Trade Speed Research Articles

Smart trading: Unlocking Artificial Intelligence in Stock Market

Purpose of research: This research examines how Artificial Intelligence (AI) is reshaping stock market trading, focusing on its applications, benefits, and market impacts. The study aims to understand how AI-driven innovations, including machine learning, predictive analytics, and natural language processing, enhance decision-making processes and improve trading strategies. Design/Methodology: The research takes a conceptual approach by thoroughly reviewing existing studies on how AI is applied in stock trading and comparing these AI-driven methods to traditional trading strategies. It also explores the ethical, technical, and regulatory challenges that come with using AI in trading. Results/Finding: The findings show that AI helps make trading more accurate and efficient, giving users an edge by quickly analysing large amounts of data and responding to market shifts. However, the speed of AI-driven trading can increase market volatility, pointing to the need for strong regulations. The study highlights how AI supports a more accessible and efficient market and helps both institutional and individual investors make smarter, data-informed decisions. Practical Implications: This research provides investors, financial institutions, and policymakers with a clearer understanding of both the benefits and limits of AI-driven trading. It highlights the importance of creating regulations that encourage ethical AI use while supporting innovation, openness, and market stability. AI-based trading systems are driving new competition among brokerage firms. Conclusion: AI is changing the game in stock market trading, making strategies faster, more precise, and accessible to more people. But there are still important challenges, like ensuring data quality, understanding complex AI decisions, and handling ethical concerns. Balancing these factors is key to integrating AI responsibly, creating a path for sustainable growth that can benefit the entire financial world.

E-Commerce: Theoretical and Epistemological Aspect

The radical innovations that led to the structural modernization of the world economy were affected by the development of the digital economy and its main components, among which it is essential to single out e-commerce. After all, it gradually begins to play an important role in the field of trade and the economy, primarily due to its importance and practical use of the assets of scientific and technological progress. Therefore, conducting scientific research to deepen its theoretical and epistemological foundations is timely, allowing us to comprehensively understand its essence and outline the main advantages that various subjects of the national economy receive from its spread. The article aims to clarify the main approaches to understanding the essence of the "electronic commerce" concept, which will allow us to comprehensively characterize the gains from its spread, which society receive. To achieve the goal, several general scientific and specific methods of cognition were used, including analysis and synthesis, content analysis, and systematization. During the study, it was found that the emergence of electronic commerce relates to the active use of the Internet for data transfer and exchange, the development of digital technologies. As a result of the analysis of the scientific literature, five main approaches to its understanding were identified: functional, interactive, component, structural, and static. The value of e-commerce is determined by its advantages because it can create additional impulses for the development of the economy. E-commerce faces certain risks, early consideration of which will allow for minimizing potential losses and creating additional impulses for its growth. In general, digitalization and the active involvement of ICT in economic relations fundamentally change established ways of doing business, affecting not only the increased speed and scale of trade, improved interaction between entities, and allowing companies to enter international markets using new technologies.

A Bayesian theory of market impact

Abstract The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade. In order to reduce the impact on prices due to the execution of large orders, traders in financial markets split large orders into a series of smaller ones, which are executed sequentially. The resulting sequence of trades is called a meta-order. Empirical studies have revealed a non-trivial set of statistical laws on how meta-orders affect prices, which include (i) the square-root behaviour of the expected price variation with the total volume traded, (ii) its crossover to a linear regime for small volumes and (iii) a reversion of average prices towards its initial value, after the sequence of trades is over. Here we recover this phenomenology within a minimal theoretical framework where the market sets prices by incorporating all information on the direction and speed of trade of the meta-order in a Bayesian manner. The simplicity of this derivation lends further support to the robustness and universality of market impact laws. In particular, it suggests that the square-root impact law originates from over-estimation of order flows originating from meta-orders.

The Role of Logistics and Infrastructure in Promoting International Trade

This study aims to explore the role of logistics and infrastructure in promoting international trade. Through systematic analysis of relevant theories and practical impacts, the research finds that logistics and infrastructure play key roles in reducing transaction costs, enhancing trade speed and reliability, and promoting market connectivity and integration. Specifically, an efficient logistics system can optimize transportation, warehousing, distribution, and inventory management, thereby improving the overall efficiency of international trade. Meanwhile, modern transportation, information and communication, and energy infrastructure provide robust support for international trade, facilitating efficient global supply chain operations and economic growth. However, the logistics and infrastructure sectors also face challenges such as infrastructure aging, cross-border logistics barriers, environmental issues, and sustainability concerns. In the future, smart logistics, green logistics, public-private partnerships, and international cooperation will become important development trends. Based on these findings, this paper proposes that governments should formulate supportive policies and improve infrastructure investment mechanisms, while enterprises should enhance logistics optimization and technological innovation. By conducting an in-depth study of the role of logistics and infrastructure in international trade, this research aims to provide theoretical support and practical guidance for policymakers and enterprises, promoting sustainable development in international trade.

The Impact of Speed-Accuracy Instructions on Spatial Congruency Effects.

In many tasks humans can trade speed against accuracy. This variation of strategy has different consequences for congruency effects in different conflict tasks. Recently, Mittelstädt et al. (2022) suggested that these differences are related to the dynamics of congruency effects as assessed by delta plots. With increasing delta plots in the Eriksen flanker task congruency effects were larger under accuracy set, and with decreasing delta plots in the Simon task they were smaller. Here we tested the hypothesis for a single task, making use of the observation that for the Simon task delta plots decline when the irrelevant feature is presented first, but increase when the relevant feature leads. The differences between congruency effects under speed and accuracy instructions confirmed the hypothesized relation to the slope of delta plots. In fact, for similar delta plots in the compared speed-accuracy conditions, the relation should be a straightforward consequence of the shorter and longer reaction times with speed and accuracy set, respectively. However, when relevant and irrelevant features were presented simultaneously, congruency effects were stronger under speed set at all reaction times. For this condition, a supplementary model-based analysis with an extended leaky, competing accumulator model suggested a stronger and longer-lasting influence of the irrelevant stimulus feature. The congruency effects for reaction times were accompanied by congruency effects for error rates when delta plots were decreasing, but not when they were increasing.

The market quality implications of speed in cross-platform trading: Evidence from Frankfurt-London microwave

Exploiting information transmission latency between stock exchanges in Frankfurt and London, and speed-inducing technological upgrades, we show that when cross-market latency arbitrage opportunities are linked to the arrival of information, high-frequency traders' (HFTs’) activities impair liquidity and enhance price discovery by facilitating the incorporation of public information into prices. Conversely, when cross-market latency arbitrage opportunities are driven by liquidity shocks, HFTs improve liquidity and reduce trading costs, thus incentivizing information acquisition and trading with private information. These findings underscore the complex nature of the association between trading speed and market quality and reconcile mixed evidence in the extant literature.

Trading with the crowd

AbstractWe formulate and solve a multi‐player stochastic differential game between financial agents who seek to cost‐efficiently liquidate their position in a risky asset in the presence of jointly aggregated transient price impact, along with taking into account a common general price predicting signal. The unique Nash‐equilibrium strategies reveal how each agent's liquidation policy adjusts the predictive trading signal to the aggregated transient price impact induced by all other agents. This unfolds a quantitative relation between trading signals and the order flow in crowded markets. We also formulate and solve the corresponding mean field game in the limit of infinitely many agents. We prove that the equilibrium trading speed and the value function of an agent in the finite N‐player game converges to the corresponding trading speed and value function in the mean field game at rate . In addition, we prove that the mean field optimal strategy provides an approximate Nash‐equilibrium for the finite‐player game.

A Multi-Blockchain Scheme for Distributed Spectrum Sharing in CBRS System

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference to incumbent users caused by spectrum trading between CBRS devices (CBSDs), and the scalability issues in blockchain-based spectrum trading are rarely discussed. To address the problems above, this paper develops the blockchain-based spectrum trading mechanisms for CBRS. Particularly, we propose a queuing mechanism for intra-coexistence group (CxG) trading, in which spectrum trading is leveraged to reduce the aggregated interference to incumbents. A new parameter termed “network feature” is proposed to prioritize spectrum transactions in different queues, which helps to achieve the trade-off between the interference to incumbents and resource requirements in spectrum transactions. Furthermore, we propose a multi-blockchain architecture and a corresponding cross-chain mechanism to improve the speed of inter-CxG spectrum trading. Simulation results show that the aggregated interference to incumbents can be reduced by adopting the proposed method. Meanwhile, when adopting the proposed cross-chain spectrum trading mechanism, the network throughput can be improved by up to 24% compared with the traditional CBRS spectrum management framework.

Dynamic asset-liability management with frictions

This paper studies a dynamic asset-liability management problem of a company with market frictions. Specifically, the asset prices are modeled by a multivariate geometric Brownian motion with their excess returns driven by some correlated stochastic signals; and the liability process is modeled by another geometric Brownian motion correlated to the asset price dynamics. The company trades dynamically to offset the risks from its liability and each trade induces both temporary and persistent price impacts. We characterize the optimal trading strategies in terms of the solutions to the coupled matrix Riccati differential systems. Due to the price impacts, the company should adopt a target-chasing strategy in which the dynamic target portfolio is expressed in terms of the return-predicting signals and realized liability. We also derive some sufficient conditions, based on the model parameters alone, to ensure the well-posedness of the coupled Riccati systems. Our numerical results indicate that the temporary and persistent price impacts have opposite implications on the company's trading behavior. While the temporary price impact slows down the company's trading speed toward the target portfolio, the persistent price impact may encourage the company to trade more aggressively to enhance the expected returns.

Circuit Design of Counting Electronic Scale Based on STC89C51 Single Chip Microcomputer

The main purpose of electronic scales is to measure, which can also be said to be for trading. Electronic scales have their relevant numerical measurement value restrictions when they are used. Therefore, they can only be used effectively within the measurement value range when they are used. As long as the weight does not exceed the measurement limit, they can be used at will. This measurement method greatly improves the measurement speed and the user's trading speed. STC89C51 single chip microcomputer as an 8-bit single chip microcomputer, first of all, the price is relatively low. The number of pins of 51 single chip microcomputer is up to 40. After subtracting some power supplies and special pins, 32 pins can be used, and all 32 pins can be used as input and output pins. Therefore, the performance price ratio of 51 single chip microcomputer is relatively high. At the same time, the application circuit of 51 single chip microcomputer is relatively simple, and only the crystal oscillator circuit and reset circuit are needed to achieve work. Besides, 51 single chip microcomputer also has serial communication, timer, external interrupt and other on-chip resources, which can meet the special requirements of some systems. Finally, in terms of programming, the 51 single-chip microcomputer can be used only after loading the header file, without configuring the input and output ports, and is also very convenient in programming. Therefore, STC89C51 single chip microcomputer is used as the basis and Proteus and KILL software are used to realize the design of electronic scales.

Tick Control in a Connected World: Challenges, Solutions, and Public Policy from a United States Border Perspective.

Ticks are able to transmit the highest number of pathogen species of any blood-feeding arthropod and represent a growing threat to public health and agricultural systems worldwide. While there are numerous and varied causes and effects of changes to tick-borne disease (re)emergence, three primary challenges to tick control were identified in this review from a U.S. borders perspective. (1) Climate change is implicated in current and future alterations to geographic ranges and population densities of tick species, pathogens they can transmit, and their host and reservoir species, as highlighted by Ixodes scapularis and its expansion across southern Canada. (2) Modern technological advances have created an increasingly interconnected world, contributing to an increase in invasive tick species introductions through the increased speed and frequency of trade and travel. The introduction of the invasive Haemaphysalis longicornis in the eastern U.S. exemplifies the challenges with control in a highly interconnected world. (3) Lastly, while not a new challenge, differences in disease surveillance, control, and management strategies in bordering countries remains a critical challenge in managing ticks and tick-borne diseases. International inter-agency collaborations along the U.S.-Mexico border have been critical in control and mitigation of cattle fever ticks (Rhipicephalus spp.) and highlight the need for continued collaboration and research into integrated tick management strategies. These case studies were used to identify challenges and opportunities for tick control and mitigation efforts through a One Health framework.

Methods to estimate subpixel level small motion from video of vibrating cutting tools

Cutting tools can vibrate with frequencies of up to a few kHz with amplitudes ranging from a few µm to hundreds of µm. To characterize these vibrations using vision-based modal analysis methods, video of vibrating tools must be acquired at high speed and resolution. High speed acquisition will ensure modes are temporally resolved without aliasing, whereas high resolution acquisition ensures that the pixel size is small enough to resolve small motion. However, since most digital cameras trade speed for resolution, estimating small motion of high frequency modes becomes difficult. To address this issue, this paper presents new methods to estimate small motion from video of vibrating cutting tools. Two methods are illustrated on a video of a slender end mill. One is hardware-based, and the other is software-based. In the hardware-based method, we use combinations of extension tubes and a reverse ring fitted with a standard lens to achieve a pixel size of as less as 1.3 µm. In the software-based method, we leverage the capability of intensity- and phase-based motion registration methods to detect small subpixel level motion, even when the pixel size is 83 µm. In both methods we use the object’s own features to detect and register motion, thus overcoming the limitation of digital image correlation schemes that need markers for target tracking. Modal parameters evaluated from motion estimated with both methods are found to agree with those extracted from twice integrated accelerations. Since methods proposed herein proffer new ways to register small oscillatory motion, we believe that our findings will further help leverage the adoption of vision-based modal analysis methods in machine tool systems as an alternative to the more traditional experimental modal analysis procedures.

Exploring Potential Applications of Quantum Computing in Transportation Modelling

The idea that quantum effects could be harnessed to allow faster computation was first proposed by Feynman. As of 2020 we appear to have achieved ‘quantum supremacy’, that is, a quantum computer that performs a given task faster than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum computing to transportation simulation and planning. To date, no such research was found to exist, therefore we begin with an introduction to quantum computing for the programmers of transport models. We discuss existing quantum computing research relevant to transportation, finding developments in network analysis, shortest path computation, multi-objective routing, optimization and calibration – of which the latter three appear to offer the greater promise in future research. Two examples are developed in greater detail, (1) an application of Grover’s quantum algorithm for extracting the mean, which has general applicability towards summarizing distributions which are expensive to compute classically, is applied to an assignment or betweenness model - quantum speedup is elusive in the general case but achievable when trading speed for accuracy for limited outputs; (2) quantum optimization is applied to an activity-based model, giving a theoretically quadratic speedup. Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future remain a challenge due to space overheads imposed by the requirement for reversible computation.

Machine learning and speed in high-frequency trading

The creative destruction wrought by high-frequency algorithmic trading has raised increasing concerns about the effect of machine learning behaviors and ultra high-frequency trading on financial markets. By employing a genetic algorithm with a classifier system as an adaptive learning tool, we address some of these concerns by studying a dynamic limit order market model with asymmetric information and varying speeds of high-frequency trading (HFT). We show that HFT benefits uninformed traders, improves information efficiency but reduces market liquidity. We find that there is a trade-off where a competition effect erodes the information and speed advantages of high-frequency traders, increasing trading speeds of HF traders reduces market liquidity but generates a hump-shaped relationship to the profitability of high-frequency traders and information efficiency. This research finds there may be potential benefits to throttling the trading speed arms race to improve market efficiency. We also find that strategic algorithmic trading compensates for diminishments in speed advantages, providing an insight on machine behavior in the FinTech age.

Comments on “the role of information in a continuous double auction: An experiment and learning model” by Mikhail Anufriev, Jasmina Arifovic, John Ledyard and Valentyn Panchenko

Anufriev et al. study whether more detailed order book information is always better for market efficiency in a modified continuous double auction market. This paper belongs to a broad research agenda on the impact of information transparency and trading speed on market stability. This comment discusses the implication of this research agenda on the design of market institutions and regulatory policies with the rise of mobile trading platforms and algorithm trading.

Discussion on “Optimally Sticky Prices: Foundations (by Jean-Paul L’Hullier and William R. Zame)”

Anufriev et al. study whether more detailed order book information is always better for market efficiency in a modified continuous double auction market. This paper belongs to a broad research agenda on the impact of information transparency and trading speed on market stability. This comment discusses the implication of this research agenda on the design of market institutions and regulatory policies with the rise of mobile trading platforms and algorithm trading.

Evaluation of the Speed, Accuracy and Precision of the QuickMIC Rapid Antibiotic Susceptibility Testing Assay With Gram-Negative Bacteria in a Clinical Setting.

The rapidly changing landscape of antimicrobial resistance poses a challenge for empirical antibiotic therapy in severely ill patients and highlights the need for fast antibiotic susceptibility diagnostics to guide treatment. Traditional methods for antibiotic susceptibility testing (AST) of bacteria such as broth microdilution (BMD) or the disc diffusion method (DDM) are comparatively slow and show high variability. Rapid AST methods under development often trade speed for resolution, sometimes only measuring responses at a single antibiotic concentration. QuickMIC is a recently developed lab-on-a-chip system for rapid AST. Here we evaluate the performance of the QuickMIC method with regard to speed, precision and accuracy in comparison to traditional diagnostic methods. 151 blood cultures of clinical Gram-negative isolates with a high frequency of drug resistance were tested using the QuickMIC system and compared with BMD for 12 antibiotics. To investigate sample turnaround time and method functionality in a clinical setting, another 41 clinical blood culture samples were acquired from the Uppsala University Hospital and analyzed on site in the clinical laboratory with the QuickMIC system, and compared with DDM for 8 antibiotics routinely used in the clinical laboratory. The overall essential agreement between MIC values obtained by QuickMIC and BMD was 83.4%, with an average time to result of 3 h 2 min (SD: 24.8 min) for the QuickMIC method. For the clinical dataset, the categorical agreement between QuickMIC and DDM was 96.8%, whereas essential and categorical agreement against BMD was 91.0% and 96.7%, respectively, and the total turnaround time as compared to routine diagnostics was shown to be reduced by 40% (33 h vs. 55 h). Interexperiment variability was low (average SD: 44.6% from target MIC) compared to the acceptable standard of ±1 log2 unit (i.e. -50% to +100% deviation from target MIC) in BMD. We conclude that the QuickMIC method can provide rapid and accurate AST, and may be especially valuable in settings with high resistance rates, and for antibiotics where wildtype and antibiotic-resistant bacteria have MIC distributions that are close or overlapping.

Video Streaming Based on Blockchain State Channel with IoT Camera

This paper proposes a system that provides video streaming services from the Internet of Media Things using blockchain and cryptocurrency (token). The user pays the token by the contract terms of the smart contract written on the blockchain through the distributed application (DApp). The IP camera, which is paid the token, streams the taken video to the user in real-time. To investigate the possibility of a blockchain camera streaming service, we upload a smart contract for streaming service on Etherium-based blockchain, and ERC20 tokens necessary for the transaction are created and implemented. To overcome the slow trading speed and the disability of proper refunding, the off-chain transaction, one of the blockchain scaling techniques, was applied and implemented in the system.

Emerging Zoonotic Infections, Social Processes and Their Measurement and Enhanced Surveillance to Improve Zoonotic Epidemic Responses: A "Big Events" Perspective.

Zoonotic epidemics and pandemics have become frequent. From HIV/AIDS through COVID-19, they demonstrate that pandemics are social processes as well as health occurrences. The roots of these pandemics lie in changes in the socioeconomic interface between humanity and non-human host species that facilitate interspecies transmission. The degree to which zoonoses spread has been increased by the greater speed and extent of modern transportation and trade. Pre-existing sociopolitical and economic structures and conflicts in societies also affect pathogen propagation. As an epidemic develops, it can itself become a social and political factor, and change and interact with pre-existing sociobehavioral norms and institutional structures. This paper uses a “Big Events” approach to frame these processes. Based on this framework, we discuss how social readiness surveys implemented both before and during an outbreak might help public health predict how overall systems might react to an epidemic and/or to disease control measures, and thus might inform interventions to mitigate potential adverse outcomes or possibly preventing outbreaks from developing into epidemics. We conclude by considering what “pathways measures”, in addition to those we and others have already developed, might usefully be developed and validated to assist outbreak and epidemic disease responses.

Robotics and Algorithmic Trading: A New Era in Stock Market Trend Analysis

This paper uses machine learning to examine how robots and algorithmic trading have transformed stock market trend analysis. The main goals are to assess how these sophisticated systems improve prediction accuracy, trading efficiency, market liquidity, and their problems and policy consequences. The research synthesizes academic, industrial, and technical literature using secondary sources. Significant results show that robots and algorithmic trading have enhanced trading speed, accuracy, and market efficiency while increasing market volatility data quality and model overfitting issues. Machine learning improves trend analysis by spotting complicated patterns and improving trading techniques. These advances need solid regulatory frameworks to control risks, including market instability and ethical issues. Policy implications include circuit breakers and transparency standards to promote fair and stable markets. This study emphasizes balancing technology innovation with regulation to provide a safe and fair trade environment.