RESUMEN
Functional objects are large and small physical entities installed in urban environments to offer specific functionalities to visitors, such as shops, escalators, and information kiosks. Instances of the novel notion are focal points of human activities and are significant in pedestrian movement. Pedestrian trajectory modelling in an urban scene is a challenging problem because of the complex patterns resulting from social interactions of the crowds and the diverse relation between pedestrians and functional objects. Many data-driven methods have been proposed to explain the complex movements in urban scenes. However, the methods considering functional objects in their formulation are rare. This study aims to reduce the knowledge gap by demonstrating the importance of pedestrian-object relations in the modelling task. The proposed modelling method, called pedestrian-object relation guided trajectory prediction (PORTP), uses a dual-layer architecture that includes a predictor of pedestrian-object relation and a series of relation-specific specialized pedestrian trajectory prediction models. The experiment findings indicate that the inclusion of pedestrian-object relation results in more accurate predictions. This study provides an empirical foundation for the novel notion and a strong baseline for future work on this topic.
RESUMEN
Limit order books (LOBs) have been widely adopted as a trading mechanism in global securities markets, and the degree of LOB transparency is one of the most studied topics in market design. In the past, this issue was mainly researched through the comparison of LOB transparency in a market before and after a policy change, although such instances were rare and occurred decades ago. This article analyzes the importance of broker identities (IDs) in the LOB with respect to price movement predictability by proposing a different approach. By analyzing raw LOB data, an enormous dataset of selected Hong Kong stocks is divided into two parts, namely the prices and order volumes (anonymous LOBs), and a list of broker IDs in the bid and ask queues. A deep learning model is then employed to predict the mid-price movement after 20 ticks. Our result indicates that the best F1 scores of the anonymous LOB and broker ID models are fairly high, ranging from 57.63% to 68.70% and from 53.70% to 59.39%, respectively. When comparing the performance of both datasets, surprisingly, the overall F1 prediction performance based solely on the broker ID dataset can reach, on average, 85.13% that of the anonymous LOB dataset. The contributions of this study are twofold. First, a machine learning-based tool for finance researchers is proposed to quantitatively measure the price predictability of LOB features, and the results of the impact of LOB transparency on traders' profitability are novel as this study is empirical. Second, the empirical result strongly suggests that the broker ID queues in the LOB consist of significant information content for price prediction, and thus, the study provides insights for regulators to determine the appropriate degree of LOB transparency to guarantee a fair market for all investors.
