In this paper we introduce a method for significantly improving the signal to noise ratio in financial data. The approach relies on combining a target variable with different context variables and use auto-encoders (AEs) to learn reconstructions of the combined inputs. The objective is to obtain agreement among pairs of AEs which are trained on related but different inputs and for which they are forced to find common ground. The training process is set up as a "conversation" where the models take turns at producing a prediction (speaking) and reconciling own predictions with the output of the other AE (listening), until an agreement is reached. This leads to a new way of constraining the complexity of the data representation generated by the AE. Unlike standard regularization whose strength needs to be decided by the designer, the proposed mutual regularization uses the partner network to detect and amend the lack of generality of the learned representation of the data. The integration of alternative perspectives enhances the de-noising capacity of a single AE and allows us to discover new regularities in financial time-series which can be converted into profitable trading strategies.

翻译：本文提出一种显著提升金融数据信噪比的方法。该方法通过将目标变量与不同上下文变量相结合，并利用自编码器学习组合输入的重构。其核心目标是使多个自编码器在训练过程中达成一致——这些自编码器基于相关但不同的输入进行训练，并被强制寻找共同的数据表征基础。训练过程被构建为一种"对话"机制：模型轮流生成预测（"发言"）并调整自身预测以与其他自编码器的输出保持一致（"聆听"），直至达成共识。这形成了一种约束自编码器数据表征复杂度的新范式。与需要设计者预先确定强度的传统正则化方法不同，所提出的互正则化机制利用伙伴网络来检测并修正数据学习表征中泛化性的不足。通过整合多元视角，该方法增强了单自编码器的去噪能力，使我们能够发现金融时间序列中新的规律性特征，这些特征可转化为具有盈利能力的交易策略。