Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.

Trading Algorítmico y metodos de Backtesting

Autores:
Suárez Corcho, Juan David
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
spa
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/54566
Acceso en línea:
http://hdl.handle.net/1992/54566
Palabra clave:
Inteligencia artificial, RNN, USDCOP, Dólar, AI, Direct Reinforcement Learning, Trading
Aprendizaje por refuerzo (Aprendizaje automático)
Cambio exterior
Fondos mutuos
Economía
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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repository_id_str
dc.title.none.fl_str_mv Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
title Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
spellingShingle Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
Inteligencia artificial, RNN, USDCOP, Dólar, AI, Direct Reinforcement Learning, Trading
Aprendizaje por refuerzo (Aprendizaje automático)
Cambio exterior
Fondos mutuos
Economía
title_short Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
title_full Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
title_fullStr Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
title_full_unstemmed Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
title_sort Inversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.
dc.creator.fl_str_mv Suárez Corcho, Juan David
dc.contributor.advisor.none.fl_str_mv Jara Pinzón, Diego
dc.contributor.author.none.fl_str_mv Suárez Corcho, Juan David
dc.contributor.jury.none.fl_str_mv Sarmiento Barbieri, Ignacio
dc.subject.keyword.none.fl_str_mv Inteligencia artificial, RNN, USDCOP, Dólar, AI, Direct Reinforcement Learning, Trading
topic Inteligencia artificial, RNN, USDCOP, Dólar, AI, Direct Reinforcement Learning, Trading
Aprendizaje por refuerzo (Aprendizaje automático)
Cambio exterior
Fondos mutuos
Economía
dc.subject.armarc.none.fl_str_mv Aprendizaje por refuerzo (Aprendizaje automático)
Cambio exterior
Fondos mutuos
dc.subject.themes.es_CO.fl_str_mv Economía
description Trading Algorítmico y metodos de Backtesting
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-10-20
dc.date.accessioned.none.fl_str_mv 2022-02-07T20:31:20Z
dc.date.available.none.fl_str_mv 2022-02-07T20:31:20Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
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dc.language.iso.es_CO.fl_str_mv spa
language spa
dc.relation.references.es_CO.fl_str_mv Campbell, J. Lo, A. MacKinlay, G. (1997). The Econometrics of Financial Markets. Princeton University Press. New Jersey
Almahdi, Saud & Yang, Steve. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications.
BVC. (15 de Octubre de 2020). Bolsa de valors de Colombia. Obtenido de https://www.bvc.com.co/pps/tibco/portalbvc/Home/Mercados/descripciongeneral/divisas?action=dummy
Bertsimas, Dimitris and Lo, Andrew, (1998), Optimal control of execution costs, Journal of Financial Markets, 1, issue 1, p. 1-50,
Business Insider. (8 de Marzo de 2018). Obtenido de https://www.businessinsider.com/jpmorgan-takes-ai-use-to-the-next-level-2017-8
Campbell, John Y. and Luis M. Vicera, (1999), Consumption and portfolio decisions when expected returns are time varying¿ Quarterly Journal of Economics, forthcoming.
Cochrane, J. H. (1991). Portfolio advice for a multifactor world, economic perspectives XXIII (3) Third quarter 1999 (pp. 59¿78). Chicago: Federal Reserve Bank of Chicago.
Cowles 3rd, A. (1933). Can stock market forecasters forecast?. Econometrica: Journal of the Econometric Society, 309-324.
Cootner, P. H. (1962). Stock prices: Ramdom vs. systematic changes. Industrial Management Review (pre-1986), 3(2), 24.
Cootner, P. H. (1964). The random character of stock market prices.
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Transactions on Neural Networks and Learning Systems, 28, 653-664.
Fallahpour, S., Hakimian, H., Taheri, K. et al. Soft Comput (2016) 20: 5051. https://doi.org/10.1007/s00500-016-2298-4
Fama, E. F. (1963). Mandelbrot and the stable Paretian hypothesis. The journal of business, 36(4), 420-429.
Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105.
Fama, E. F., & Blume, M. E. (1966). Filter rules and stock-market trading. The Journal of Business, 39(1), 226-241.
Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969). The Adjustment of Stock Prices to New Information. International Economic Review, 10(1), 1-21. doi:10.2307/2525569
Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417. doi:10.2307/2325486
Financial Times. (8 de Marzo de 2018). Obtenido de https://www.ft.com/content/ff7528bc-ec16-11e7-8713-513b1d7ca85a
Fischer, Thomas G. (2018) : Reinforcement learning in financial markets a survey, FAU Discussion Papers in Economics, No. 12/2018, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Erlangen
Gold, C. (2003). FX trading via recurrent reinforcement learning. Proceedings of IEEE International Conference on Computational Intelligence in Financial Engineering. 363 - 370.
Hens, Thorsten and Wöhrmann, Peter, (2007), Strategic asset allocation and market timing: a reinforcement learning approach, Computational Economics, 29, issue 3, p. 369-381
James, I. S. (2012). Handbook of Exchange Rates. Wiley & Sons, Inc.
Jensen, M. (1978). Some anomalous evidence regarding market efficiency.
Malkiel, B. G. (2003). The ef¿cient market hypothesis and its critics. Journal of Economic Perspectives, 17, 59¿82.
Miller, M. (1999) The Journal of Portfolio Management Jul 1999, 25 (4) 95-101; DOI: 10.3905/jpm.1999.319752
Moody, J., Saffell, M. (2001), Learning to Trade via Direct Reinforcement, IEEE Transactions on Neural Networks, Vol.12, July, 2001
Lucas Jr, R. E. (1978). Asset prices in an exchange economy. Econometrica: Journal of the Econometric Society, 1429-1445.
Ojeda, C., Castaño, E. (2014). Prueba de Eficiencia Débil en el Mercado Accionario Colombiano. Universidad de Medellín. Semestre Económico. 17. pp 13-42.
Pendharkar, P. C., & Cusatis, P. J. (2018). Trading financial indices with reinforcement learning agents. Expert Systems With Applications, 103, 1-13. https://doi.org/10.1016/j.eswa.2018.02.032
Prado, M. L. (2018). Advances in Financial Machine Learning. New Yersey : John Wiley & Sons.
Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial management review, 6(2).
Shleifer, A. (2000). Inef¿cient markets: An introduction to behavioral ¿nance. Oxford: Oxford University Press.
Translateur, E. (2017). Predicción del Mercado de TES en el Corto Plazo. Facultad de Economía, Universidad de los Andes. Tesis PEG.
Trujillo (2015). Trading algoríttmico: un análisis para el mercado financiero colombiano. Tesis de Pregrado Universidad de los Andes.
William A. Branch, George W. Evans; Asset Return Dynamics and Learning, The Review of Financial Studies, Volume 23, Issue 4, 1 April 2010, Pages 1651¿1680, https://doi.org/10.1093/rfs/hhp112
Y. Deng, F. Bao, Y. Kong, Z. Ren, Q. Dai, Deep Direct Reinforcement Learning for Financial Signal Representation and Trading, IEEE Transactions on Neural Networks and Learning Systems, April, 2015
Moody, J.,Wu, L., Liao, Y., Saffell, M. (1998), Performance Functions and Reinforcement Learning for Trading Systems and Portfolios, Journal of Forecasting, vol. 17, 1998
Lu, D. (2017). Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks. arXiv preprint arXiv:1707.07338, 2017.
Raffaella Giacomini, & White, H. (2006). Tests of Conditional Predictive Ability. Econometrica, 74(6), 1545-1578. Retrieved from http://www.jstor.org/stable/41230834
Campbell,J.Y.,&Shiller,R.J.(1988).The dividend price ratio and expectations of future dividens and discount factors. Review of Financial studies, 1, 195-228
Campbell, J. Y., (2000). Asset pricing at the millenium. Journal of Finance, 55(4), 1515-1567.
Fama, E. (1998). Market eficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49, 283-306.
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dc.publisher.faculty.es_CO.fl_str_mv Facultad de Economía
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spelling Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jara Pinzón, Diegof7504ea1-04d4-4b60-a2c9-c4dc0eb1c475600Suárez Corcho, Juan David53ad354a-c52c-4e9a-ae21-6fd3876771a1600Sarmiento Barbieri, Ignacio2022-02-07T20:31:20Z2022-02-07T20:31:20Z2020-10-20http://hdl.handle.net/1992/54566instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Trading Algorítmico y metodos de BacktestingGracias a los avances en la capacidad computacional y el desarrollo de la Inteligencia Artificial, se han creado agentes autónomos que logran superar a los humanos en la forma en que ejecutan las tareas y toman decisiones. Reinforcement Learning es un claro ejemplo de esto. Este tipo de metodología ha logrado mostrar resultados en cuanto a la ejecución, inversión y administración de fondos en finanzas. En este escrito se plantea rechazar la hipótesis de caminata aleatoria en el mercado de tasa de cambio peso colombiano/dólar (USD/COP) en el corto plazo, explotando la capacidad predictiva de RL y realizando un análisis de diferentes medidas de rendimiento del agente modelado.Magíster en EconomíaMaestría40spaUniversidad de los AndesMaestría en EconomíaFacultad de EconomíaInversión y rebalanceo de corto plazo en el USDCOP, un acercamiento relativo y de aprendizaje por refuerzo.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMInteligencia artificial, RNN, USDCOP, Dólar, AI, Direct Reinforcement Learning, TradingAprendizaje por refuerzo (Aprendizaje automático)Cambio exteriorFondos mutuosEconomíaCampbell, J. Lo, A. MacKinlay, G. (1997). The Econometrics of Financial Markets. Princeton University Press. New JerseyAlmahdi, Saud & Yang, Steve. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications.BVC. (15 de Octubre de 2020). Bolsa de valors de Colombia. Obtenido de https://www.bvc.com.co/pps/tibco/portalbvc/Home/Mercados/descripciongeneral/divisas?action=dummyBertsimas, Dimitris and Lo, Andrew, (1998), Optimal control of execution costs, Journal of Financial Markets, 1, issue 1, p. 1-50,Business Insider. (8 de Marzo de 2018). Obtenido de https://www.businessinsider.com/jpmorgan-takes-ai-use-to-the-next-level-2017-8Campbell, John Y. and Luis M. Vicera, (1999), Consumption and portfolio decisions when expected returns are time varying¿ Quarterly Journal of Economics, forthcoming.Cochrane, J. H. (1991). Portfolio advice for a multifactor world, economic perspectives XXIII (3) Third quarter 1999 (pp. 59¿78). Chicago: Federal Reserve Bank of Chicago.Cowles 3rd, A. (1933). Can stock market forecasters forecast?. Econometrica: Journal of the Econometric Society, 309-324.Cootner, P. H. (1962). Stock prices: Ramdom vs. systematic changes. Industrial Management Review (pre-1986), 3(2), 24.Cootner, P. H. (1964). The random character of stock market prices.Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Transactions on Neural Networks and Learning Systems, 28, 653-664.Fallahpour, S., Hakimian, H., Taheri, K. et al. Soft Comput (2016) 20: 5051. https://doi.org/10.1007/s00500-016-2298-4Fama, E. F. (1963). Mandelbrot and the stable Paretian hypothesis. The journal of business, 36(4), 420-429.Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105.Fama, E. F., & Blume, M. E. (1966). Filter rules and stock-market trading. The Journal of Business, 39(1), 226-241.Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969). The Adjustment of Stock Prices to New Information. International Economic Review, 10(1), 1-21. doi:10.2307/2525569Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417. doi:10.2307/2325486Financial Times. (8 de Marzo de 2018). Obtenido de https://www.ft.com/content/ff7528bc-ec16-11e7-8713-513b1d7ca85aFischer, Thomas G. (2018) : Reinforcement learning in financial markets a survey, FAU Discussion Papers in Economics, No. 12/2018, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, ErlangenGold, C. (2003). FX trading via recurrent reinforcement learning. Proceedings of IEEE International Conference on Computational Intelligence in Financial Engineering. 363 - 370.Hens, Thorsten and Wöhrmann, Peter, (2007), Strategic asset allocation and market timing: a reinforcement learning approach, Computational Economics, 29, issue 3, p. 369-381James, I. S. (2012). Handbook of Exchange Rates. Wiley & Sons, Inc.Jensen, M. (1978). Some anomalous evidence regarding market efficiency.Malkiel, B. G. (2003). The ef¿cient market hypothesis and its critics. Journal of Economic Perspectives, 17, 59¿82.Miller, M. (1999) The Journal of Portfolio Management Jul 1999, 25 (4) 95-101; DOI: 10.3905/jpm.1999.319752Moody, J., Saffell, M. (2001), Learning to Trade via Direct Reinforcement, IEEE Transactions on Neural Networks, Vol.12, July, 2001Lucas Jr, R. E. (1978). Asset prices in an exchange economy. Econometrica: Journal of the Econometric Society, 1429-1445.Ojeda, C., Castaño, E. (2014). Prueba de Eficiencia Débil en el Mercado Accionario Colombiano. Universidad de Medellín. Semestre Económico. 17. pp 13-42.Pendharkar, P. C., & Cusatis, P. J. (2018). Trading financial indices with reinforcement learning agents. Expert Systems With Applications, 103, 1-13. https://doi.org/10.1016/j.eswa.2018.02.032Prado, M. L. (2018). Advances in Financial Machine Learning. New Yersey : John Wiley & Sons.Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial management review, 6(2).Shleifer, A. (2000). Inef¿cient markets: An introduction to behavioral ¿nance. Oxford: Oxford University Press.Translateur, E. (2017). Predicción del Mercado de TES en el Corto Plazo. Facultad de Economía, Universidad de los Andes. Tesis PEG.Trujillo (2015). Trading algoríttmico: un análisis para el mercado financiero colombiano. Tesis de Pregrado Universidad de los Andes.William A. Branch, George W. Evans; Asset Return Dynamics and Learning, The Review of Financial Studies, Volume 23, Issue 4, 1 April 2010, Pages 1651¿1680, https://doi.org/10.1093/rfs/hhp112Y. Deng, F. Bao, Y. Kong, Z. Ren, Q. Dai, Deep Direct Reinforcement Learning for Financial Signal Representation and Trading, IEEE Transactions on Neural Networks and Learning Systems, April, 2015Moody, J.,Wu, L., Liao, Y., Saffell, M. (1998), Performance Functions and Reinforcement Learning for Trading Systems and Portfolios, Journal of Forecasting, vol. 17, 1998Lu, D. (2017). Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks. arXiv preprint arXiv:1707.07338, 2017.Raffaella Giacomini, & White, H. (2006). Tests of Conditional Predictive Ability. Econometrica, 74(6), 1545-1578. Retrieved from http://www.jstor.org/stable/41230834Campbell,J.Y.,&Shiller,R.J.(1988).The dividend price ratio and expectations of future dividens and discount factors. Review of Financial studies, 1, 195-228Campbell, J. Y., (2000). Asset pricing at the millenium. Journal of Finance, 55(4), 1515-1567.Fama, E. (1998). Market eficiency, long-term returns, and behavioral finance. 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