6 research outputs found

    Forecasting Stock Exchange Data using Group Method of Data Handling Neural Network Approach

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    The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem-solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. This study aimed to show it is efficient to use the group method of data handling (GMDH)-type neural networks and their application for the classification of numerical results. Such modeling serves to display the precision of GMDH-type neural networks. Following the US withdrawal from the Joint Comprehensive Plan of Action in April 2018, the behavior of the stock exchange data stream and commend algorithms has not been able to predict correctly and fit in the network satisfactorily. This paper demonstrated that Group Method Data Handling is most likely to improve inductive self-organizing approaches for addressing realistic severe problems such as the Iranian financial market crisis. A new trajectory would be used to verify the consistency of the obtained equations hence the models' validity

    Funcionamiento del trading algor铆tmico en los mercados de capitales

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    Trabajo final (Licenciatura en Administraci贸n con orientaci贸n en Finanzas)Prop贸sito: este trabajo tiene como finalidad exponer informaci贸n acerca del trading algor铆tmico y su relaci贸n con el mercado de capitales, el an谩lisis t茅cnico y fundamental, los activos financieros y sus derivados para todo aquel interesado en interiorizarse en el mundo de las finanzas. Metodolog铆a: se realiz贸 una revisi贸n sistem谩tica de literatura, relevando 572 art铆culos acerca del trading algor铆tmico, publicados en el periodo 2015-2022. En la b煤squeda se aplicaron criterios de exclusi贸n, quedando un total de 29 art铆culos. Su an谩lisis pertinente permiti贸 contestar las preguntas de investigaci贸n y desarrollar la tem谩tica elegida. Adem谩s, se efectuaron entrevistas semi-estructuradas a personas trabajando en la operatoria de trading. Conclusiones: El trading algor铆tmico posee ventajas excepcionales sobre el trading discrecional. Entre ellas se destaca la capacidad de procesamiento superior que tiene una computadora que simplifica toda operaci贸n y reduce los tiempos empleados y por otro lado, elimina el lado emocional de la toma de decisiones del proceso de inversi贸n. Limitaciones: En el protocolo de investigaci贸n se estableci贸 la condici贸n de seleccionar solo art铆culos de libre acceso y aceptar 煤nicamente los art铆culos que hayan sido redactados en ingl茅s o el espa帽ol. Los idiomas de los textos que fueron dejados de lado son franc茅s, alem谩n, portugu茅s y ucraniano. Originalidad-Valor: El valor del trabajo radica en que se aborda una tem谩tica novedosa en el campo de las finanzas por medio de dos metodolog铆as que aportan por un lado informaci贸n de calidad y con respaldo cient铆fico y por otro lado la experiencia y conocimientos de los profesionales entrevistados que actualmente trabajan con esta herramienta.Fil: Castro, Francisco Javier. Universidad Nacional de C贸rdoba. Facultad de Ciencias Econ贸micas; Argentina.Fil: Gervasoni, Luc铆a Florencia. Universidad Nacional de C贸rdoba. Facultad de Ciencias Econ贸micas; Argentina.Fil: Giannelli, Agostina Bel茅n. Universidad Nacional de C贸rdoba. Facultad de Ciencias Econ贸micas; Argentina.Fil: Vogel Dotta, Mar铆a Sol. Universidad Nacional de C贸rdoba. Facultad de Ciencias Econ贸micas; Argentina

    Sports betting: a new asset class to bet on

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    This dissertation has the aim to present a complete overview of the current features and activities related to the sports betting industry and to explain the reasons why it can be considered a new asset class to invest on. The first chapter explains the main features of both fixed-odds and exchange betting market, the second describes the activity of sport trading, while the third presents a deep investigation concerning the market efficiency. Chapter 4 shows the arbitrage opportunities implementable in this market, that come from the efficiency study of the previous chapter. Before the conclusion, a personal study about the value betting arbitrage opportunity is presented, confirming that abnormal returns are achievable
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