The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction
Abstract
The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important for the development of effective trading strategies in investments. The main objective of the research is to perform the comparative analyses of different nonparametric methods, that is, fuzzy artificial neural networks (fuzzyANN) and genetic algorithm artificial neural networks (GAANN) for predicting the movements of the stock market indexes. The survey is conducted on the BELEX15, SBITOP, BUX and CROBEX stock market indexes. Model estimates were carried out through the prediction error MAE, MAPE and RMSE. The research results point to the adequacy of the nonparametric methods applicat...ion in investments.
Keywords:
RMSE / Prediction / MAPE / MAE / Investments / GAANN / FuzzyANNSource:
IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy), 2015, 111-115Publisher:
- IEEE, NEW YORK
Collections
Institution/Community
Fakultet zaštite životne sredineTY - CONF AU - Ralević, Nebojša AU - Anđelić, Goran AU - Đaković, Vladimir AU - Glisović, Natasa S. PY - 2015 UR - https://redun.educons.edu.rs/handle/123456789/257 AB - The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important for the development of effective trading strategies in investments. The main objective of the research is to perform the comparative analyses of different nonparametric methods, that is, fuzzy artificial neural networks (fuzzyANN) and genetic algorithm artificial neural networks (GAANN) for predicting the movements of the stock market indexes. The survey is conducted on the BELEX15, SBITOP, BUX and CROBEX stock market indexes. Model estimates were carried out through the prediction error MAE, MAPE and RMSE. The research results point to the adequacy of the nonparametric methods application in investments. PB - IEEE, NEW YORK C3 - IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy) T1 - The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction EP - 115 SP - 111 UR - conv_874 ER -
@conference{ author = "Ralević, Nebojša and Anđelić, Goran and Đaković, Vladimir and Glisović, Natasa S.", year = "2015", abstract = "The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important for the development of effective trading strategies in investments. The main objective of the research is to perform the comparative analyses of different nonparametric methods, that is, fuzzy artificial neural networks (fuzzyANN) and genetic algorithm artificial neural networks (GAANN) for predicting the movements of the stock market indexes. The survey is conducted on the BELEX15, SBITOP, BUX and CROBEX stock market indexes. Model estimates were carried out through the prediction error MAE, MAPE and RMSE. The research results point to the adequacy of the nonparametric methods application in investments.", publisher = "IEEE, NEW YORK", journal = "IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy)", title = "The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction", pages = "115-111", url = "conv_874" }
Ralević, N., Anđelić, G., Đaković, V.,& Glisović, N. S.. (2015). The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction. in IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy) IEEE, NEW YORK., 111-115. conv_874
Ralević N, Anđelić G, Đaković V, Glisović NS. The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction. in IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy). 2015;:111-115. conv_874 .
Ralević, Nebojša, Anđelić, Goran, Đaković, Vladimir, Glisović, Natasa S., "The Comparative Analyses of the Nonparametric Methods for Investment Return Prediction" in IEEE 13th International Symposium on Intelligent Systems and Informatics (Sisy) (2015):111-115, conv_874 .