This study aims to address the challenges of financial price prediction in high-frequency trading (HFT) by introducing a novel continual learning framework based on factor predictors via graph neural networks. The model integrates multi-factor pricing theory with real-time market dynamics, effectively bypassing the limitations of conventional time series forecasting methods, which often lack financial theory guidance and ignore market correlations. We propose three heterogeneous tasks, including price gap regression, changepoint detection, and price moving average regression to trace the short-, intermediate-, and long-term trend factors present in the data. We also account for the cross-sectional correlations inherent in the financial market, where prices of different assets show strong dynamic correlations. To accurately capture these dynamic relationships, we resort to spatio-temporal graph neural network (STGNN) to enhance the predictive power of the model. Our model allows a continual learning strategy to simultaneously consider these tasks (factors). To tackle the catastrophic forgetting in continual learning while considering the heterogeneity of tasks, we propose to calculate parameter importance with mutual information between original observations and the extracted features. Empirical studies on the Chinese futures data and U.S. equity data demonstrate the superior performance of the proposed model compared to other state-of-the-art approaches.

It is challenging to predict futures prices with traditional econometric models as it necessitates a comprehensive consideration of both historical observations and correlations among various futures. Spatial-temporal graph neural networks (STGNNs) offer a promising approach for modeling such complex spatio-temporal data. Nevertheless, the direct application of STGNNs to high-frequency futures data remains a challenge, as traders must account for both short- and long-term characteristics when making decisions. To capture these distinct timeframes, we leverage additional label information by devising four heterogeneous tasks: price regression, price gap regression, price moving average regression, and change-point detection. To make full use of these labels, we train the model in a continual manner. Traditional continual GNNs define the gradient of losses as parameter importance to overcome the catastrophic forgetting (CF) issue, while this is unsuitable for our heterogeneous tasks with losses in distinct spaces. Therefore, we propose to calculate the parameter importance with mutual information between the original observations and the extracted features. The empirical results based on 49 varieties of commodity futures demonstrate that our model achieves superior performance compared to the leading approaches, highlighting the effectiveness of our approach in modeling the intricate dynamics of the futures market.

Triangular meshes are commonly used to reconstruct the surfaces of 3-dimensional (3D) objects based on the point cloud data. With an increasing demand for high-quality approximation, the sizes of point cloud data and the generated triangular meshes continue to increase, resulting in high computational cost in data processing, visualization, analysis, transmission and storage. Motivated by the process of sculpture polishing, we develop a novel progressive mesh compression approach, called the greedy 3D-polishing algorithm, to sequentially remove redundant points and triangles in a greedy manner while maintaining the approximation quality of the surface. Based on the polishing algorithm, we propose the approximate curvature radius to evaluate the scale of features polished at each iteration. By reformulating the compression rate selection as a change-point detection problem, a rank-based procedure is developed to select the optimal compression rate so that most of the global features of the 3D object surface can be preserved with statistical guarantee. Experiments on both moderate- and large-scale 3D shape datasets show that the proposed method can substantially reduce the size of the point cloud data and the corresponding triangular mesh, so that most of the surface information can be retained by a much smaller number of points and triangles.

In the multi-armed bandit (MAB) framework, we investigate the problem of learning the means of distributions that are associated with a finite n umber o f a rms under a monotonic constraint. Different from the traditional MAB, our problem involves a parameter constraint and a limited trial budget (i.e., the number of arm pulls is small). However, the number of training samples can be as large as possible through (infinite) simulations, while each training sample is of limited size. This situation arises when some additional information is provided before the trial starts and each arm pull (or testing) could be of extraordinary cost. For example, in cancer dose-finding clinical trials, higher toxicity probabilities are typically associated with higher dose levels (i.e., the monotonic dose–toxicity constraint), and the loss due to the drug’s toxicity, side-effects or death of patients can be enormous. We formulate this problem in the reinforcement learning (RL) paradigm, which is referred to as a bandit arm localization problem. We propose a novel approach in a double deep Q-learning framework, which is integrated with a state-of-the-art statistical model to preserve the parameter constraint and develop a more effective learning strategy. The double deep Q-learning model can be trained with a large (can be as large as infinite) number of simulated trials, which is the first time to cast dose finding in the RL framework. We evaluate the performance of our approach through extensive simulation studies in realistic settings of phase I clinical trials. The proposed double deep Q-learning is shown to outperform the baseline methods in cancer dose-finding trials.

Although self-supervised learning (SSL) has been successfully applied to graph data using graph neural networks (GNNs), most of the existing methods only consider undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth between two connected nodes). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed graphs where information can only be passed along one direction. For example, a directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. To accommodate such an asymmetric structure of directed graphs, we propose a simple yet remarkably effective SSL framework for directed graph analysis to incorporate such one-way information passing. We define an incoming embedding and an outgoing embedding for each node to model its schemes of sending and receiving features respectively. We propose an auxiliary SSL task to predict the existence of the directed edges with the incoming and outgoing embeddings of nodes. The auxiliary SSL task is jointly trained with a downstream primary task that updates nodes’ incoming features and outgoing features in accordance with labels. Extensive experiments on multiple real-world directed graph datasets demonstrate outstanding performances of the proposed self-supervised GNNs in both node-level and graph-level tasks.

By setting a typeface, each character of the Chinese text can be converted to a glyph pixel matrix. We propose to conduct text classification with such glyph features using bi-directional convolution. Although the pixel embedding can be applied to all languages, it is much more convenient to be used to represent Chinese scripts due to the square shape of Chinese characters. We extract both the forward and backward n-gram features of the text via bi-directional convolutional operations and then concatenate them. A subsequent 1-dimensional max-over-time pooling is applied to the bi-directional feature maps, and then three fully connected layers are used for conducting text classification. The proposed model has a light-weight architecture that only contains a single-layer convolutional neural network. Experiments on several Chinese text classification datasets demonstrate surprisingly excellent results for the training speed and superior performance of the proposed model in comparison with traditional methods.

We propose a Bayesian model selection (BMS) boundary detection procedure using non-local prior distributions for a sequence of data with multiple systematic mean changes. By using the non-local priors in the BMS framework, the BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. Further, we speedup the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. From both theoretical and numerical perspectives, we show that the non-local inverse moment prior leads to the fastest convergence rate in identifying the true change points on the boundaries. Extensive simulation studies are conducted to compare the BMS with existing methods, and our method is illustrated with application to the magnetic resonance imaging guided radiation therapy data.