A Hierarchical Futures Data Analysis Framework for Multiple Accounts Detection In futures markets, participators are required to report their Multiple Accounts (MAs) to the market supervisors. However, a small portion of traders tent to use MA to extend their position limit or even manipulate the market prices. To detect these traders, a systematic approach have to be developed to automatically assess the behaviour similarity of each account pair. In this paper, we propose a Hierarchical Futures Data Analysis framework (HierFDA) with multiple data processing components and an unsupervised machine learning model to detect MAs. Since we aim to investigate the whole market with millions of accounts, HierFDA is designed to be memoryefficient and can be easily parallelized. It consists of two layers of components to filter out the pairs of accounts with different levels of similarities. As most of the market participators are u0027regularu0027 individuals and only a tiny portion of the accounts are MA, we apply a deep auto encoder for unsupervised anomaly detection to identify the accounts with extremely high trading similarities. By training with sufficient samples which are not previously identified as MA, the deep neural network can automatically balance the importance of the similarity features on the results. The offline test results show that HierFDA has superior robustness to the sample noise. And we also deploy HierFDA to the production environment of Dalian Commodity Exchange. The online results show that over 97% identified MA groups are approved by the expert and the rest are merged with previous manually discovered groups.

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis Singular spectrum analysis (SSA) is a nonparametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noisefree time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and nonparametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.