Financial Multivariate Time Series (Fin-MTS) forecasting is increasingly critical in the financial market. Unlike other Multivariate Time Series (MTS) data, Fin-MTS exhibits particular characteristics, including non-linearity, volatility, and hidden periodicities, which thus introduce great challenges for modelling it well. Existing state-of-the-art models for Fin-MTS forecasting often overlook hidden periodic characteristics, such as credit and monetary policy cycles. More importantly, these models usually show limited capability in well capturing the intra-series and inter-series dynamic information during the modelling process, resulting in significant information loss in quantitative finance modelling and thus limited forecasting performance. To this end, in this paper, we introduce a novel model called Fourier Graph Convolution Transformer (FreTransformer) for Fin-MTS modelling and forecasting. FreTransformer is not only able to well model both the intra- and inter-series dynamic dependencies, but also well capture the important hidden periodicities embedded in Fin-MTS data. FreTransformer first maps the original time domain data into the frequency domain to disclose the hidden periodicities and then employs a novel Fourier Graph Convolution Network to well capture the complex intra- and inter-series dependencies within Fin-MTS. Extensive experiments on real-world US market data across 12 phases demonstrate that our method outperforms current state-of-the-art models. Our source code is publicly available at this repository: https://github.com/AmsonntagChow/FreTransformer

Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based crime and social network interactions, behavior coupling contributes to the causes of eventual business problems. Effective approaches for analyzing coupled behaviors are not available, since existing methods mainly focus on individual behavior analysis. This paper discusses the problem of Coupled Behavior Analysis (CBA) and its challenges. A Coupled Hidden Markov Model (CHMM)-based approach is illustrated to model and detect abnormal group-based trading behaviors. The CHMM models cater for: 1) multiple behaviors from a group of people, 2) behavioral properties, 3) interactions among behaviors, customers, and behavioral properties, and 4) significant changes between coupled behaviors. We demonstrate and evaluate the models on order-book-level stock tick data from a major Asian exchange and demonstrate that the proposed CHMMs outperforms HMM-only for modeling a single sequence or combining multiple single sequences, without considering coupling relationships to detect anomalies. Finally, we discuss interaction relationships and modes between coupled behaviors, which are worthy of substantial study.

Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approaches are available for mining NSP, which mainly rely on re-scanning databases after identifying PSP. As a result, they are very inefficient. In this paper, we propose an efficient algorithm for mining NSP, called e-NSP, which mines for NSP by only involving the identified PSP, without re-scanning databases. First, negative containment is defined to determine whether or not a data sequence contains a negative sequence. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The supports of NSC are then calculated based only on the corresponding PSP. Finally, a simple but efficient approach is proposed to generate NSC. With e-NSP, mining NSP does not require additional database scans, and the existing PSP mining algorithms can be integrated into e-NSP to mine for NSP efficiently. e-NSP is compared with two currently available NSP mining algorithms on 14 synthetic and real-life datasets. Intensive experiments show that e-NSP takes as little as 3% of the runtime of the baseline approaches and is applicable for efficient mining of NSP in large datasets.

The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement.

In capital market surveillance, an emerging trend is that a group of hidden manipulators collaborate with each other to manipulate three trading sequences: buy-orders, sell-orders and trades, through carefully arranging their prices, volumes and time, in order to mislead other investors, affect the instrument movement, and thus maximize personal benefits. If the focus is on only one of the above three sequences in attempting to analyze such hidden group based behavior, or if they are merged into one sequence as per an investor, the coupling relationships among them indicated through trading actions and their prices/volumes/times would be missing, and the resulting findings would have a high probability of mismatching the genuine fact in business. Therefore, typical sequence analysis approaches, which mainly identify patterns on a single sequence, cannot be used here. This paper addresses a novel topic, namely coupled behavior analysis in hidden groups. In particular, we propose a coupled Hidden Markov Models (HMM)-based approach to detect abnormal group-based trading behaviors. The resulting models cater for (1) multiple sequences from a group of people, (2) interactions among them, (3) sequence item properties, and (4) significant change among coupled sequences. We demonstrate our approach in detecting abnormal manipulative trading behaviors on orderbook-level stock data. The results are evaluated against alerts generated by the exchange's surveillance system from both technical and computational perspectives. It shows that the proposed coupled and adaptive HMMs outperform a standard HMM only modeling any single sequence, or the HMM combining multiple single sequences, without considering the coupling relationship. Further work on coupled behavior analysis, including coupled sequence/event analysis, hidden group analysis and behavior dynamics are very critical.

Market Surveillance plays an important role in maintaining market integrity, transparency and fairness. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems are facing challenges of misuse, mis-disclosure and misdealing of information, announcement and order in one market or crossing multiple markets. Therefore, there is a crucial need to develop workable methods for smart surveillance. To deal with such issues, we propose an innovative methodology - microstructure activity pattern analysis. Based on this methodology, a case study in identifying exceptional microstructure activity patterns is carried out. The experiments on real-life stock data show that microstructure activity pattern analysis opens a new and effective means for crucially understanding and analysing market dynamics. The resulting findings such as exceptional microstructure activity patterns can greatly enhance the learning, detection, adaption and decision-making capability of market surveillance.