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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Pastrana Olarte, Lina Rocio. | - |
dc.date.accessioned | 2022-10-05T14:51:58Z | - |
dc.date.available | 2018-03-15 | - |
dc.date.available | 2022-10-05T14:51:58Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Pastrana Olarte, L. R. (2017). Análisis de los métodos de predicción de índices bursátiles como herramienta decisoria de inversión [Trabajo de Grado Pregrado, Universidad de Pamplona]. Repositorio Hulago Universidad de Pamplona. http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3601 | es_CO |
dc.identifier.uri | http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3601 | - |
dc.description | Esta monografía presenta una revisión bibliográfica sobre los métodos que se han utilizado los últimos años para la predicción de los índices bursátiles. Los métodos que vamos a dar a conocer son aquellos que han ayudado por décadas a los inversionistas a tomar una mejor decisión a la hora de invertir. Empezamos desde los modelos lineales, por la fácil interpretación de sus elementos, tienen una considerable ventaja sobre otros, lo que ha hecho que sean utilizados en un sinnúmero de aplicaciones; una de ellas ha sido la predicción de series de tiempo financieras, de seguido se mostrara los modelos no lineales el cual utilizan modelos estadísticos no paramétricos y no lineales debido a que muchas relaciones importantes en el área de las finanzas tienen este tipo de relaciones. Las redes neuronales artificiales poseen la propiedad de capturar las características no lineales de los índices de bolsa y han demostrado que pueden ser entrenadas con una cantidad suficiente de información para identificar dichas relaciones no lineales entre los valores de entrada y salida. Por último, se dará a conocer los modelos híbridos que hace referencia a la combinación de dos o más elementos. En la predicción de la bolsa de valores muchos autores han realizado combinaciones de métodos o modelos de predicción buscando incorporar en el método híbrido las ventajas de cada uno de los modelos anteriores. | es_CO |
dc.description.abstract | La autora no proporciona la información sobre este ítem. | es_CO |
dc.format.extent | 30 | es_CO |
dc.format.mimetype | application/pdf | es_CO |
dc.language.iso | es | es_CO |
dc.publisher | Universidad de Pamplona – Facultad de Ingenierias y Arquitectura. | es_CO |
dc.subject | Indices Bursátiles. | es_CO |
dc.subject | Métodos o modelos de predicción. | es_CO |
dc.subject | Herramientas para la inversión. | es_CO |
dc.subject | Bolsa de valores. | es_CO |
dc.title | Análisis de los métodos de predicción de índices bursátiles como herramienta decisoria de inversión. | es_CO |
dc.type | http://purl.org/coar/resource_type/c_7a1f | es_CO |
dc.date.accepted | 2017-12-15 | - |
dc.relation.references | ABOUELDAHAB, T. Y FAKHRELDIN, M. (2011). prediction of stock market indices using hybrid ge- netic algorithm/ particle swarm optimization with perturbation term. international conference on swarm intelligence. cergy, france. | es_CO |
dc.relation.references | ALONSO, J. Y GARCIA, J. (2009). ¿qué tan buenos son los patrones del igbc para predecir suapplications , 177-83. | es_CO |
dc.relation.references | ASADI, S.; HADAVANDI, E.; MEHMANPAZIR, F. Y NAKHOSTIN, M. (2012). hybridization of evolutionary levenberg–marquardt neural networks and data pre-processing for stock market prediction. knowledge-based systems, 245-58. | es_CO |
dc.relation.references | BHARDWAJ, G. Y SWANSON, N. (2006). an empirical investigation of the usefulness of arfima models for predicting macroeconomic and financial time series. journal of econo- metrics, 539-78 | es_CO |
dc.relation.references | BOYACIOGLU, M. A. Y AVCI, D. (2010). an adaptive network-based fuzzy inference system (anfis) for the prediction of stock market return: the case of the istanbul stock exchange. expert systems with applications, 7908-12. | es_CO |
dc.relation.references | CHAIGUSIN, S.; CHIRATHAMJAREE, C. Y CLAYDEN, J. (2008). the use of neural networks in the pre- diction of the stock exchange of thailand (set) index. computational intelligence for modelling control & automation. ieee | es_CO |
dc.relation.references | CHANG, Y.; YEUNG, C. Y YIP, C. (2000). analysis of the influence of economic indicators on circumstances. journal of banking & finance, 1959-77 | es_CO |
dc.relation.references | CHEN, A.-S.LEUNG, M. Y DAOUK, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the taiwan stock index. computers & ope- rations research, 901-23. | es_CO |
dc.relation.references | CHEN, S.-S. (2009). predicting the bear stock market: macroeconomic variables as lea- ding indicators. journal of banking & finance, 211-23. | es_CO |
dc.relation.references | CHENG, C.-H.; CHEN , T.-L. Y WEI, L.-Y. (2010). a hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. information sciences, 1610-29. | es_CO |
dc.relation.references | CLEMENTS, M.; FRANSES, P. Y SWANSON, n. (2004). forecasting economic and financial time- comportamiento? universidad icesi, 13-36. | es_CO |
dc.relation.references | DAI, W.; WU, J.-Y. Y LU, C.-J. (2012). combining nonlinear independent component analysis and neural network for the prediction of asian stock market indexes. expert sys- tems with applications, 4444-52. | es_CO |
dc.relation.references | DOMÍNGUEZ GIJÓN, R. Y ZAMBRANO REYES, A. (2011). Pronóstico con modelos arima para los casos del índice de precios y cotizaciones (IPC) y la acción de américa móvil (am). memoria del xxi coloquio mexicano de economía matemática y econometría. | es_CO |
dc.relation.references | EL-HENAWY, I.; KAMAL, A.; ABDELBARY, H. Y ABAS, A. (2010). predicting stock index using neural | es_CO |
dc.relation.references | ENKE, D.; GRAUER, M. Y MEHDIYEV, N. (2011). stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks. procedia computer science, 201-06. | es_CO |
dc.relation.references | FRANSES, P. Y GHIJSELS, H. (1999). additive outliers, garch and forecasting volatility. inter- national journal of forecasting, 1-9. | es_CO |
dc.relation.references | GENÇTÜRK, M.; ÇELIK, I. Y BINICI, Ö. (2012). causal relations among stock returns and ma- croeconomic variables in a small and open economy. african journal of business management, 6177-82. | es_CO |
dc.relation.references | GJERDE, Ø. Y SÆTTEM, F. (1999). causal relations among stock returns and macroeconomic. | es_CO |
dc.relation.references | GURESEN, E.; KAYAKUTLU, G. Y DAIM, T. (2011). using artificial neural network models in stock market index prediction. expert systems with applications, 10389-97. | es_CO |
dc.relation.references | HADAVANDI, E.; SHAVANDI, H. Y GHANBARI, A. (2010). integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. knowledge-based systems,journal of international financial market, 61-74. | es_CO |
dc.relation.references | KIM, K.-J. Y HAN, I. (2000). genetic algorithms approach to feature discretization in arti- ficial neural networks for the prediction of stock price index. expert systems with applications, 125-32. | es_CO |
dc.relation.references | KOMO, D.; CHANG, C.-I. Y KO, H. (1994). neural network technology for stock market index prediction. international symposium on speech, image processing and neural net- works (pp. 543-546). hong kong: ieee. | es_CO |
dc.relation.references | KWON, C. Y SHIN, T. (1998). cointegration and causality between macroeconomic variables and stock market returns. global finance journal, 71-81. | es_CO |
dc.relation.references | LASFER, M.; MELNIK , A. Y THOMAS, D. (2003). short-term reaction of stock markets in stressful | es_CO |
dc.relation.references | LEE, K. Y JO, G. (1999). Expert system for predicting stock market timing using a candles- | es_CO |
dc.relation.references | LIU, H.-C. Y HUNG, J.-C. (2010). forecasting s&p-100 stock index volatility: the role of vo- latility asymmetry and distributional assumption in garch models. expert systems with applications, 4928-34. | es_CO |
dc.relation.references | LÓPEZ HERRERA, F. Y VÁSQUEZ TÉLLEZ, F. (2002). variables macroeconómicas y un modelo mul- tifactorial para la bolsa mexicana de valores: análisis empírico sobre una muestra de activos. academia. revista latinoamericana de investigacion , 5-28. | es_CO |
dc.relation.references | LU, C.-J.; CHANG, C.-H.; CHEN, C.-Y.; CHIU, C.-C. Y LEE, T.-S. (2009). stock index prediction: a comparison of mars, bpn and svr in an emerging market. industrial engineering and engineering management. ieee. | es_CO |
dc.relation.references | MAJHI, R.; PANDA, G.; MAJHI, B. Y SAHOO, G. (2009). efficient prediction of stock market in- dices using adaptive bacterial foraging optimization (abfo) and bfo based techni- ques. expert systems with applications, elsevier, 10097-104. | es_CO |
dc.relation.references | MAYSAMI, HOWE Y HAMZAH (2004). relationship between macroeconomic variables and stock market indices: cointegration evidence from stock exchange of singapore’s all-s sector indices. journal pengurusan, 47-77. | es_CO |
dc.relation.references | PAI, P.-F. Y LIN, C.-S. (2005). a hybrid arima and support vector machines model in stock price forecasting. omega, elsevier, 497-505. | es_CO |
dc.relation.references | PARISI, A.; PARISI, F. Y DÍAZ, D. (2006). modelos de algoritmo genético y redes neuronales en la predicción de índices bursátiles asiáticos. cuadernos de economía, 251-84. | es_CO |
dc.relation.references | PIERDZIOCH, C.; DOPKE, J. Y HARTMANN, D. (2008). “forecasting stock market volatility with macroeconomic variables in real time”. journal of economics and business, 256-76. | es_CO |
dc.relation.references | REDDY, B. (2010). prediction of stock market indices – using sas. ieee. series with non-linear models. international journal of forecasting, 169-83. stock prices using multiple regression. tick chart. expert systems with applications, 357-64. tuations and market volatility. journal of banking & finance, 2026-35. | es_CO |
dc.relation.references | ZEMKE, S. (1999). nonlinearindexprediction. physica a: statistical mechanics and its roh, t. (2007). forecasting the volatility of stock price index. expert systems with applications, 916-22. | es_CO |
dc.relation.references | SHEN, W.; GUO, X.; WUB, C. Y WU, D. (2011). forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. knowledge- based systems, 378-85. | es_CO |
dc.relation.references | TORO OCAMPO, E. M.; MOLINA CABRERA, A. Y GARCÉS RUIZ, A. (2006). pronóstico de bolsa de valores empleando técnicas inteligentes. tecnura. | es_CO |
dc.relation.references | WANG , J.-Z.; WANG, J.-J.; ZHANG, Z.-G. Y GUO, S.-P. (2011). forecasting stock indices with back propagation neural network. expert systems with applications, 14346-55. | es_CO |
dc.relation.references | WANG, J.-J.; WANG, J.-Z.; ZHANG, Z.-G. Y GUO, S.-P. (2012). stock index forecasting based on a hybrid model. omega, 758-66. | es_CO |
dc.relation.references | WANG, Y.-F.; CHENG, S. Y HSU, M.-H. (2010). incorporating the markov chain concept into fuzzy stochastic prediction of stock indexes. applied soft computing, 613-17. | es_CO |
dc.relation.references | YU, T.-K. Y HUARNG, K.-H. (2010). a neural network-based fuzzy time series model to improve forecasting. expert systems with applications, 3366-72. | es_CO |
dc.relation.references | YUDONG, Z. Y LENAN, W. (2009). stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. expert systems with applications, 8849-54. | es_CO |
dc.relation.references | ZHU, X.; XU, L.; WANG, H. Y LI, H. (2008). predicting stock index increments by neural net- works: the role of trading volume under different horizons. expert systems with applications, 3043-54. | es_CO |
dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | es_CO |
dc.type.coarversion | http://purl.org/coar/resource_type/c_2df8fbb1 | es_CO |
Aparece en las colecciones: | Ingeniería Industrial |
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Pastrana_2017_TG.pdf | Pastrana_2017_TG | 624,19 kB | Adobe PDF | Visualizar/Abrir |
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