Agent-based Modeling And Market Microstructure

In most modern financial markets, traders express their preferences for assets by making orders. These orders are either executed if a counterparty is willing to match them or collected in a priority queue, called a limit order book. Such markets are said to adopt an order-driven trading mechanism. A key question in this domain is to model and analyze the strategic behavior of market participants, in response to different definitions of the trading mechanism (e.g., the priority queue changed from the continuous double auctions to the frequent call market). The objective is to design financial markets where pernicious behavior is minimized.The complex dynamics of market activities are typically studied via agent-based modeling (ABM) methods, enriched by Empirical Game-Theoretic Analysis (EGTA) to compute equilibria amongst market players and highlight the market behavior (also known as market microstructure) at equilibrium. This thesis contributes to this research area by evaluating the robustness of this approach and providing results to compare existing trading mechanisms and propose more advanced designs.In Chapter 4, we investigate the equilibrium strategy profiles, including their induced market performance, and their robustness to different simulation parameters. For two mainstream trading mechanisms, continuous double auctions (CDAs) and frequent call markets (FCMs), we find that EGTA is needed for solving the game as pure strategies are not a good approximation of the equilibrium. Moreover, EGTA gives generally sound and robust solutions regarding different market and model setups, with the notable exception of agents’ risk attitudes. We also consider heterogeneous EGTA, a more realistic generalization of EGTA whereby traders can modify their strategies during the simulation, and show that fixed strategies lead to sufficiently good analyses, especially taking the computation cost into consideration.After verifying the reliability of the ABM and EGTA methods, we follow this research methodology to study the impact of two widely adopted and potentially malicious trading strategies: spoofing and submission of iceberg orders. In Chapter 5, we study the effects of spoofing attacks on CDA and FCM markets. We let one spoofer (agent playing the spoofing strategy) play with other strategic agents and demonstrate that while spoofing may be profitable in both market models, it has less impact on FCMs than on CDAs. We also explore several FCM mechanism designs to help curb this type of market manipulation even further. In Chapter 6, we study the impact of iceberg orders on the price and order flow dynamics in financial markets. We find that the volume of submitted orders significantly affects the strategy choice of the other agents and the market performance. In general, when agents observe a large volume order, they tend to speculate instead of providing liquidity. In terms of market performance, both efficiency and liquidity will be harmed. We show that while playing the iceberg-order strategy can alleviate the problem caused by the high-volume orders, submitting a large enough order and attracting speculators is better than taking the risk of having fewer trades executed with iceberg orders.We conclude from Chapters 5 and 6 that FCMs have some exciting features when compared with CDAs and focus on the design of trading mechanisms in Chapter 7. We verify that CDAs constitute fertile soil for predatory behavior and toxic order flows and that FCMs address the latency arbitrage opportunities built in those markets. This chapter studies the extent to which adaptive rules to define the length of the clearing intervals — that might move in sync with the market fundamentals — affect the performance of frequent call markets. We show that matching orders in accordance with these rules can increase efficiency and selfish traders’ surplus in a variety of market conditions. In so doing, our work paves the way for a deeper understanding of the flexibility granted by adaptive call markets

An algorithm for fragment-aware virtual network reconfiguration.

In view of the fact that the current online virtual network embedding algorithms do not consider the fragment resources generated in the embedding process deeply enough, resulting in the problem that the acceptance ratio and the revenue to cost ratio are both low, a mathematical model for virtual network reconfiguration is constructed and a heuristic algorithm for fragment-aware virtual network reconfiguration (FA-VNR) is proposed. The FA-VNR algorithm selects the set of virtual nodes to be migrated according to the fragment degrees of the physical nodes, and selects the best virtual node migration scheme according to the reduction of the fragment degrees of the physical nodes as well as the reduction of the embedding cost of the embedded virtual networks. Extensive simulation results show that the proposed FA-VNR algorithm not only can obviously improve the acceptance ratio and the revenue to cost ratio of the current online virtual network embedding algorithm, but also has better optimization effect than the existing virtual network reconfiguration algorithm

Virtual Network Embedding Based on Topology Potential

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio

Transmit and Receive Array Structure Design of Two-Dimensional hybrid Phased-MIMO Radar based on Nested Array

In order to reduce the loss of Degree of Freedom (DOF) brought by the transmit subarray splitting of two-dimensional hybrid phased-MIMO radar, this paper presents a design method of transmitting and receiving array based on nested array structure. Firstly, a two-dimensional hybrid phased-MIMO radar transmitting array based on one-dimensional nested array is presented. On this basis, the receiving end is set as a nested array, and finally a virtual array and difference coarray are formed to expand the number of virtual array elements. The expansion increases the DOF of arrays while preserving the advantages of hybrid phased-MIMO radars. Simulation experiments show that compared with the traditional and coprime hybrid phased-MIMO radar, the proposed method can effectively improve the array DOF and Direction-of-Arrival (DOA) estimation accuracy

Transmit and Receive Array Structure Design of Two-Dimensional hybrid Phased-MIMO Radar based on Nested Array

In order to reduce the loss of Degree of Freedom (DOF) brought by the transmit subarray splitting of two-dimensional hybrid phased-MIMO radar, this paper presents a design method of transmitting and receiving array based on nested array structure. Firstly, a two-dimensional hybrid phased-MIMO radar transmitting array based on one-dimensional nested array is presented. On this basis, the receiving end is set as a nested array, and finally a virtual array and difference coarray are formed to expand the number of virtual array elements. The expansion increases the DOF of arrays while preserving the advantages of hybrid phased-MIMO radars. Simulation experiments show that compared with the traditional and coprime hybrid phased-MIMO radar, the proposed method can effectively improve the array DOF and Direction-of-Arrival (DOA) estimation accuracy

An optimizing nested MIMO array with hole-free difference coarray

According to the newly proposed nested MIMO (Multiple-Input Multiple-Input Multiple Output Multiple Array) array design method, we propose to replace the traditional nested array into an optimizing nested array, ie, to optimizing nested MIMO array design. It not only retains the original advantage of nested MIMO array design closed expression with array element position and degree of freedom(DOF), but also greatly improves the array aperture and DOF. Optimizing nested MIMO array firstly uses the optimizing nested array as the transmitting and receiving arrays, and then make the difference set processing for the coarray of MIMO array (coarray, CA). By properly designing the array spacing of the transmitting and receiving arrays, we can obtain a non-porous difference array. When the total number of array elements is given, by analyzing the characteristics of the array structure, the best array element number of the transmitting and receiving arrays can be obtained. Simulation experiments show that compared with the nested MIMO array design, the proposed method can effectively expand the array aperture, increase the DOF, and increase the DOA estimation accuracy of the MIMO radar without increasing the number of actual array elements

An optimizing nested MIMO array with hole-free difference coarray

According to the newly proposed nested MIMO (Multiple-Input Multiple-Input Multiple Output Multiple Array) array design method, we propose to replace the traditional nested array into an optimizing nested array, ie, to optimizing nested MIMO array design. It not only retains the original advantage of nested MIMO array design closed expression with array element position and degree of freedom(DOF), but also greatly improves the array aperture and DOF. Optimizing nested MIMO array firstly uses the optimizing nested array as the transmitting and receiving arrays, and then make the difference set processing for the coarray of MIMO array (coarray, CA). By properly designing the array spacing of the transmitting and receiving arrays, we can obtain a non-porous difference array. When the total number of array elements is given, by analyzing the characteristics of the array structure, the best array element number of the transmitting and receiving arrays can be obtained. Simulation experiments show that compared with the nested MIMO array design, the proposed method can effectively expand the array aperture, increase the DOF, and increase the DOA estimation accuracy of the MIMO radar without increasing the number of actual array elements

Urban building extraction from high-resolution remote sensing imagery based on multi-scale recurrent conditional generative adversarial network

Urban building extraction from high-resolution remote sensing imagery is important for urban planning, population statistics, and disaster assessment. However, the high density and slight boundary differences of urban building regions pose a great challenge for accurate building extraction. Although existing building extraction methods have achieved better results in urban building extraction, there are still some problems, such as boundary information loss, poor extraction effect for dense regions, and serious interference by building shadows. To accurately extract building regions from high-resolution remote sensing images, in this study, we propose a practical method for building extraction based on convolution neural networks (CNNs). Firstly, the multi-scale recurrent residual convolution is introduced into the generative network to extract the multi-scale and multi-resolution features of remote sensing images. Secondly, the attention gates skip connection (AGs) is used to enhance the information interaction between different scale features. Finally, the adversarial network with parallel architecture is used to decrease the difference between the extracted results and the ground truths. Moreover, the conditional information constraint is introduced in the training process to improve robustness and generalization ability of the proposed method. The qualitative and quantitative analyses are performed on IAILD and Massachusetts datasets. The experimental results show that the proposed method can accurately and effectively extract building regions from remote sensing images