Optimal Execution Research Articles

Transient Impact from the Nash Equilibrium of a Permanent Market Impact Game

A large body of empirical literature has shown that market impact of financial prices is transient. However, from a theoretical standpoint, the origin of this temporary nature is still unclear. We show that an implied transient impact arises from the Nash equilibrium between a directional trader and one arbitrageur in a market impact game with fixed and permanent impact. The implied impact is the one that can be empirically inferred from the directional trader’s trading profile and price reaction to order flow. Specifically, we propose two approaches to derive the functional form of the decay kernel of the transient impact model, one of the most popular empirical models for transient impact, from the behavior of the directional trader at the Nash equilibrium. The first is based on the relationship between past order flow and future price change, while in the second we solve an inverse optimal execution problem. We show that in the first approach the implied kernel is unique, while in the second case infinite solutions exist and a linear kernel can always be inferred.

From zero-intelligence to queue-reactive: limit-order-book modeling for high-frequency volatility estimation and optimal execution

Estimators of integrated and spot variance are tested using the queue-reactive model of the limit order book

Improving reinforcement learning algorithms: Towards optimal learning rate policies

AbstractThis paper shows how to use results of statistical learning theory and stochastic algorithms to have a better understanding of the convergence of Reinforcement Learning (RL) once it is formulated as a fixed point problem. This can be used to propose improvement of RL learning rates. First, our analysis shows that the classical asymptotic convergence rate is pessimistic and can be replaced by with , and the number of iterations. Second, we propose a dynamic optimal policy for the choice of the learning rate used in RL. We decompose our policy into two interacting levels: the inner and outer levels. In the inner level, we present the PASS algorithm (for “PAst Sign Search”) which, based on a predefined sequence of learning rates, constructs a new sequence for which the error decreases faster. The convergence of PASS is proved and error bounds are established. In the outer level, we propose an optimal methodology for the selection of the predefined sequence. Third, we show empirically that our selection methodology of the learning rate outperforms significantly standard algorithms used in RL for the three following applications: the estimation of a drift, the optimal placement of limit orders, and the optimal execution of a large number of shares.

PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification

Multi-label classification has recently attracted research interest as a data mining task. Many current applications in data mining address problems that have instances belonging to more than one class. This requires the development of new efficient methods. Instance selection has been used in multi-label learning to improve the execution time and classification performance of many learning methods. Following the single-label approach, instance selection has been applied by selecting or unselecting the same instances for all labels. In this paper, we present a different and novel approach. An instance might be useful for some labels and harmful for others; therefore, our algorithm allows each instance to be discarded, selected, or only partially selected for use in the classification of certain labels. An extensive comparison using 45 datasets shows the usefulness of our approach in improving the current instance selection methods for multi-label problems, as well as the ability of our algorithm to compete with other more complex multi-label classification methods.

A RERNN-SGO Technique for Improved Quasi-Z-Source Cascaded Multilevel Inverter Topology for Interfacing Three Phase Grid-Tie Photovoltaic System

In this manuscript, a proficient control strategy-based improved Quasi-Z-Source Cascaded Multilevel Inverter (QZS-CMI) topology for interfacing photovoltaic (PV) system is proposed. The control strategy is joint execution of both recalling-enhanced recurrent neural network (RERNN) and Shell Game Optimization (SGO), therefore it is called RERNN-SGO technique. The major intention of proposed system determined the photovoltaic system efficiency and maximize the power extraction. The interface among load and PV dc source is to accomplish by the QZSI. At first, the objective function is determined depending on the control constraint and parameters, like current, voltage, modulation index, power. These parameters are utilized to propose RERNN-SGO method. The RERNN-SGO method is used to improve the power delivery, voltage profile and minimum power oscillation for power distribution with load. The maximal power delivered the load is to ensure the artificial intelligence (AI) strategy with the help of maximal power point tracking (MPPT). To regulate shoot through duty ratio and modulation load, the proposed AI method is used. The proposed system is inspired on MATLAB/Simulink site, and then efficiency is related with existing systems under various load conditions. The efficiency of proposed system in Case 1 and Case 2 is 87.363% and 85.3904%.

Facilitators and barriers to conducting an efficient, competent and high-quality oncological multidisciplinary team meeting

BackgroundOptimal oncological care nowadays requires discussing every patient in a multidisciplinary team meeting (MDTM). The number of patients to be discussed is rising rapidly due to the increasing incidence and...

A provably-secure authenticated key agreement protocol for remote patient monitoring IoMT

With the continuous improvement of the public medical system, the current medical service industry is more intelligent. The Internet-of-Medical-Things (IoMT) plays a significant role in smart medicine. The emergence of remote wireless monitoring platforms in the IoMT systems has significantly reduced the risk of virus infection among medical staff. However, data may be easily stolen by criminals during the transmission process. We propose a three-factor enhanced protocol to monitor patient body information and mitigate the issues. The proposed protocol can provide the confidentiality of transmitted data and the privacy of doctors and patients. The security of the proposed protocol is demonstrated in the Random Oracle Model (ROM) for computational security. In addition, by comparing the proposed protocol with other related works, our designed protocol has efficient performance both in execution time and communication costs.

A discrete-time optimal execution problem with market prices subject to random environments

A discrete-time optimal execution problem with market prices subject to random environments

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

BackgroundDetecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages.Materials and MethodsA dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study.ResultsThe training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05).ConclusionThe present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.

A stand-alone photovoltaic microgrid simulation environment using FPGA hardware acceleration

Microgrids represent a growing paradigm shift from centralised energy generation to a distributed model. However, given the relative novelty compared to traditional grids, there are still many unknown factors regarding the optimal design of this kind of system. Given this, the study of microgrids can benefit from developing tools designed specifically for such purposes. This paper presents the results of the hardware acceleration of a previously developed stand-alone microgrid simulation environment through an FPGA. The accuracy of the system was validated through a comparison with benchmark software. Execution time performance is measured against the non-hardware accelerated simulation program for microgrid configurations with varying sizes. Real-time simulations are achieved with a reduction of accuracy during transients. Finally, hardware resource usage and further improvements are discussed.

Improving the efficiency of multimodal transportation of chemical products from the Volgograd region to the near and far abroad

Introduction. The industrial sector of Russia is the most important segment of the country’s economy. Until the beginning of the 21st century, large chemical enterprises in Russia were focused on the domestic market. There was a so-called supply strategy formed back in the USSR for the Union Socialist republics. The products of the chemical industry were mainly transported by road around the city and to neighboring regions, the main cargo traffic for longer distances was mastered by rail and river transport. Currently, the largest chemical industry enterprise in the Volgograd region, AO Kaustik, is actively exporting to Europe, South America and Asia. To carry out international transportation of the company’s products, hired tractors with semi-trailers that meet all modern standards of environmental safety are used. The interaction of road transport with rail, river, and sea is actively taking place, effective logistics multimodal systems for the delivery of chemical products are being organized. Road and rail transportation of products is organized with Asian countries, with European countries – by road, rail and sea, with South American countries – by sea. Today, the company’s transport carries out international multimodal transportation to the near and far abroad. The issue of improving the efficiency of road transport is relevant. The paper considers the links of the logistics system of transportation of chemical products of the enterprise between the countries, along the routes: Russia – Netherlands and Russia – Kyrgyzstan. The comparison of road and rail transportation by time and cost on Volgograd – Bishkek route is carried out. The full loading of the vehicle with transport packages according to the calculation takes place in compliance with the loads on the axles of the tractor and semi-trailer. Improving the efficiency of transportation depends on the organization of loading and unloading points, customs control points, movement along routes, and the work of drivers. The statistical regularities of the distribution of the execution time of elements and operations in the loading point of the enterprise are investigated. The ways of organizing the work of motor transport drivers on the international Volgograd – Bishkek – Volgograd route are considered, the calculation of the duration of the elements of the transportation process and their cost is presented.Materials and methods. The paper uses methods of statistical and economic analysis of data on the functioning of the chemical industry in the region. Graph-analytical methods are used to form transport packages and ensure that the car is fully loaded. Methods for determining the shortest distances and drawing up routes on the transport network are used. Methods of organizing the work of drivers are used to ensure single and tour work on the international route. The effectiveness of the proposed measures is evaluated by economic methods.Results. It is revealed that the chemical industry of the region has a stable economic situation that allows it to develop new international markets. The links of the multimodal logistics system for the delivery of the company’s products along Volgograd – Bishkek and Volgograd – Netherlands international routes are investigated. Improving the efficiency of transportation depends on the organization of loading and unloading points. A statistical analysis of the elements and stages of the transportation process of caustic soda was carried out. A scheme for placing transport packages with bags of caustic soda in a semi-trailer of a motor vehicle has been developed. International routes of transportation of products of AO Kaustik using road transport have been developed: Volgograd – Bishkek and Volgograd – Netherlands routes. The mode of operation of motor transport when performing international transportation using tour and single organization of drivers’ work is calculated. We recommend a tour trip as the most effective way to organize the work of drivers. Tour riding is recommended as the most effective way of organizing the work of road transport in the organization of international transportation to near and far abroad. The final price of 1 ton of transported products on the Volgograd – Bishkek route has been determined. The effective functioning of road transport in the international logistics system for the delivery of caustic soda allows you to reduce transport time, transport costs and reduce the cost of a unit of production for the end user.Conclusions. The article develops measures to improve the organization of international transportation of products of JSC «Caustic» using road transport. The final price of 1 ton of products in the Bishkek warehouse, taking into account the initial cost of granular caustic soda and transportation costs in the case of organizing a single driver’s ride, will be 83246 rubles/ton, and for tour driving, respectively, 81355 rubles/ton.

Graph partitioning MapReduce-based algorithms for counting triangles in large-scale graphs

Counting number of triangles in the graph is considered a major task in many large-scale graph analytics problems such as clustering coefficient, transitivity ratio, trusses, etc. In recent years, MapReduce becomes one of the most popular and powerful frameworks for analyzing large-scale graphs in clusters of machines. In this paper, we propose two new MapReduce algorithms based on graph partitioning. The two algorithms avoid the problem of duplicate counting triangles that other algorithms suffer from. The experimental results show a high efficiency of the two algorithms in comparison with an existing algorithm, overcoming it in the execution time performance, especially in very large-scale graphs.

ПРОБЛЕМА АВТОМАТИЗИРОВАННОЙ ДЕТЕКЦИИ ВИКТИМНОГО ПОВЕДЕНИЯ ИНДИВИДА С ТОЧКИ ЗРЕНИЯ КАЧЕСТВА СЕРВИСА

This article discusses the issue of automatic detection of potentially victimized behavior of an individual in a digital environment in terms of the quality of the service provided. An analysis of precedents for detecting the victim behavior demonstrates a connection between the presence of multiple false accounts in an individual and a high potential for the victim behavior. Such an inference makes it possible to detect individuals prone to victimization through the detection of their false accounts. Also, the analysis of recent works in the subject area demonstrates that one of the most effective methods for detecting false aliases in the network is stylometric analysis, which, however, is the most time-consuming and requires the analysis of the largest amount of data. In this regard, the issue of compliance with the quality of services to the declared level in terms of the execution time of operations becomes relevant, which is a non-trivial task when searching and processing information in highly distributed digital environments. The article presents approaches and methods for reducing the execution time of search operations and detection of individuals with the victim behavior by adapting system parameters to search conditions, as well as by parallelizing and placing data processing close to their sources. Experimental calculations are also presented, on the basis of which conclusions are drawn about the appropriateness of using the adaptive properties of systems and elements of the fog computing concept.

Optimal Execution and Speculation With Trade Signals

Optimal Execution and Speculation With Trade Signals

Revisiting Optimal Execution of Portfolio Transactions: A Dynamic Programming and Reinforcement Learning Approach

Revisiting Optimal Execution of Portfolio Transactions: A Dynamic Programming and Reinforcement Learning Approach

Optimal Execution of Lean Six Sigma 4.0 Strategies: AHP-driven Prioritization Approach

Optimal Execution of Lean Six Sigma 4.0 Strategies: AHP-driven Prioritization Approach

Reinforcement Learning for Continuous-Time Optimal Execution: Actor-Critic Algorithm and Error Analysis

Reinforcement Learning for Continuous-Time Optimal Execution: Actor-Critic Algorithm and Error Analysis

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced technology is growing abruptly with the increase in the number of users. One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions. While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud. Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA. To fill this gap, this article proposes an automatic multi-agent framework that ensures the minimization of the SLA violation rate in workload execution. The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent. All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature. With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres. The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization. The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes. The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works. The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.

Rationalisation methods for managing the production processes of textile products from the regulated field

One of the essential features of the unified European market is the free movement of many industrial products, thanks to the harmonisation of the laws of the European Union countries. The rigour of safety regulations has led to a constant increase in the supply and volume of personal protective equipment. The EU legislation defines “essential requirements” for personal protective equipment but does not do the same for technical specifications. From this point of view, products can be divided into non-regulated and regulated products. Protective products and toys are manufactured in the regulated sector in the textile sector and require formal certification under European Community directives. Products intended for military use (in the regulated area) are subject to special, sometimes top-secret rules. Fashionable clothing belongs to the non-regulated area and does not require formal certification. The protective equipment must ensure compliance with EU regulations, such as absolute individual protection against specific risks; durable protective functions; psychological comfort while wearing it; ease of maintenance. The production of textile garments in the regulated sector requires flawless execution with the help of qualified personnel, who have created the physical conditions for the optimal execution of the operations to achieve a constant level of quality. A factor that determines the constant quality level is efficient production management. Production management in the textile industry refers to structuring processes, setting time rules and ensuring efficient working conditions for the people performing the work (rational design of the workplace makes it possible to achieve greater efficiency and meet occupational safety requirements). Once the requirements are clearly defined from the customer’s point of view, their translation into technical measures is a task for the manufacturing company. With this in mind, the main objective of this paper is to provide an example of streamlining the management of production to facilitate the manufacturing process of a product in a regulated sector that is compliant with European regulations. The case study has been carried out for the manufacturing process of a pistol bag.

Optimal order execution under price impact: a hybrid model

In this paper we explore optimal liquidation in a market populated by a number of heterogeneous market makers that have limited inventory-carrying and risk-bearing capacity. We derive a reduced form model for the dynamics of their aggregated inventory considering a proper scaling limit. The resulting price impact profile is shown to depend on the characteristics and relative importance of their inventories. The model is flexible enough to reproduce the empirically documented power law behavior of the price impact function. For any choice of the market makers characteristics, optimal execution within this modeling approach can be recast as a linear-quadratic stochastic control problem. The value function and the associated optimal trading rate can be obtained semi-explicitly subject to solving a differential matrix Riccati equation. Numerical simulations are conducted to illustrate the performance of the resulting optimal liquidation strategy in relation to standard benchmarks. Remarkably, they show that the increase in performance is determined by a substantial reduction of higher order moment risk.