Journal articles: 'Algorithmic exchange'

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Haque, Md Asif Ul, Satyam Asthana, Mohit Kumar Jha, Darshan Deshmukh, and Prof Sandhya Gundre. "Stock Market Prediction Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4633–36. http://dx.doi.org/10.22214/ijraset.2022.43344.

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Abstract: Lately, numerous institutional financial backers utilize algorithmic exchanging to finish their speculation choices. This technique decreases the exchange costs, and further develops the venture return. Algorithmic exchanging is another exchange mode. This paper presents the algorithmic exchanging and the improvement cycle, presents the advancement interaction of exchanges costs, audits the most recent exploration literary works of algorithmic exchanging technique, and presents the writings of venture portfolio choice. In light of the current exploration and the ongoing circumstance of algorithmic exchanging our country, this paper fosters the application and idea for algorithmic exchanging Keywords: Neuron-Fuzzy systems, LSTM, Artificial neural network, Hidden Markov model, Data mining, Stock market prediction, TSLM and RNN.

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AYDIN, Ayser. "AN ALGORITHMIC TRADING APPLICATION IN CRYPTO EXCHANGE." JOURNAL OF ACADEMIC SOCIAL RESOURCES 6, no. 29 (2021): 1269–73. http://dx.doi.org/10.31569/asrjournal.306.

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Athanasiou, George, and Stelios Kotsios. "An algorithmic approach to exchange rate stabilization." Economic Modelling 25, no. 6 (2008): 1246–60. http://dx.doi.org/10.1016/j.econmod.2008.04.001.

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Kabari, Ledisi Giok, Marcus B. Chigoziri, and Joseph Eneotu. "Machine Learning Algorithmic Study of the Naira Exchange Rate." European Journal of Engineering Research and Science 5, no. 2 (2020): 183–86. http://dx.doi.org/10.24018/ejers.2020.5.2.1739.

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In this study, we discuss various machine learning algorithms and architectures suitable for the Nigerian Naira exchange rate forecast. Our analyses were focused on the exchange rates of the British Pounds, US Dollars and the Euro against the Naira. The exchange rate data was sourced from the Central Bank of Nigeria. The performances of the algorithms were evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error and the coefficient of determination (R-Squared score). Finally, we compared the performances of these algorithms in forecasting the exchange rates.

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Kabari, Ledisi Giok, Marcus B. Chigoziri, and Joseph Eneotu. "Machine Learning Algorithmic Study of the Naira Exchange Rate." European Journal of Engineering and Technology Research 5, no. 2 (2020): 183–86. http://dx.doi.org/10.24018/ejeng.2020.5.2.1739.

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In this study, we discuss various machine learning algorithms and architectures suitable for the Nigerian Naira exchange rate forecast. Our analyses were focused on the exchange rates of the British Pounds, US Dollars and the Euro against the Naira. The exchange rate data was sourced from the Central Bank of Nigeria. The performances of the algorithms were evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error and the coefficient of determination (R-Squared score). Finally, we compared the performances of these algorithms in forecasting the exchange rates.

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Batiuk, B. V. "Problems and prospects of algorithmic trade in financial markets." Entrepreneur’s Guide 13, no. 2 (2020): 9–16. http://dx.doi.org/10.24182/2073-9885-2020-13-2-9-16.

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The use of algorithms in trading (algorithmic trading) is the trend of recent decades, which has largely changed the market. As part of the research, the fundamentals of algorithmic trading, it’s possible application during exchange trading, were examined in detail. An assessment was also made of the accessibility of obtaining exchange robots for the corporate sector, the benefits of use and optimal conditions for use. Moreover, the impact of exchange trading on the global economy is valuable and a forecast is made for the further development of trading robots.

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Zhalezka, B., A. Stadnik, and V. Siniauskaya. "TECHNICAL ANALYSIS AND INDICATORS APPLICATION IN ALGORITHMIC MARKETING." Экономическая наука сегодня, no. 15 (May 19, 2022): 119–30. http://dx.doi.org/10.21122/2309-6667-2022-15-119-130.

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This paper is devoted to the investigation of the stock exchange decision making process by means of technical analysis on the base of indicators. Methods of the basic technical indicators calculation are considered. Trading strategy of the stock exchange decision making based on the new complex specific technical indicator is suggested, which is accounting values of the following basic technical indicators: moving average convergence divergence, parabolic stop and reverse, average directional moving index rating, momentum volume weighted average price. An example of trading strategy automation is considered. Its back testing is realized, which confirms correctness of its application in the stock exchange decision making.

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Garcia, David, and Frank Schweitzer. "Social signals and algorithmic trading of Bitcoin." Royal Society Open Science 2, no. 9 (2015): 150288. http://dx.doi.org/10.1098/rsos.150288.

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The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

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Kondratieva, T., L. Prianishnikova, and I. Razveeva. "Machine learning for algorithmic trading." E3S Web of Conferences 224 (2020): 01019. http://dx.doi.org/10.1051/e3sconf/202022401019.

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The purpose of the study is to confirm the feasibility of using machine learning methods to predict the behavior of the foreign exchange market. The article examines the theoretical and practical aspects of the implementation of artificial neural networks in the process of Internet trading. We studied the features of constructing automated trading advisors that perform trading operations based on the forecast of neural networks in combination with indicator signals. As a result, a hybrid system has been built that has a high-precision forecast and allows you to make a profit with the correct selection of parameters.

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Teodorovic, Natasa. "Liquidity, price impact and trade informativeness: Evidence from the London stock exchange." Ekonomski anali 56, no. 188 (2011): 91–123. http://dx.doi.org/10.2298/eka1188091t.

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The rapid development of electronic trading has significantly changed stock exchange markets. Electronic systems providing trading processes have defined a new stock market environment. Such a new environment requires trading process redefinition (generally defined as algorithmic trading), as well as redefinition of well known microstructure hypotheses. This paper conducts standard Hasbrouck?s (1991a, 1991b) market microstructure time series analysis to examine adverse selection and information asymmetry issues on diverse liquidity leveled stocks listed on the London Stock Exchange, which is a market with a significant algorithmic trading share. Based on the results obtained from the considered sample, this paper suggests that the contribution of unexpected trade in the volatility of the efficient price is larger for intensively traded stocks, arguing that Hasbrouck?s (1991a, 1991b) model recognizes algorithmic trading as an unexpected trade, i.e. as a trade caused by superior information.

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CARTEA, ÁLVARO, and SEBASTIAN JAIMUNGAL. "ALGORITHMIC TRADING OF CO-INTEGRATED ASSETS." International Journal of Theoretical and Applied Finance 19, no. 06 (2016): 1650038. http://dx.doi.org/10.1142/s0219024916500382.

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We assume that the drift in the returns of asset prices consists of an idiosyncratic component and a common component given by a co-integration factor. We analyze the optimal investment strategy for an agent who maximizes expected utility of wealth by dynamically trading in these assets. The optimal solution is constructed explicitly in closed-form and is shown to be affine in the co-integration factor. We calibrate the model to three assets traded on the Nasdaq exchange (Google, Facebook, and Amazon) and employ simulations to showcase the strategy’s performance.

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B., Aishwarya, P. Kavin K., Abhinav S., and Krithikdharan. "Binary Trading Bot with Arbitrage Algorithm." Binary Trading Bot with Arbitrage Algorithm 8, no. 11 (2023): 6. https://doi.org/10.5281/zenodo.10164418.

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The advent of algorithmic trading has revolutionized financial markets, introducing sophisticated strategies capable of capitalizing on market inefficiencies. Our project presents a novel trading bot that leverages an arbitrage algorithm to identify and exploit price discrepancies in different markets, thereby achieving risk-free profit opportunities. Our trading bot employs real-time market data collection from multiple exchanges, scrutinizing the prices of the same asset across different platforms. Upon detecting a significant price gap, the bot swiftly triggers trades to buy the asset at the lower-priced exchange and simultaneously sell it at the higher-priced exchange. Careful risk assessment, accounting for transaction fees, slippage, and transfer times between exchanges, ensures the profitability of each arbitrage opportunity.Keywords:- Arbitrage, Real Time data, Scrutinize.

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Jarrahi, Mohammad Hossein, Gemma Newlands, Min Kyung Lee, Christine T. Wolf, Eliscia Kinder, and Will Sutherland. "Algorithmic management in a work context." Big Data & Society 8, no. 2 (2021): 205395172110203. http://dx.doi.org/10.1177/20539517211020332.

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The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

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Șerban, Florentin, and Bogdan-Petru Vrînceanu. "A Comparison Between Trend Following (Tidy Little Robot – TLR) and Reversal Trading (Dirty Little Robot – DLR) Algobot Strategies." European Journal of Theoretical and Applied Sciences 1, no. 5 (2023): 805–22. http://dx.doi.org/10.59324/ejtas.2023.1(5).68.

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Algorithmic trading has become a defining force in modern financial markets, offering traders precision, speed, and automation. This article explores the world of algorithmic trading through the lens of two distinct algobots, Tidy Little Robot (TLR) and Dirty Little Robot (DLR). Developed using Pine Script 5 on the TradingView platform and implemented on the Pionex exchange, these algobots navigate the BTC/USDT perpetual market with a 1-minute timeframe. TLR excels in trend-following scenarios, while DLR specializes in identifying reversals. The study delves into their performance, methodology, implications, interpretations, limitations, and future research prospects, offering valuable insights into the evolving landscape of algorithmic trading.

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Florentin, Șerban, and Vrînceanu Bogdan-Petru. "A Comparison Between Trend Following (Tidy Little Robot – TLR) and Reversal Trading (Dirty Little Robot – DLR) Algobot Strategies." European Jornal of Theoretical and Sciences 1, no. 5 (2023): 805–22. https://doi.org/10.59324/ejtas.2023.1(5).68.

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Algorithmic trading has become a defining force in modern financial markets, offering traders precision, speed, and automation. This article explores the world of algorithmic trading through the lens of two distinct algobots, Tidy Little Robot (TLR) and Dirty Little Robot (DLR). Developed using Pine Script 5 on the TradingView platform and implemented on the Pionex exchange, these algobots navigate the BTC/USDT perpetual market with a 1-minute timeframe. TLR excels in trend-following scenarios, while DLR specializes in identifying reversals. The study delves into their performance, methodology, implications, interpretations, limitations, and future research prospects, offering valuable insights into the evolving landscape of algorithmic trading.

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Smeulders, Bart, Valentin Bartier, Yves Crama, and Frits C. R. Spieksma. "Recourse in Kidney Exchange Programs." INFORMS Journal on Computing 34, no. 2 (2022): 1191–206. http://dx.doi.org/10.1287/ijoc.2021.1099.

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We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational efficiency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally difficulty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models.

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Xianghan, Zhang, and Li Zhengwei. "Research on the Impact of Algorithmic Management on Employee Work Behavior in Platform Enterprises." Journal of Economics and Management Sciences 8, no. 2 (2025): p99. https://doi.org/10.30560/jems.v8n2p99.

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This paper, set against the backdrop of platform enterprises, examines the impact of algorithmic management on employee work behavior. Through a literature review and theoretical construction, the study first defines the core concepts of platform enterprises, algorithmic management, and employee work behavior, while outlining related theoretical foundations such as institutional theory, the technology acceptance model, and social exchange theory. Based on this, a conceptual model is developed that describes how algorithmic management influences employee behavior through mediating variables such as trust, sense of control, and performance incentives. The research suggests that while algorithmic management enhances work efficiency and enables personalized incentives, it may also trigger issues such as excessive monitoring, privacy infringements, and a decline in employee autonomy. Finally, the paper discusses how platform enterprises should balance technological applications with humanistic care in practice, offering theoretical insights and references for future empirical research.

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Thomas, Suzanne L., Dawn Nafus, and Jamie Sherman. "Algorithms as fetish: Faith and possibility in algorithmic work." Big Data & Society 5, no. 1 (2018): 205395171775155. http://dx.doi.org/10.1177/2053951717751552.

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Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material form. They mediate emerging distributions of power often too nascent, too slippery or too disconcerting to directly acknowledge. Drawing primarily on 2016 ethnographic research with computer vision professionals, we show how faith in what algorithms can do shapes the social encounters and exchanges of their production. By analyzing algorithms through the lens of fetishism, we can see the social and economic investment in some people’s labor over others. We also see everyday opportunities for social creativity and change. We conclude that what is problematic about algorithms is not their fetishization but instead their stabilization into full-fledged gods and demons – the more deserving objects of critique.

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Engel, Yagil, and Moshe Tennenholtz. "Posted Prices Exchange for Display Advertising Contracts." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 276–82. http://dx.doi.org/10.1609/aaai.v27i1.8656.

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We propose a new market design for display advertising contracts, based on posted prices. Our model and algorithmic framework address several major challenges: (i) the space of possible impression types is exponential in the number of attributes, which is typically large, therefore a complete price space cannot be maintained; (ii) advertisers are usually unable or reluctant to provide extensive demand (willingness-to-pay) functions, (iii) the levels of detail with which supply and demand are specified are often not identical.

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Nitin M. Shivale. "Optimizing Blockchain Protocols with Algorithmic Game Theory." Advances in Nonlinear Variational Inequalities 27, no. 4 (2024): 231–46. http://dx.doi.org/10.52783/anvi.v27.1503.

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Because blockchain technology is changing so quickly, different algorithms have been created to make sure that independent systems are safe, scalable, and efficient. However, as blockchain networks get bigger, protocol efficiency problems get harder to solve. This study looks at how blockchain technology and algorithmic game theory can be used together to solve these problems and suggests a new way to make blockchain systems work better. A part of mathematics called algorithmic game theory studies strategic exchanges where the results depend on the actions of many players. It can instruct us a part almost how to create blockchain frameworks work superior. Able to consider and move forward distinctive parts of convention execution, such as agreement forms, exchange preparing, and network security, by considering of blockchain clients as coherent specialists with their possess objectives. The primary portion of the paper talks approximately essential blockchain conventions, like Confirmation of Work (PoW) and Verification of Stake (PoS), and how they have issues with scaling and proficiency. At that point, we conversation around a few imperative thoughts from algorithmic amusement hypothesis, like Nash equilibrium, component plan, and sell off hypothesis. We appear how these thoughts can be used in blockchain systems. We see at how different game-theoretic strategies can alter how individuals act, make the most excellent utilize of assets, and boost the full execution of systems utilizing complex numerical models and re-enactments. A huge portion of the study is around how game-theoretic forms can be utilized to create assention strategies superior. For illustration, we see at how motivating force structures can be made so that the objectives of mineworkers or validators are adjusted with those of the organize. This brings down the chance of awful behaviour and makes the convention more solid.

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Nazarov, Azizjon. "Liability Mechanisms and Dispute Resolution in Crypto Exchange Contracts: Balancing Code-Based Execution and Legal Enforceability." Uzbek Journal of Law and Digital Policy 2, no. 5 (2024): 11–19. http://dx.doi.org/10.59022/ujldp.223.

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This paper examines the tension between code-based execution and legal enforceability in smart contracts used by cryptocurrency exchanges. As decentralized finance grows in prominence, there is an increasing need to balance the immutability and automation of blockchain-based agreements with traditional legal protections and dispute resolution mechanisms. We analyze current approaches to liability allocation and conflict resolution in major crypto exchanges, identifying key challenges in harmonizing algorithmic governance with existing contract law. Case studies of recent exchange hacks and failures are used to illustrate the limitations of purely code-based systems. We then propose a hybrid model that preserves the efficiency of automated execution while incorporating safeguards for human intervention in exceptional circumstances. This framework aims to enhance user protections, regulatory compliance, and overall trust in decentralized financial infrastructure. Our findings have implications for exchange operators, regulators, and contract law as it evolves to address blockchain-enabled agreements.

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ZINCHENKO, Fedir. "THE DEVELOPMENT OF GLOBAL EXCHANGE NETWORKS." WORLD OF FINANCE, no. 4(57) (2018): 137–47. http://dx.doi.org/10.35774/sf2018.04.137.

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Introduction. For every exchange it is critical to maintain and spread its trading network. The desire to achieve so called liquidity effect is especially important in highly competitive environment in which modern trading platform are operating. The way exchanges expands their networks evolves and differs from country to country. The purposeof the article is to analyze and define the characteristics of exchange networks establishment at the present stage of global capital market development, to specify different forms of integration among regulated markets, especially from developing countries. Results. The article investigates the influence of network or liquidity effects on the strategy of organized stock markets behavior. Two main directions of exchange networks development are revealed: through integration and by use of modern communication tools. Characteristics of different forms of exchanges international integration are defined: from formal cooperation to corporate merger. The specific functions of the most known regional organized stock markets integration are analyzed, by the example of MILA, SEE Link and Stock Connect. The leading role of information and communication providers in the construction of modern global stock exchanges networks has been identified. Conclusions. Advantages that provide a network effect for organized stock markets leads to a different forms of stock exchanges cooperation - from formal agreements to full consolidation and mergers. At the same time, these processes are typical for exchanges from markets with different levels of development. With the rapid development of IT and algorithmic trading, the creation of stock exchange networks has significantly intensified globally. Striving to satisfy the demand of modern computerized traders, exchanges are in close cooperation with communication providers that own and operate world-wide optic-fiber networks.

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Chaboud, Alain P., Benjamin Chiquoine, Erik Hjalmarsson, and Clara Vega. "Rise of the Machines : Algorithmic Trading in the Foreign Exchange Market." International Finance Discussion Paper 2009, no. 980 (2009): 1–46. http://dx.doi.org/10.17016/ifdp.2009.980.

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CHABOUD, ALAIN P., BENJAMIN CHIQUOINE, ERIK HJALMARSSON, and CLARA VEGA. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market." Journal of Finance 69, no. 5 (2014): 2045–84. http://dx.doi.org/10.1111/jofi.12186.

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Lotti, Laura. "Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange." Philosophy & Technology 31, no. 1 (2017): 43–58. http://dx.doi.org/10.1007/s13347-016-0249-8.

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Rahat, Uroosa, Ammar Siddiqui, Khurram Pervez, and Muhammad Hasan. "Impediment in Adaptation of Algorithm Trading: A Case of Frontier Stock Exchange." KIET Journal of Computing and Information Sciences 6, no. 2 (2023): 67–84. http://dx.doi.org/10.51153/kjcis.v6i2.192.

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The global financial markets have been significantly affected by the rapid change in technology. The study is an attempt to get to know the barriers to not adopting algorithmic trading in conventional stock exchanges. This research aims to plan and analytically proposed a model for explaining the reasons why frontier stock exchange traders and investors are hesitant to adopt algorithmic trading as a tool. The research includes variables; Lack of awareness, Trust, Lack of Government interest, unemployment, and unnecessary investment, which were extracted from previously available literature based on the theory of reason and technology acceptance model (TAM). A sample of 50 traders/investors from Pakistan stock markets was taken by using convenience sampling. Data was collected through a questionnaire and analyzed using correlation and linear regression techniques. The results show trust factor is the biggest hurdle in implementing Algorithm Trading which means countries like Pakistan which are following conventional methods for trading in stock markets have great doubts about the efficiency of Algorithm base trading because of the less human interaction and dependency on machines. Fear of miscalculation and the inexperience of data engineers are also one of the reasons conventional stock exchanges are reluctant to adopt algorithm trading. Similarly, variables like Lack of Government interest, unnecessary investment, and employment have a significant effect on the implementation of algorithm trading. Moreover, lack of awareness is the least significant factor, which shows the traders and investors in the Pakistan Stock Exchange are well aware of algorithm trading but the results cannot be generalized to the population due to a limited sample size of the study.

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Kumar, Ramesh. "Understanding the Impact of Algorithmic Trading on Indian Financial Markets: A Quantitative Analysis." Asian Journal of Advanced Research and Reports 19, no. 2 (2025): 64–73. https://doi.org/10.9734/ajarr/2025/v19i2891.

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This paper explores the transformative impact of algorithmic trading on the Indian financial markets, with a focus on market volatility, liquidity, and efficiency. The aim of the paper is to examine the impact of Algorithmic trading on Indian financial market and to assess the role of high-frequency trading in market liquidity and efficiency. In study design, used a mixed-methods approach, the study combines quantitative analysis of historical trading data from the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) with qualitative insights from regulatory filings and industry reports. The study employed the time series analysis, event studies and regression analysis techniques for analyzed the Market volatility (standard deviation of returns), liquidity (bid-ask spread), and trading volumes. The findings highlight significant benefits, including enhanced liquidity, tighter spreads, and improved execution speed. However, challenges persist, such as short-term volatility spikes and the risk of systemic disruptions during flash crashes. Regulatory interventions, like SEBI’s circuit breakers and AI surveillance systems, have mitigated some risks, but ongoing challenges in equitable infrastructure access remain. The study concludes with recommendations to balance technological innovation with market stability, advocating a hybrid approach that integrates algorithmic precision with human oversight to ensure efficiency and resilience in an increasingly automated trading ecosystem.

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Artyushkin, A. Y., P. V. Voronina, and A. V. Tatarinov. "Mathematical software of an optimal control system of heat exchange object with dynamic parameters specified in algorithmic form." Izvestiya MGTU MAMI 7, no. 1-4 (2013): 59–64. http://dx.doi.org/10.17816/2074-0530-67783.

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The article considers technological object of the periodic action principle, which performes the heat exchange between a liquid medium and the refrigerant through the dividing wall of the cooling jacket. Heat exchange process occurs under conditions of natural convection. The optimal control problem is formulated based on the thermodynamics approach. It is shown that the solution must provide a minimum during the process of dissipative average losses. Mathematical and algorithmic maintenance to solve the problem is developed.

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Forman, Christopher, Joachim Henkel, Aija Elina Leiponen, et al. "The Trust Machine? The Promise of Blockchain-Based Algorithmic Governance of Exchange." Academy of Management Proceedings 2019, no. 1 (2019): 13603. http://dx.doi.org/10.5465/ambpp.2019.13603symposium.

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Zhao, Jinhui, Wei Zhang, Tianyu Hu, Ouguan Xu, Shengxiang Yang, and Qichun Zhang. "A Hybrid Mode Membrane Computing Based Algorithm with Applications for Proton Exchange Membrane Fuel Cells." Mathematics 11, no. 14 (2023): 3054. http://dx.doi.org/10.3390/math11143054.

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Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.

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Yarkun, Volodymyr, та Yaroslav Paramud. "Алгоритмічно-програмні засоби синхронізації при обміні даними великих обсягів". Computer systems and network 1, № 1 (2016): 111–18. http://dx.doi.org/10.23939/csn2016.857.111.

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Chernova, Marina A., Leysan M. Sungatullina, and Nikolay A. Malev. "PROSPECTS FOR THE USE OF TECHNICAL INNOVATIONS IN EXCHANGE TRADING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 4/9, no. 145 (2024): 56–62. http://dx.doi.org/10.36871/ek.up.p.r.2024.04.09.009.

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This article analyzes the prospects for the introduction of innovative solutions in stock trading. A separate cluster of investors has formed in the financial market, who use algorithmic systems for their activities. The development of artificial intelligence provides an additional impetus for the development of trading robots and expands the possibilities of their use by different participants. It becomes clear that the financial market is an innovative environment in itself and without the use of modern tools it is impossible to have a discussion about its development.

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Ding, Tian, Xiaojun Yuan, and Soung Chang Liew. "Algorithmic Beamforming Design for MIMO Multiway Relay Channel With Clustered Full Data Exchange." IEEE Transactions on Vehicular Technology 67, no. 10 (2018): 10081–86. http://dx.doi.org/10.1109/tvt.2018.2859312.

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Lee, JeongHoe, and Navid Sabbaghi. "Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets." Digital Finance 2, no. 1-2 (2019): 15–37. http://dx.doi.org/10.1007/s42521-019-00016-9.

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КОРОБОВ, С. А., and К. А. НАДЫБИН. "APPLICATION OF ALGORITHMIC MANAGEMENT TOOLS IN MODERN ECONOMIC CONDITIONS." Экономика и предпринимательство, no. 9(158) (November 18, 2023): 1032–35. http://dx.doi.org/10.34925/eip.2023.158.09.200.

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Стремительное развитие алгоритмов машинного обучения, лежащих в основе современных систем искусственного интеллекта, открыло новые возможности для автоматизации рабочих процессов и функций управления. В предлагаемой статье исследуется алгоритмическое управление как социотехническое явление, которое отражает как взаимодействия ключевых стейкхолдеров бизнес-процессов, так и новые организационные решения по развитию предпринимательских навыков внутри компании. В работе рассмотрены основные ключевые проблемы применения инструментов алгоритмического управления в современных экономических условиях. По мнению авторов статьи, во-первых, алгоритмическое управление формирует новую систему взаимоотношений между рабочими и руководителями, во-вторых, управление алгоритмами требует новых ролей и компетенций персонала организации, в-третьих, алгоритмическое управление более сильно влияет на обмен знаниями и информацией, как на техническом, так и на организационном уровне. В заключение рассмотрены вопросы применения инструментов алгоритмического управления в современных экономических условиях. The rapid development of machine learning algorithms that underlie modern artificial intelligence systems has opened up new opportunities for automating workflows and management functions. This article examines algorithmic management as a sociotechnical phenomenon that reflects both the interactions of key stakeholders in business processes and new organizational solutions for the development of entrepreneurial skills within the company. The paper examines the main key problems of using algorithmic management tools in modern economic conditions. According to the authors of the article, firstly, algorithmic management forms a new system of relationships between workers and managers, secondly, algorithmic management requires new roles and competencies of the organization’s personnel, thirdly, algorithmic management has a stronger impact on the exchange of knowledge and information, as at both technical and organizational levels. In conclusion, the issues of using algorithmic management tools in modern economic conditions are considered.

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Coduto, Joshua Richard, and Johna Leddy. "Taffit: An Algorithm for Fitting Tafel Data." ECS Meeting Abstracts MA2023-01, no. 50 (2023): 2567. http://dx.doi.org/10.1149/ma2023-01502567mtgabs.

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Tafel analysis is widely used to characterize electrochemical kinetics and assess the properties of electrocatalysts for use in fuel cells, electrolyzers, and other applications. This method is limited in part by the subjective determination of linearity, as the kinetic parameters obtained by the regression may vary significantly depending on the chosen linear region. In an effort to increase measurement quality and decrease subjectivity, an algorithm has been developed in Microsoft® Excel® that generates a Tafel plot from an LSV and determines the exchange current density j 0, charge transfer coefficient α, and Tafel slope of closest fit. Comparisons of kinetic parameters between conventional and algorithmic Tafel analysis are made for the hydrogen evolution reaction (HER, 2H+ + 2e- ⇌ H2) for different electrodes. The algorithmic parameters correlate well with conventional methods and show increased measurement precision. Similar agreement is observed between literature and algorithmic fits of representative Tafel plots. The developed algorithm allows for straightforward, rapid, and user bias limited Tafel analysis and can be used to increase measurement quality.

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Sun, Binbin, Shan Pei, Qingjin Wang, and Xuelei Meng. "Understanding the Impact of Algorithmic Discrimination on Unethical Consumer Behavior." Behavioral Sciences 15, no. 4 (2025): 494. https://doi.org/10.3390/bs15040494.

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The prevalence of artificial intelligence (AI) increases social concern surrounding unethical consumer behavior in human–AI interaction. Existing research has mainly focused on anthropomorphic characteristics of AI and unethical consumer behavior (UCB). However, the role of algorithms in unethical consumer behavior, which is central to AI, is not yet fully understood. Drawing on social exchange theory, this study investigates the impact of algorithmic discrimination on UCB and explores the interrelationships and underlying mechanisms. Through three experiments, this study found that experiencing algorithmic discrimination significantly increases UCB, with anticipatory guilt mediating this relationship. Moreover, consumers’ negative reciprocity beliefs moderated the effects of algorithmic discrimination on anticipatory guilt and UCB. In addition, this study distinguish between active and passive UCB based on their underlying ethical motivations. This enhances the study’s universality by assessing both types of behaviors and highlighting their differences. These insights extend current research on UCB within the purview of AI agents and provide valuable insights into effectively mitigating losses caused by UCB behaviors, offering improved directions for facilitating AI agents to provide fair, reliable, and efficient interactions for both businesses and consumers.

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Baek, Seungho, Kwan Yong Lee, Merih Uctum, and Seok Hee Oh. "Robo-Advisors: Machine Learning in Trend-Following ETF Investments." Sustainability 12, no. 16 (2020): 6399. http://dx.doi.org/10.3390/su12166399.

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We examine an application of machine learning to exchange traded fund investments in the U.S. market. To find how the changes in exchange traded fund prices are associated with expected market fundamentals, we propose three parsimonious risk factors extracted from various U.S. economic and market indicators. Based on the information set including these three factors, we build a predictive support vector machine model that can detect long or short investment signals. We find that the high probability of an upward momentum from our forecasting model suggests a long exchange traded fund signal, whereas the low probability of a downward momentum indicates a short exchange traded fund signal. We further design an algorithmic trading system with the support vector machine factor model. We find that the trading system shows practically desirable and robust performances over in-sample and out-of-sample trading periods

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Dix, Guus, Wolfgang Kaltenbrunner, Joeri Tijdink, Govert Valkenburg, and Sarah De Rijcke. "Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management." Politics and Governance 8, no. 2 (2020): 15–25. http://dx.doi.org/10.17645/pag.v8i2.2594.

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Marketization and quantification have become ingrained in academia over the past few decades. The trust in numbers and incentives has led to a proliferation of devices that individualize, induce, benchmark, and rank academic performance. As an instantiation of that trend, this article focuses on the establishment and contestation of ‘algorithmic allocation’ at a Dutch university medical centre. Algorithmic allocation is a form of data-driven automated reasoning that enables university administrators to calculate the overall research budget of a department without engaging in a detailed qualitative assessment of the current content and future potential of its research activities. It consists of a range of quantitative performance indicators covering scientific publications, peer recognition, PhD supervision, and grant acquisition. Drawing on semi-structured interviews, focus groups, and document analysis, we contrast the attempt to build a rationale for algorithmic allocation—citing unfair advantage, competitive achievement, incentives, and exchange—with the attempt to challenge that rationale based on existing epistemic differences between departments. From the specifics of the case, we extrapolate to considerations of epistemic and market fairness that might equally be at stake in other attempts to govern the production of scientific knowledge in a quantitative and market-oriented way.

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Zenil, Hector. "A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions." Entropy 22, no. 6 (2020): 612. http://dx.doi.org/10.3390/e22060612.

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Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.

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Schwalbe, Ulrich. "ALGORITHMS, MACHINE LEARNING, AND COLLUSION." Journal of Competition Law & Economics 14, no. 4 (2018): 568–607. http://dx.doi.org/10.1093/joclec/nhz004.

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Abstract This paper discusses whether self-learning price-setting algorithms can coordinate their pricing behavior to achieve a collusive outcome that maximizes the joint profits of the firms using them. Although legal scholars have generally assumed that algorithmic collusion is not only possible but also exceptionally easy, computer scientists examining cooperation between algorithms as well as economists investigating collusion in experimental oligopolies have countered that coordinated, tacitly collusive behavior is not as rapid, easy, or even inevitable as often suggested. Research in experimental economics has shown that the exchange of information is vital to collusion when more than two firms operate within a given market. Communication between algorithms is also a topic in research on artificial intelligence, in which some scholars have recently indicated that algorithms can learn to communicate, albeit in somewhat limited ways. Taken together, algorithmic collusion currently seems far more difficult to achieve than legal scholars have often assumed and is thus not a particularly relevant competitive concern at present. Moreover, there are several legal problems associated with algorithmic collusion, including questions of liability, of auditing and monitoring algorithms, and of enforcing competition law.

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Vedapradha, R., R. Hariharan, D. David Winster Praveenraj, E. Sudha, Megha Pandey, and Sharath Ambrose. "Algorithm trading and its application in stock broking services." E3S Web of Conferences 376 (2023): 05002. http://dx.doi.org/10.1051/e3sconf/202337605002.

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Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis & Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second.

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Istianatul Ulya, Sadzadia Qothrunnada, and Sarpini. "PENGARUH TEKNOLOGI TERHADAP PERKEMBANGAN PASAR VALUTA ASING." JURNAL AKADEMIK EKONOMI DAN MANAJEMEN 1, no. 4 (2024): 203–17. https://doi.org/10.61722/jaem.v1i4.3324.

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Technological advances have significantly affected various economic sectors, including the foreign exchange (forex) market. This study aims to analyze the impact of technological developments on the dynamics and efficiency of the foreign exchange market by using descriptive qualitative methods. The results of the analysis show that the application of technologies such as algorithmic trading, blockchain, artificial intelligence (AI), and mobile device-based trading applications have improved accessibility, transaction efficiency, as well as risk management capabilities in forex trading. Moreover, technology has also enabled market participants in emerging economies to participate more inclusively. However, the digitalization of forex trading also presents challenges, including increased market volatility, digital asset risk and cyber threats. The study identifies strategic measures such as diversification of foreign exchange reserves, strengthening financial regulations, and the use of derivative instruments as efforts to manage risks and maintain market stability. With continued optimization of technology, the foreign exchange market can serve as a more stable and efficient economic instrument in supporting international trade activities.

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Павлюк, Н. А. "Mathematical and algorithmic models of reconfiguration of a modular robotic system." Вестник КРАУНЦ. Физико-математические науки, no. 4 (December 29, 2020): 122–31. http://dx.doi.org/10.26117/2079-6641-2020-33-4-122-131.

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Обоснована актуальность научной проблемы разработки алгоритмических моделей и программных средств автономного соединения и взаимодействия модульных гомогенных роботов. Представлен обзор существующих модульных робототехнических устройств и модульных робототехнических систем. Рассмотрены разработанные концептуальная и теоретико-множественная модели модульной робототехнической системы. Описаны алгоритмы физического соединения и информационного взаимодействия гомогенных модульных робототехнических устройств при построении связанных пространственных структур. The relevance of a research problem is justified, which consists in development of the algorithmic models and software components for autonomous connection and interaction of the modular homogeneous robots. A review of existing modular robotic devices and modular robotic systems is presented. Developed conceptual and set-theoretic models of a modular robotic system are considered. Algorithms of physical connection and data exchange of homogeneous modular robotic devices are described in context of composition of coupled spatial structures.

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Angstadt, Janet M., Michael T. Foley, Ross Pazzol, and James D. Van De Graaff. "FINRA proposes requiring registration of associated persons who develop algorithmic trading strategies." Journal of Investment Compliance 16, no. 3 (2015): 33–36. http://dx.doi.org/10.1108/joic-06-2015-0042.

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Purpose – To analyze FINRA’s proposal that would require registration with FINRA of associated persons of FINRA-member firms who are primarily responsible for the design, development or significant modification of an algorithmic trading strategy. Design/methodology/approach – This article discusses the rationale and details of the proposed requirements. Findings – If adopted in its current form, the proposed rule-making, particularly when combined with the SEC’s proposed amendments to Rule 15b9-1 under the Securities and Exchange Act, would result in many various individuals who currently are not subject to a FINRA registration requirement, to pass a qualification examination and register. Originality/value – This article contains valuable information about important FINRA rule making activity.

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Phiri, Andrew. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period." Business and Economic Horizons 13, no. 1 (2017): 1–9. https://doi.org/10.15208/beh.2017.01.

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Using weekly data collected from 20.09.2008 to 09.12.2016, this paper uses dynamic threshold adjustment models to demonstrate how the introduction of high-frequency and algorithmic trading on the Johannesburg Stock Exchange (JSE) has altered convergence relations between the federal fund rate and equity returns for aggregate and disaggregate South African market indices. We particularly find that for the post-crisis period, the JSE appears to operate more efficiently, in the weak-form sense, under high frequency trading platforms.

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Foley, Michael T., Janet M. Angstadt, Ross Pazzol, and James D. Van De Graaff. "FINRA rule amendment requires registration of associated persons who develop algorithmic trading strategies." Journal of Investment Compliance 17, no. 3 (2016): 39–41. http://dx.doi.org/10.1108/joic-07-2016-0028.

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Purpose To analyze a recently approved FINRA rule amendment that will require registration with FINRA of associated persons of FINRA-member firms who are primarily responsible for the design, development or significant modification of an algorithmic trading strategy. Design/methodology/approach This article discusses the rationale and details of the proposed requirements. Findings The amended FINRA rule, particularly when combined with the SEC’s proposed amendments to Rule 15b9-1 under the Securities and Exchange Act of 1934, will result in many individuals who currently are not subject to a FINRA registration requirement to pass a qualification examination and register. Originality/value This article contains valuable information about important FINRA rule-making activity.

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Nisha Gurung, MD Rokibul Hasan, Md Sumon Gazi, and Md Zahidul Islam. "Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market." Journal of Business and Management Studies 6, no. 2 (2024): 132–43. http://dx.doi.org/10.32996/jbms.2024.6.2.13.

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In the recent past, algorithmic trading has become exponentially predominant in the American stock market. The principal objective of this research was to explore the employment of machine learning frameworks in formulating algorithmic trading strategies tailored for the US stock market. For this investigation, an array of software tools was employed, comprising the Pandas library for data manipulation and analysis, the Python programming language, the Scikit-learn library for machine learning algorithms and analysis metrics, and the LIME library for explainable AI. In this study, the researcher gathered an extensive dataset from the Amazon Stock Exchange, spanning from October 19, 2018, to October 16, 2022. The dataset comprised a wide range of parameters related to Amazon's stock data, facilitating a rigorous analysis of its market performance. Five models were subjected to the experiment, notably Ridge Regression, Ada-Boost, Light-GBM, XG-Boost, Linear Regression, and Cat-Boost. From the experiment result, it was evident that the XG-Boost attained the highest R-squared (99.24%) and accuracy (99.23%) among all the algorithms. From the above results, the analyst inferred that the XG-Boost was able to learn a more complex and accurate model of the stock exchange data compared to the other algorithms. XG-Boost algorithm can be utilized to back-test distinct trading strategies on historical data, enabling investors to evaluate their efficiency before risking real capital. By assessing a wide array of factors, the XG-Boost algorithm can assist investors in selecting stocks with a higher probability of outperforming the market.

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Buder, Fabian, Koen Pauwels, and Kairun Daikoku. "The Illusion of Free Choice in the Age of Augmented Decisions." NIM Marketing Intelligence Review 13, no. 1 (2021): 46–51. http://dx.doi.org/10.2478/nimmir-2021-0008.

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Abstract In our augmented world, many decision situations are designed by smart technologies. Artificial intelligence helps reduce information overload, filter relevant information and limit an otherwise overwhelming abundance of choices. While such algorithms make our lives more convenient, they also fulfill various organizational objectives that users may not be aware of and that may not be in their best interest. We do not know whether algorithms truly optimize the benefits of their users or rather the return on investment of a company. They are not only designed for convenience but also to be addictive, and this opens the doors for manipulation. Therefore, augmented decision making undermines the freedom of choice. To limit the threats of augmented decisions and enable humans to be critical towards the outcomes of artificial intelligence–driven recommendations, everybody should develop “algorithmic literacy.” It involves a basic understanding of artificial intelligence and how algorithms work in the background. Algorithmic literacy also requires that users understand the role and value of the personal data they sacrifice in exchange for decision augmentation.

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Szabo, Roland. "Mathematical Background and Algorithms of a Collection of Android Apps for a Google Play Store Page." Applied Sciences 15, no. 8 (2025): 4431. https://doi.org/10.3390/app15084431.

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This paper discusses three algorithmic strategies tailored for distinct applications, each aiming to tackle specific operational challenges. The first application unveils an innovative SMS messaging system that substitutes manual typing with voice interaction. The key algorithm facilitates real-time conversion from speech to text for message creation and from text to speech for message playback, thus turning SMS communication into an audio-focused exchange while preserving conventional messaging standards. The second application suggests a secure file management system for Android, utilizing encryption and access control algorithms to safeguard user privacy. Its mathematical framework centers on cryptographic methods for file security and authentication processes to prevent unauthorized access. The third application redefines flashlight functionality using an optimized touch interface algorithm. By employing a screen-wide double-tap gesture recognition system, this approach removes the reliance on a physical button, depending instead on advanced event detection and hardware control logic to activate the device’s flash. All applications are fundamentally based on mathematical modeling and algorithmic effectiveness, emphasizing computational approaches over implementation specifics.

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Journal articles on the topic 'Algorithmic exchange'

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