Relevant bibliographies by topics / Algorithmic exchange

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Algorithmic exchange'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Algorithmic exchange"

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Idvall, Patrik, and Conny Jonsson. "Algorithmic Trading : Hidden Markov Models on Foreign Exchange Data." Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10719.

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<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>
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Song, Yupu. "A Forex Trading System Using Evolutionary Reinforcement Learning." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/1240.

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Building automated trading systems has long been one of the most cutting-edge and exciting fields in the financial industry. In this research project, we built a trading system based on machine learning methods. We used the Recurrent Reinforcement Learning (RRL) algorithm as our fundamental algorithm, and by introducing Genetic Algorithms (GA) in the optimization procedure, we tackled the problems of picking good initial values of parameters and dynamically updating the learning speed in the original RRL algorithm. We call this optimization algorithm the Evolutionary Recurrent Reinforcement Learning algorithm (ERRL), or the GA-RRL algorithm. ERRL allows us to find many local optimal solutions easier and faster than the original RRL algorithm. Finally, we implemented the GA-RRL system on EUR/USD at a 5-minute level, and the backtest performance showed that our GA-RRL system has potentially promising profitability. In future research we plan to introduce some risk control mechanism, implement the system on different markets and assets, and perform backtest at higher frequency level.
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Mozayyan, Esfahani Sina. "Algorithmic Trading and Prediction of Foreign Exchange Rates Based on the Option Expiration Effect." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252297.

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The equity option expiration effect is a well observed phenomenon and is explained by delta hedge rebalancing and pinning risk, which makes the strike price of an option work as a magnet for the underlying price. The FX option expiration effect has not previously been explored to the same extent. In this paper the FX option expiration effect is investigated with the aim of finding out whether it provides valuable information for predicting FX rate movements. New models are created based on the concept of the option relevance coefficient that determines which options are at higher risk of being in the money or out of the money at a specified future time and thus have an attraction effect. An algorithmic trading strategy is created to evaluate these models. The new models based on the FX option expiration effect strongly outperform time series models used as benchmarks. The best results are obtained when the information about the FX option expiration effect is included as an exogenous variable in a GARCH-X model. However, despite promising and consistent results, more scientific research is required to be able to draw significant conclusions.<br>Effekten av aktieoptioners förfall är ett välobserverat fenomen, som kan förklaras av delta hedge-ombalansering och pinning-risk. Som följd av dessa fungerar lösenpriset för en option som en magnet för det underliggande priset. Effekten av FX-optioners förfall har tidigare inte utforskats i samma utsträckning. I denna rapport undersöks effekten av FX-optioners förfall med målet att ta reda på om den kan ge information som kan användas till prediktioner av FX-kursen. Nya modeller skapas baserat på konceptet optionsrelevanskoefficient som bestämmer huruvida optioner har en större sannolikhet att vara "in the money" eller "out of the money" vid en specificerad framtida tidpunkt och därmed har en attraktionseffekt. En algoritmisk tradingstrategi skapas för att evaluera dessa modeller. De nya modellerna baserade på effekten av FX-optioners förfall överpresterar klart jämfört med de tidsseriemodeller som användes som riktmärken. De bästa resultaten uppnåddes när informationen om effekten av FX-optioners förfall inkluderas som en exogen variabel i en GARCH-X modell. Dock, trots lovande och konsekventa resultat, behövs mer vetenskaplig forskning för att kunna dra signifikanta slutsatser.
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Gomolka, Johannes. "Algorithmic Trading : Analyse von computergesteuerten Prozessen im Wertpapierhandel unter Verwendung der Multifaktorenregression." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5100/.

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Die Elektronisierung der Finanzmärkte ist in den letzten Jahren weit vorangeschritten. Praktisch jede Börse verfügt über ein elektronisches Handelssystem. In diesem Kontext beschreibt der Begriff Algorithmic Trading ein Phänomen, bei dem Computerprogramme den Menschen im Wertpapierhandel ersetzen. Sie helfen dabei Investmententscheidungen zu treffen oder Transaktionen durchzuführen. Algorithmic Trading selbst ist dabei nur eine unter vielen Innovationen, welche die Entwicklung des Börsenhandels geprägt haben. Hier sind z.B. die Erfindung der Telegraphie, des Telefons, des FAX oder der elektronische Wertpapierabwicklung zu nennen. Die Frage ist heute nicht mehr, ob Computerprogramme im Börsenhandel eingesetzt werden. Sondern die Frage ist, wo die Grenze zwischen vollautomatischem Börsenhandel (durch Computer) und manuellem Börsenhandel (von Menschen) verläuft. Bei der Erforschung von Algorithmic Trading wird die Wissenschaft mit dem Problem konfrontiert, dass keinerlei Informationen über diese Computerprogramme zugänglich sind. Die Idee dieser Dissertation bestand darin, dieses Problem zu umgehen und Informationen über Algorithmic Trading indirekt aus der Analyse von (Fonds-)Renditen zu extrahieren. Johannes Gomolka untersucht daher die Forschungsfrage, ob sich Aussagen über computergesteuerten Wertpapierhandel (kurz: Algorithmic Trading) aus der Analyse von (Fonds-)Renditen ziehen lassen. Zur Beantwortung dieser Forschungsfrage formuliert der Autor eine neue Definition von Algorithmic Trading und unterscheidet mit Buy-Side und Sell-Side Algorithmic Trading zwei grundlegende Funktionen der Computerprogramme (die Entscheidungs- und die Transaktionsunterstützung). Für seine empirische Untersuchung greift Gomolka auf das Multifaktorenmodell zur Style-Analyse von Fung und Hsieh (1997) zurück. Mit Hilfe dieses Modells ist es möglich, die Zeitreihen von Fondsrenditen in interpretierbare Grundbestandteile zu zerlegen und den einzelnen Regressionsfaktoren eine inhaltliche Bedeutung zuzuordnen. Die Ergebnisse dieser Dissertation zeigen, dass man mit Hilfe der Style-Analyse Aussagen über Algorithmic Trading aus der Analyse von (Fonds-)Renditen machen kann. Die Aussagen sind jedoch keiner technischen Natur, sondern auf die Analyse von Handelsstrategien (Investment-Styles) begrenzt.<br>During the last decade the electronic trading on the stock exchanges advanced rapidly. Today almost every exchange is running an electronic trading system. In this context the term algorithmic trading describes a phenomenon, where computer programs are replacing the human trader, when making investment decisions or facilitating transactions. Algorithmic trading itself stands in a row of many other innovations that helped to develop the financial markets technologically (see for example telegraphy, the telephone, FAX or electronic settlement). Today the question is not, whether computer programs are used or not. The question arising is rather, where the border between automatic, computer driven and human trading can be drawn. Conducting research on algorithmic trading confronts scientists always with the problem of limited availability of information. The idea of this dissertation is to circumnavigate this problem and to extract information indirectly from an analysis of a time series of (fund)-returns data. The research question here is: Is it possible to draw conclusions about algorithmic trading from an analysis of (funds-)return data? To answer this question, the author develops a complete definition of algorithmic trading. He differentiates between Buy-Side and Sell-Side algorithmic trading, depending on the functions of the computer programs (supporting investment-decisions or transaction management). Further, the author applies the multifactor model of the style analysis, formely introduced by Fung and Hsieh (1997). The multifactor model allows to separate fund returns into regression factors that can be attributed to different reasons. The results of this dissertation do show that it is possible to draw conclusions about algorithmic trading out of the analysis of funds returns. Yet these conclusions cannot be of technical nature. They rather have to be attributed to investment strategies (investment styles).
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Xu, Siyao. "Bi-Objective Optimization of Kidney Exchanges." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/62.

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Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best "cure" for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al.~first discussed the classic kidney exchange problem. Freedman et al.~expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work.
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Uematsu, Akira Arice de Moura Galvão. "Algoritmos de negociação com dados de alta frequência." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28042012-114138/.

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Em nosso trabalho analisamos os dados provenientes da BM&F Bovespa, a bolsa de valores de São Paulo, no período de janeiro de 2011, referentes aos índices: BOVESPA (IND), o mini índice BOVESPA (WIN) e a taxa de câmbio (DOL). Estes dados são de alta frequência e representam vários aspectos da dinâmica das negociações. No conjunto de valores encontram-se horários e datas dos negócios, preços, volumes oferecidos e outras características da negociação. A primeira etapa da tese foi extrair as informações necessárias para análises a partir de um arquivo em protocolo FIX, foi desenvolvido um programa em R com essa finalidade. Em seguida, estudamos o carácter da dependência temporal nos dados, testando as propriedades de Markov de um comprimento de memória fixa e variável. Os resultados da aplicação mostram uma grande variabilidade no caráter de dependência, o que requer uma análise mais aprofundada. Acreditamos que esse trabalho seja de muita importância em futuros estudos acadêmicos. Em particular, a parte do carácter específico do protocolo FIX utilizado pela Bovespa. Este era um obstáculo em uma série de estudos acadêmicos, o que era, obviamente, indesejável, pois a Bovespa é um dos maiores mercados comerciais do mundo financeiro moderno.<br>In our work we analyzed data from BM&F Bovespa, the stock exchange in São Paulo. The dataset refers to the month January 2011 and is related to BOVESPA index (IND), mini BOVESPA index (WIN) and the exchange tax (DOL). These, are high frequency data representing various aspects of the dynamic of negotiations. The array of values includes the dates/times of trades, prices, volumes offered for trade and others trades characteristics. The first stage of the thesis was to extract information to the analysis from an archive in FIX protocol, it was developed a program in R with this aim. Afterwards, we studied the character of temporal dependence in the data, testing Markov properties of a fixed and variable memory length. The results of this application show a great variability in the character of dependence, which requires further analysis. We believe that our work is of great importance in future academic studies. In particular, the specific character of the FIX protocol used by Bovespa. This was an obstacle in a number of academic studies, which was, obviously, undesirable since Bovespa is one of the largest trading markets in the modern financial world.
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Stehlik, Milan. "Some Properties of Exchange Design Algorithms Under Correlation." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/994/1/document.pdf.

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In this paper we discuss an algorithm for the construction of D-optimal experimental designs for the parameters in a regression model when the errors have a correlation structure. We show that design points can collapse under the presence of some covariance structures and a so called nugget can be employed in a natural way. We also show that the information of equidistant design on covariance parameter is increasing with the number of design points under exponential variogram, however these designs are not D-optimal. Also in higher dimensions the exponential structure without nugget leads to collapsing of the D-optimal design when also parameters of covariance structure are of interest. However, if only trend parameters are of interest, the designs covering uniformly the whole design space are very efficient. For illustration some numerical examples are also included. (author's abstract)<br>Series: Research Report Series / Department of Statistics and Mathematics
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Rambaldi, Marcello. "Some applications of Hawkes point processes to high frequency finance." Doctoral thesis, Scuola Normale Superiore, 2017. http://hdl.handle.net/11384/85718.

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Gotta, Nancy C. (Nancy Colleen) 1975. "A translation algorithm to solve semantic conflicts in information exchange." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47524.

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Müller, Johannes Christian [Verfasser]. "Auctions in Exchange Trading Systems: Modeling Techniques and Algorithms / Johannes Müller." Berlin : epubli GmbH, 2014. http://d-nb.info/106322747X/34.

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Books on the topic "Algorithmic exchange"

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(Firm), Institutional Investor, ed. Algorithmic trading: Precision, control, execution. Institutional Investor, 2005.

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(Firm), Institutional Investor, ed. Algorithmic trading II: Precision, control, execution. Institutional Investor, 2006.

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Banks, Erik. Dark pools: Off-exchange liquidity in an era of high frequency, program and algorithmic trading. 2nd ed. Palgrave Macmillan, 2014.

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Goldberg, Andrew V. Combinatorial algorithms for the generalized circulation problem. Dept. of Computer Science, Stanford University, 1988.

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Katsiroubas, D. Rapid surface selection and design algorithms for compact exchangers. UMIST, 1993.

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Martins, Tiago, and Rui Neves. Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76680-1.

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Halton, Christopher H. An effectiveness study for prioritization algorithms in a communications node model for the Copernicus Tactical Data Information Exchange System (TADIXS). Naval Postgraduate School, 1997.

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Kaspina, Roza, and Lyubov' Plotnikova. Accounting and taxation of foreign economic activities of organizations. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1018339.

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The tutorial contains practical examples of organization and conducting accounting and tax accounting of foreign economic activity and the examples that reveal the specifics of foreign exchange operations. Given a multivariate system of control of knowledge of students with answers and solution algorithm is a full set of the Fund of assessment tools for current and intermediate control. Used active learning methods in the form of colloquiums, business games, discussions and other interactive forms.&#x0D; Meets the requirements of Federal state educational standards of higher education of the last generation.&#x0D; Designed for students enrolled in our undergraduate and graduate students of economic universities. It can be useful to executives and managers of organizations, chief accountants and economists, auditors in their practical work, as presented in the textbook material allows to solve a dispute.
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Chan, Ernie. Algorithmic Trading: Winning Strategies and Their Rationale. Wiley & Sons, Incorporated, John, 2013.

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Chan, Ernie. Algorithmic Trading: Winning Strategies and Their Rationale. Wiley & Sons, Limited, John, 2014.

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Book chapters on the topic "Algorithmic exchange"

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Chen, Jiehua, Adrian Chmurovic, Fabian Jogl, and Manuel Sorge. "On (Coalitional) Exchange-Stable Matching." In Algorithmic Game Theory. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85947-3_14.

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Dalton, James E. "Algorithmic Execution in Foreign Exchange." In Handbook of Exchange Rates. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118445785.ch21.

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Gradwohl, Ronen. "Price Variation in a Bipartite Exchange Network." In Algorithmic Game Theory. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79309-0_11.

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Babaioff, Moshe, Patrick Briest, and Piotr Krysta. "On the Approximability of Combinatorial Exchange Problems." In Algorithmic Game Theory. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79309-0_9.

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Chen, Zhou, Yukun Cheng, Xiaotie Deng, Qi Qi, and Xiang Yan. "Agent Incentives of Strategic Behavior in Resource Exchange." In Algorithmic Game Theory. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66700-3_18.

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Efsandiari, Hossein, and Guy Kortsarz. "Brief Announcement: New Mechanisms for Pairwise Kidney Exchange." In Algorithmic Game Theory. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48433-3_25.

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Shmyrëv, V. I. "An algorithmic approach for searching an equilibrium in fixed budget exchange models." In Russian Contributions to Game Theory and Equilibrium Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32061-x_12.

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Wadhwa, Deepanshu. "The Development of Leader-Member Exchange Construct and the Emergence of Algorithmic Leader-Member Exchange Construct in the Gig Economy." In Digital Economy Post COVID-19 Era. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0197-5_58.

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Schneier, Bruce. "Key-Exchange Algorithms." In Applied Cryptography, Second Edition. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119183471.ch22.

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Korte, Bernhard, Rainer Schrader, and László Lovász. "General Exchange Structures — Greedoids." In Algorithms and Combinatorics. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58191-5_4.

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Conference papers on the topic "Algorithmic exchange"

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Garms, Lydia, Taofiq Paraiso, Neil Hanley, et al. "Experimental Demonstration of a Hybrid Authenticated Key Exchange Integrating QKD and PQC in a Single Protocol." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qth4b.7.

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Currently deployed public-key infrastructure faces an imminent threat in the face of quantum computers. It is expected that, given sufficiently large and reliable quantum computers, dedicated quantum algorithms such as Shor's algorithm [1] will completely break cryptographic schemes whose security is based on the discrete logarithm and integer factorisation problems. This includes widely deployed cryptographic algorithms such as RSA, Diffie-Hellman and elliptic-curve based approaches which underpin the security of existing electronic communications. Therefore, the secrecy of highly sensitive information currently being exchanged is under significant threat as messages can be recorded in bulk and decrypted in the future in so-called harvest-and-decrypt attacks.
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Wang, Yicai. "An Interactive Information Exchange Method for Campus Social Networks." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019217.

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Garduño-Aguirre, J., R. Bayareh-Mancilla, W. Gómez-Flores, A. Vera-Hernandez, and L. Leija-Salas. "An Algorithm for Supervised Segmentation of Infrared Foot Sole Images Based on a Bayesian Classification Model." In 2025 Global Medical Engineering Physics Exchanges/ Pan American Health Care Exchanges (GMEPE/PAHCE). IEEE, 2025. https://doi.org/10.1109/gmepe/pahce65777.2025.11002788.

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Maiti, Arnab, and Palash Dey. "Parameterized Algorithms for Kidney Exchange." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/58.

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In kidney exchange programs, multiple patient-donor pairs each of whom are otherwise incompatible, exchange their donors to receive compatible kidneys. The Kidney Exchange problem is typically modelled as a directed graph where every vertex is either an altruistic donor or a pair of patient and donor; directed edges are added from a donor to its compatible patients. The computational task is to find if there exists a collection of disjoint cycles and paths starting from altruistic donor vertices of length at most l_c and l_p respectively that covers at least some specific number t of non-altruistic vertices (patients). We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + max{l_p, l_c}, and (3) the number of vertex types in the input graph when l_p &lt;= l_c. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of Kidney Exchange.
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Peters, T. J., N. F. Stewart, D. R. Ferguson, and P. S. Fussell. "Algorithmic tolerances and semantics in data exchange." In the thirteenth annual symposium. ACM Press, 1997. http://dx.doi.org/10.1145/262839.263027.

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Conti, Jean Pierre Jarrier, and Heitor Silvério Lopes. "Algorithmic Trading Using Genetic Algorithms in the Brazilian Stock Exchange." In Congresso Brasileiro de Inteligência Computacional. ABRICOM, 2020. http://dx.doi.org/10.21528/cbic2019-112.

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Maknickienė, Nijolė, Ieva Kekytė, and Algirdas Maknickas. "COMPUTATION INTELLIGENCE BASED DAILY ALGORITHMIC STRATEGIES FOR TRADING IN THE FOREIGN EXCHANGE MARKET." In Business and Management 2018. VGTU Technika, 2018. http://dx.doi.org/10.3846/bm.2018.53.

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Successful trading in financial markets is not possible without a support system that manages the preparation of the data, prediction system, and risk management and evaluates the trading efficien-cy. Selected orthogonal data was used to predict exchange rates by applying recurrent neural network (RNN) software based on the open source framework Keras and the graphical processing unit (GPU) NVIDIA GTX1070 to accelerate RNN learning. The newly developed software on the GPU predicted ten high-low distributions in approximately 90 minutes. This paper compares different daily algorith-mic trading strategies based on four methods of portfolio creation: split equally, optimisation, orthogonality, and maximal expectations. Each investigated portfolio has opportunities and limita-tions dependent on market state and behaviour of investors, and the efficiencies of the trading sup-port systems for investors in foreign exchange market were tested in a demo FOREX market in real time and compared with similar results obtained for risk-free rates.
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Gercekovich, D. A., O. Yu Basharina, I. S. Shilnikova, E. Yu Gorbachevskaya, and S. A. Gorsky. "Information and algorithmic support of a multi-level integrated system for the investment strategies formation." In 3rd International Workshop on Information, Computation, and Control Systems for Distributed Environments 2021. Crossref, 2021. http://dx.doi.org/10.47350/iccs-de.2021.06.

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The article summarizes the accumulated practical experience of the authors in the development of algorithms for the formation of investment strategies. For this purpose, the optimization of the studied parameters, information support of investment activities, verification, monitoring and adjustment in the testing mode and the subsequent practical application of the described tools are considered. The system is based on the main provisions of the Markowitz portfolio theory. The analytical block of the Information System Portfolio Investor includes Profitability-Risk model; empirical models of optimal complexity; hybrid predictive model systems; the principle of combining (integrating) both models and forecasts, as well as decision rules; optimization of the training sample length (modified Markowitz model); optimization of the frequency of monitoring and adjusting the composition of the investment portfolio. The principles of design and development of the information block of the system, its replenishment and functioning are described in detail. All the above listed components of the algorithmic content of the investment decision making system are described sequentially. The system modules have been successfully tested on a wide class of financial instruments: ordinary shares, preferred shares, government and corporate bonds, exchange commodities, stock, commodity, industry and bond indices, exchange-traded investment funds and real estate funds. The implemented Markowitz model with a dynamic database of historical data can significantly increase the efficiency of investment decisions, which is facilitated by taking into account the characteristics of both the markets under study and the corresponding financial instruments.
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Iasbik, Marina Pires, Andressa Carmo Pena Martinez, and Jorge Lira de Toledo Gazel. "Integration of BIM and Algorithmic Design logics through data exchange between Grasshopper plugin and Revit and Archicad software." In Congreso SIGraDi 2020. Editora Blucher, 2020. http://dx.doi.org/10.5151/sigradi2020-65.

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Weber Martins, Thiago, Christian Steinmetz, Katharina Albrecht, and Reiner Anderl. "Web-Based Application for Algorithm Based Product Development Process of Integral Bifurcated Sheet Metal Parts." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59120.

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Within the Collaborative Research Center 666 the algorithm based product development process has been established. It is based on state of the art product development methodologies and enhanced in order to optimize the product development process of integral bifurcated sheet metal parts. Algorithms based on mathematical optimization approaches as well as the initial product requirements and constraints information are applied to obtain an optimized design as CAD-Model. Regarding this methodology there are still some challenges to be solved, such as reduction of iterations steps to elaborate final product design as CAD-model, use of heterogeneous data as well as software and enhancement of information exchange. Therefore, this paper introduces a concept for a web-based application to support the algorithmic product development methodology and CAD modeling in CRC 666. It enables the development and adaptation of integral bifurcated parts based on the initial optimization data provided by XML-files. Besides the description of use cases and use scenarios, the concept is implemented as a web-based application for validation purposes. Based on the validation, advantages and limitations of the presented approach are discussed.
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Reports on the topic "Algorithmic exchange"

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Wilson, D., Steven Peckham, Max Krackow, Sora Haley, Sophia Bragdon, and Jay Clausen. Discriminating buried munitions based on physical models for their thermal response. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49749.

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Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, although increasingly popular, often require large training datasets including all environments to which they will be applied. Algorithms incorporating an understanding of the physical processes underlying the thermal signature potentially provide improved performance and mitigate the need for large training datasets. To that end, this report formulates a simplified model for the energy exchange near the ground and describes how it can be incorporated into maximum-likelihood ratio and Bayesian classifiers capable of distinguishing buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over a 24-hour period. These algorithms will be tested on experimental data in a future study.
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Hoffman, P. Algorithms for Internet Key Exchange version 1 (IKEv1). RFC Editor, 2005. http://dx.doi.org/10.17487/rfc4109.

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Allende López, Marcos, Diego López, Sergio Cerón, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies that rely on blockchain ledgers as well as the thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in their public and immutable ledgers. We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network. In our implementation, quantum entropy was provided via the IronBridge Platform from CQC and we used LACChain Besu as the blockchain network.
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Cypher, R. E., J. L. Sanz, and L. Snyder. Hypercube and Shuffle-Exchange Algorithms for Image Component Labeling. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada197574.

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Hoffman, P. Use of Hash Algorithms in Internet Key Exchange (IKE) and IPsec. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4894.

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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider only lagged predictors, it is possible to beat the Random Walk if we also consider the Brazilian Real futures as an additional predictor, for the frequency of one minute and up to two minutes ahead, confirming the importance of the Brazilian futures market in determining the spot exchange rate.
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Schiller, J. Cryptographic Algorithms for Use in the Internet Key Exchange Version 2 (IKEv2). RFC Editor, 2005. http://dx.doi.org/10.17487/rfc4307.

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Wouters, P., ed. Deprecation of the Internet Key Exchange Version 1 (IKEv1) Protocol and Obsoleted Algorithms. RFC Editor, 2023. http://dx.doi.org/10.17487/rfc9395.

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Smyslov, V. Using GOST Cryptographic Algorithms in the Internet Key Exchange Protocol Version 2 (IKEv2). RFC Editor, 2023. http://dx.doi.org/10.17487/rfc9385.

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Hoffman, P. The AES-XCBC-PRF-128 Algorithm for the Internet Key Exchange Protocol (IKE). RFC Editor, 2004. http://dx.doi.org/10.17487/rfc3664.

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