用无监督学习降维和可视化。一个关键的贡献是，使用Jarrow et al.（2012）提出的一种新的假设检验，在每日采样和日内时间尺度上对整体汇总的交易策略进行了统计套利测试。（低频）每日抽样策略未能通过成本后套利检验，而（高频）日内抽样策略没有被证伪为成本后统计套利。交易策略的成功，交易成本和滑点的估计被认为是随着一个在线基准投资组合算法的性能比较。此外，通过使用Bailey等人（2016）引入的非参数程序恢复回测过拟合估计的概率来分析算法的泛化误差。这项工作旨在从数据信息的角度探索和更好地理解不同技术交易策略之间的相互作用。
Deep reinforcement learning for optimal trading with partial information
Scuola Normale Superiore, Pisa（比萨高等师范学院）；Department of Statistical Sciences, University of Toronto（多伦多大学统计科学系）；Oxford-Man Institute for Quantitative Finance, University of Oxford（牛津大学牛津-曼量化金融研究所）；Dipartimento di Matematica, University of Bologna（博洛尼亚大学数学系）
Hidden Order in Trades Predicts the Size of Price Moves
Astrophysics Science Division, NASA Goddard Space Flight Center（NASA戈达德太空飞行中心天体物理学科学部）；Department of Physics, The Catholic University of America（美利坚天主教大学物理系）
