Predicción de precios de activos financieros con deep learning en un entorno de trading de Alta Frecuencia y una gestión activa de riesgos

Treballs Finals del Màster de Ciències Actuarials i Financeres, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2020-2021, Tutor: Luis Ortiz GraciaEl pronóstico de series financieras siempre ha sido un tópico de gran interés, ya que modelar los precios de los activos implica un gran reto. Esto se debe a que, en general, las series financieras tienen un comportamiento aleatorio y desde un punto de vista estrictamente econométrico, en muchas ocasiones, no podemos pronosticar los precios de los activos, pues es ruido blanco. De acuerdo con lo anterior, lo que busca este trabajo final de máster es realizar un montaje exploratorio de series financieras en periodos de muy corto tiempo y aplicar técnicas de deep learning como redes neuronales, para predecir el precio de los activos desde una perspectiva de clasificación y regresión y hacer el despliegue de esta estrategia de trading de alta frecuencia un entorno lo más real posible. De igual manera, se desea introducir el cálculo de medidas de riesgo como el valor en riesgo VaR y la pérdida esperada —ES— (expected shortfall) en cada uno de los instantes, para la gestión activa de riesgos de este portafolio. También se espera utilizarlo como una medida de riesgo para los stop loss. Abstract The forecast of financial time series has always been a topic of great interest since modeling asset prices implies a great challenge. This is because, in general, financial series have a random behavior and in an econometric point of view, in many occasions, we cannot foresee asset prices, as it is white noise. In accordance with the above, what this final master's thesis seeks is to carry out an exploratory study of financial series in very short periods of time and apply deep learning techniques such as neural networks, to predict the price of assets from a classification and regression perspective and make the deployment of this high frequency trading strategy as realistic as possible. Similarly, we want to introduce the calculation of risk measures such as Value at Risk (VaR) and the expected Shortfall —ES— at each moment, to actively manage the risks of this portfolio. It is also expected to be used as a risk measure for stop losses

First observation of the quantized exciton-polariton field and effect of interactions on a single polariton.

© 2018 The Authors. Published by Science. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1126/sciadv.aao6814Polaritons are quasi-particles that originate from the coupling of light with matter and that demonstrate quantum phenomena at the many-particle mesoscopic level, such as Bose-Einstein condensation and superfluidity. A highly sought and long-time missing feature of polaritons is a genuine quantum manifestation of their dynamics at the single-particle level. Although they are conceptually perceived as entangled states and theoretical proposals abound for an explicit manifestation of their single-particle properties, so far their behavior has remained fully accounted for by classical and mean-field theories. We report the first experimental demonstration of a genuinely quantum state of the microcavity polariton field, by swapping a photon for a polariton in a two-photon entangled state generated by parametric downconversion. When bringing this single-polariton quantum state in contact with a polariton condensate, we observe a disentangling with the external photon. This manifestation of a polariton quantum state involving a single quantum unlocks new possibilities for quantum information processing with interacting bosons

Espirales de reflexividad crítica y propositiva para escribir la educación media de Bogotá

399 p. Libro digita